Associate Professor & Head, Department of Computer Science & Engineering, CSPIT, Charotar University of Science & Technology (CHARUSAT), Gujarat, India
Thursday August 27, 2026 9:28am - 9:30am IST Virtual Room DGOA, India
Authors - Nita Dakhare, Shailesh Gahane Abstract - Kidney disease poses a significant global health challenge, necessitating innovative approaches for early detection and intervention. This study delves into the realm of predictive analytics through the utilization of machine learning algorithms to enhance kidney disease risk assessment. The research employs a comprehensive dataset comprising clinical and demographic variables, fostering a robust analysis of potential risk factors. The initial phase involves a systematic exploration of the dataset, employing statistical methods to identify correlations and patterns within the data. Subsequently, a comparative analysis of various machine learning algorithms, including but not limited to support vector machines, decision trees, and ensemble methods, is undertaken. Development of hybrid algorithm for kidney disease prediction using machine learning involves combining different techniques to improve accuracy, robustness or efficiency in predicting this condition. This evaluation aims to pinpoint the most effective model in terms of accuracy, sensitivity, and specificity in predicting kidney disease onset. The model development phase focuses on the implementation of the chosen machine learning model, incorporating features that contribute significantly to predictive accuracy. The model undergoes rigorous validation using distinct datasets to ensure its generalizability and reliability. Additionally, interpretability and transparency are prioritized to enhance the model's clinical applicability and acceptance. The study's findings provide valuable insights into the identification and understanding of key predictors of kidney disease, offering a potential tool for early diagnosis and intervention. The integration of machine learning in kidney disease prediction not only aids healthcare professionals in risk stratification but also contributes to the broader landscape of predictive analytics in preventive healthcare. The implications of this research extend to improving patient outcomes, reducing healthcare costs, and fostering a proactive approach to managing kidney disease on a global scale.
Authors - Vedant Vaidya, Shailesh Gahane, Prachi Mandade, Deepak S. Sharma, Pankajkumar Anawade Abstract - Pharmaceutical storage management is an important aspect of the health care system It makes sure medicines are on hand and stops fake drugs from spreading, while boosting overall operations. Old ways of tracking stock, like counting by hand or using barcodes, face many issues. These methods tend to be slow, prone to mistakes, and need lots of manual work. This leads to high running costs and inefficiencies. This study looks at how NFC card tech might solve these problems in drug inventory control. We focus on key areas such as accelerating inventory checks, reducing expenses, preventing counterfeit medications, protecting patients, and streamlining the supply chain. NFC cards help stop fake drugs by giving each item a secure tamper-proof ID. NFC cards aid in the fight against fake medicine. This guarantees that genuine medications pass through the supply chain. Additionally, patients are safer when utilizing NFC cards. It reduces drug mix-ups, provides reliable data on drug usage, and enables accurate prescription tracking. We also demonstrate how NFC technology improves supply chain efficiency. It streamlines the entire process of sending medications where they need to go by enabling real-time updates and reducing stock management delays. Besides, NFC calling card boost patient safety. They allow exact prescription monitoring thin down on medicinal drug mistakes, and propose trustworthy datum on drug usage. We too highlight how NFC tech further supply chemical chain productiveness. It activate live updates and cutting off delays in stock management making the whole drug distribution appendage smoother. Our research wraps up by showing that NFC placard tech offers a growth-friendly, budget-friendly fix for the crowing topic in drug inventory control. It impart major gains in precision, f number, costs, and safety. This spend a penny it a hopeful answer to bring drug supply Chain up to date.
Authors - Vanshika Landge, Shailesh Gahane, Deepak S. Sharma, Pankajkumar Anawade Abstract - The relocation to a new city poses significant challenges to the students, especially with the search for safe and relatively affordable accommodation, food service, and transportation. Stress associated with academic demands tends to be amplified in light of these difficulties, indicating the need for a fully integrated solution that would correspond to the needs of a student. This paper explores a web application aimed to help students during their relocation period to new urban environments. Key services include housing listings, food delivery options, community engagement tools, and transportation services while incorporating budgeting features that enable financial responsibility. The application is user-centric and makes relocation easier for students and fosters a sense of community among them. The research indicates that there are critical gaps in the literature. It shows that current digital solutions miss the specific needs of students, especially with regard to affordability, safety, and ease of access to essential services. The methodology includes requirement analysis, exhaustive literature reviews, development in iterations, and rigid testing to ensure that this application will meet the expectations of the users. Utilizing contemporary web technologies and real-time data integration, this project addresses both the logistical problems and emotional support to facilitate students in informed decision making. Ultimately, this research shall contribute to a better understanding of the student experience while alleviating the stress involved in moving to unknown environments and enables the students to focus on their academic pursuit while becoming an integral part of the new community. It is, therefore, an important step toward a comprehensive solution to the multifaceted problems students face from relocation.
Authors - Reena Bhagat, Smita Urkunde, Payal Khode, Shailesh Gahane Abstract - The dynamic interplay between Human Resource (HR) practices and business analytics has emerged as a pivotal factor in driving organizational performance. This research investigates the integration of HR practices with business analytics to enhance operational efficiency and strategic decision-making at Varron Autokast LTD., Nagpur. It also explores the impact of HR Analytics and Performance Management Systems on organizational outcomes at Wipro Limited, Pune. Employing a mixed-methods approach, the study delves into how HR analytics tools and data-driven strategies optimize talent management, improve workforce productivity, and align HR objectives with organizational goals. The research emphasizes the role of advanced analytics in identifying key performance indicators, fostering employee engagement, and enabling predictive insights for proactive HR interventions. Key findings aim to provide actionable frameworks for leveraging HR analytics in diverse corporate contexts, ensuring scalable, adaptive, and measurable improvements in HR processes. This study contributes to the broader understanding of HR analytics as a transformative tool for achieving sustainable competitive advantage in a rapidly evolving business landscape.
Authors - Shrinivas Patwardhan, Shailesh Gahane, Pankajkumar Anawade, Vanshika Landge, Prachi Mandade Abstract - The provision of essential medicines in rural health facilities is a complex issue, primarily influenced by frequent stock repletion, drug wastage, and poor record-keeping. Most of these problems are as a result of limited resources, old organizational systems, and poor infrastructure that characterizes most rural settings. This study evaluates the possible applicability of advanced technologies, like Radio Frequency Identification (RFID), the Internet of Things (IoT), and cloud computing, in meeting the above-mentioned requirements and to better inventory management of rural health facilities. It shall be considered with a mixed-methods approach based on survey and interview methodologies and case studies as well as cost-benefit analysis for testing feasibility, benefits, and drawback regarding the introduction of these technologies into low resource environments. The findings of this study indicate that the implementation of RFID, IoT, and cloud computing technologies possesses the capacity to significantly reduce drug wastage, enhance operational efficiency, and increase inventory accuracy. The primary obstacles to the adoption of these technologies include insufficient internet connectivity, constrained financial resources, and the necessity for specialized training. This study supports stepwise implementation, with key attention to pilot testing, financial assessment, and scalable approaches to these technological innovations. Finally, the investigation determines that, despite the considerable promise these technologies hold in transforming rural healthcare systems, there exists an urgent requirement to address technical, logistical, and financial obstacles to render them feasible and appropriate for application in resource-constrained environments.
Authors - Ritika Tiwari, Shailesh Gahanae Abstract - This research work suggested brain tumor detection and the use of a combination of deep learning and reinforcement studying techniques applied to magnetic resonance imaging (MRI) records. The mixing of deep mastering models, specifically convolutional neural networks (CNN) and reinforcement gaining knowledge of algorithms, aims to enhance the accuracy and performance of brain tumor detection structures. A comprehensive assessment of machine overall performance is carried out using standards such as sensitivity, specificity, accuracy, and computational performance. Early treatment for mind tumors is critical. The only way to identify a tumor is by biopsy, which requires mind surgical treatment. Medical doctors can locate and classify brain tumors with the help of equipment primarily based on Computational algorithms. To help medical doctors perceive early Tumor with high ac-curacy, we are able to suggest deep gaining knowledge of and diverse system studying strategies using magnetic resonance imaging mind and enable the prognosis of numerous varieties of tumors as well as healthy tumors. Massive image files need to be processed and this may be a completely time-eating undertaking. due to the fact brain tumors and normal tissues have similar findings, it is able to be tough to differentiate nearby tumors. Consequently, there's a want for a rather sensitive automatic tumor detection technique. Experimental effects demonstrate the effectiveness of our technique, with vast improvements in accuracy, sensitivity, and specificity in comparison to conventional strategies. Moreover, we discuss the consequences of our findings for scientific practice, highlighting the capacity of deep getting to know-based strategies to beautify the performance and reliability of brain tumor detection. Standard, this research contributes to advancing the sector of clinical photo evaluation and underscores the importance of leveraging deep mastering and MRI within the combat in opposition to mind tumors.
Authors - Shrinivas Patwardhan, Shailesh Gahane, Pankajkumar Anawade, Prachi Mandade, Vedant Vaidya Abstract - Pharmaceutical inventory management in health care settings is important to ensure accessibility, access and ability to essential medicines. However, the challenges in rural areas include limited infrastructure, insufficient storage systems, disabled tracking methods, poor visibility in the supply chain and lack of monitoring of real-time portfolio. These factors cause frequent warehouses, drugs and disruption in the patient's care, affecting health results in signed areas. This paper examines the current status of pharmaceutical inventory management in rural health systems, including both manual and automatic systems to determine the efficiency, efficiency and scalability of these approaches. It then examines the effect of poor inventory management on medicines, patient safety and general lack of health care. In addition, the study in existing research and training, especially in the environment with low resources, where cost effective, technology -driven solutions are necessary, intervals within.
Authors - Reena Bhagat, Smita Urkunde, Payal Khode, Shailesh Gahane Abstract - Data driven strategy is already on the rise for better performance of Human Resource in its decision making, thus helping to attract business in the current global market. The escalating growth, hands in glove with human resources, is the transformation of HR analytics within performance management systems, motivating organizations to consolidate the objectives and performance of individuals. The current research is about the integration and impacts of HR Analytics made in Wipro Limited, Pune and aims to identify the role of HR Analytics toward improvement in the performance of the workforce, aligning their goals, and mean to enhance the overall productivity of the organization. This research would cover both the methods: quantitative and qualitative analyses to establish the use and effectiveness of HR Analytics when it introduces quantitative data analysis along with the instrument with qualitative data. Some commonly faced challenges where HR analytics could be used are: silos in data, lack of technological infrastructure, employee resistance, and so on. This research will also embody certain benefits of the HR analytics among some of which: it helps in decision-making, talent management, and allocation of resources in a better manner. It further gives strategic recommendations to organizations for optimum integration of HR analytics and brings out actionable insights to better guarantee performance and subsequent organizational growth. The new findings contribute to HR Analytics and HRM Literature Growth, which can serve as praxis toward the solution for HR professionals and organizational leaders or policymakers.
Authors - Lal Mohan kumar, Shailesh Gahane, Chandan Kumar, Deepak S. Sharma, Pankajkumar Anawade Abstract - This paper does go into the roles cloud computing has in changing the face of online education, but this time, it focuses on its advantages and the flip-side of it all. Advantages reaped from using cloud computing in the education sector include resource access to scalable, flexible, and accessible learning, where students are able to learn through various personalized learning experiences with collaborative learning environments from which the students and their educators interact and share insights in real time. Most importantly, this paper discovers that cloud-based platforms offer many benefits, such as improving access to educational resources and data analytics to achieve personalized learning support for diversity in learning styles. However, despite the widespread benefits, this study also considers inevitable critical challenges that may limit equal access to education, such as creating considerable difficulties related to data privacy issues, digital literacy, and the digital divide. Therefore, research needs to be con-ducted to apply cloud computing solutions in education to improve understanding of its benefits and limitations. Such recognition would lead to better incorporation of cloud computing solutions to facilitate learner engagement, improve educational outcomes, and support inclusive educational ecosystems in those institutions. Thus, this paper suggests more empirical research be conducted to understand the long-term impact of cloud computing on student performance, engagement, and retention in different educational contexts.
Authors - Vanshika Landge, Shailesh Gahane, Deepak S. Sharma, Pankajkumar Anawade Abstract - Public transportation systems face rising pressure to provide services that are secure, efficient, and accessible to users, while still having a major segment dependent on outdated infrastructure which cannot fulfill the demands of modern-day commuters. Some key challenges include inefficient fare-collection mechanisms, rigid travel routes and poor provision of real-time information. This paper covers the adoption of Radio Frequency Identification (RFID) and Near Field Communication (NFC) technologies within the public transportation system as one of the comprehensive approaches. The proposed solution integrates safe and contactless fare collection along with dynamic travel flexibility through real-time GPS updates with help of smart cards as well as mobile applications. Its multi-phase research approach toward requirement analysis, prototype building, pilot testing, and scaling up ensures the robustness as well as practicality in the system. Modular architectures for scalability, safe use of advanced encryption, as well as intuitive interfaces towards users are integrated into this proposed solution. Pilot implementations show considerable improvements in operational efficiency, transaction accuracy, passenger satisfaction, and system reliability. The results show that RFID and NFC technologies are promising innovations to trans-form public transportation to address essential weaknesses in security, adaptability, and user convenience. This work lays a foundation for introducing innovative, integrated solutions to urban mobility in a manner that promotes sustainable, adaptable, and commuter-centered transit systems.
Authors - Deepti Maheshwari, Shailesh Gahane Abstract - Website phishing poses a massive security threat that continues to increase in prevalence. Internet scammers exploit human faith by running imitation websites which aim to obtain confidential user information. An extensive review of multiple phishing detection techniques and hybrid detection models appears in this paper which brings together different detection methods to speed up and increase the accuracy of breaking down phishing-related websites. The paper investigates how machine learning (ML), artificial intelligence (AI) and heuristic-based approaches and anomaly detection should be implemented within hybrid systems which detect phishing behavior. Multiple studies from the literature receive analysis through which we identify their research approaches as well as their outcomes together with their limitations along with their contributions to the field. The evaluation will demonstrate how hybrid models can boost the detection of phished emails while detailing methods to strengthen model performance and flexible design and growing capability.
Authors - Rinkle Solanki, Shailesh Gahane Abstract - Surgical site infections (SSIs) represent a major concern for the healthcare industry, highlighting the need for timely intervention and effective predictive strategies.This paper presents an external validation framework for machine learning algorithms designed to identify and forecast SSIs in advance. We use sophisticated algorithms to create accurate predictive models using a variety of variables, including clinical features, microbiological data, and patient demographics. Across a range of patient demographics and therapeutic circumstances, thorough external validation is carried out. Our results demonstrate how effective this strategy is at precisely identifying SSIs, enabling prompt interventions, and improving patient outcomes. Surgical care procedures could be improved and medical expenses could be decreased by using validated models.
Authors - G Jeyashaathvee, Anish Pranav, Sindhu Chandra Sekharan, Jesline D, Ajanthaa Lakkshmanan Abstract - The challenge of extracting useful insights from unstructured data in the presence of big digital information is still present today. AI Intensive Timestamp based Summarization proposes a novel framework to automatically extract and summarize pivotal events, along with their corresponding timestamps from different data sets. Making use of Natural Language Processing and deep learning machine learning approaches, the system checks text data for temporal markers, extracts salient events and produces verbose summaries. The method proposed shall make the historical analysis easier and trend detection along with automated reporting of large dataset summaries more concise. Technique is implemented using Named Entity Recognition for date extraction and transformer models in case of summarization Experiments show that AI-based timestamp summarization is efficient in enhancing IR results and autodoc reliability. Making This Research Unique and Contributing to the much Expanding AI-driven text analysis filed, a Scalable in nature for timestamp extraction on domain wise basis.
Authors - Ankita Mehta, Shailesh Gahane Abstract - This paper discusses about, we display a profound learning-based approach bipolar clutter discovery utilizing Convolutional- Neural Systems (CNN) and Long Short-Term Memory (LSTM) systems. To assess the model’s performance, two particular datasets Twitter information and survey data were analyzed. Preprocessing steps, counting information enlargement, normalization, and the application of the Adam optimizer, were joined to upgrade the model’s adequacy. The model’s exactness and misfortune were measured for both datasets, and it was watched that the survey dataset given superior execution, yielding higher precision and lower misfortune compared to the Twitter dataset. These discoveries propose that the questionnaire-based information may be more reasonable for solid bipolar disorder location within the given show. The inquire about illustrates the potential of combining CNN and LSTM for mental wellbeing examination, highlighting the significance of information determination in accomplishing ideal comes about.
Thursday August 27, 2026 9:30am - 11:30am IST Virtual Room BGOA, India
Authors - Sarika G. Songire, Deepa S. Deshpande Abstract - Deep learning algorithms have completely transformed medical diagnostics by enabling accurate and efficient identification of brain tumors. Brain disorders often arise due to increased in excessive and improper cell counts, which can damage neural structures and, in severe cases, lead to malignant brain cancer. The reducing mortality rates requires prompt intervention and early discovery. This research work presents an architecture of deep neural network specifically designed for the purpose of detecting brain tumors from MRI images. The proposed model is evaluated against existing research using a similar dataset to assess its effectiveness. For comparison, performance parameters including area under the curve (AUC), recall, accuracy, precision, and loss are used. According to experimental results, the suggested CNN model accomplishes 98.61% accuracy, 99.70% AUC, 99% of both precision and recall, and 0.41 loss in a data set of 3,264 MRI images. These findings indicate that the suggested model surpasses current models and provides a dependable and effective technique for prompt brain tumor diagnosis.
Authors - Ayush Itkhede, Aryan Fulsunge, Sarthak Bomanwar, Sujal khobragade, D.M.Shinde Abstract - The integration of embedded systems into brick manufacturing has led to significant improvements in efficiency, quality control, and resource management. This paper presents an embedded system designed to optimize brick production by monitoring critical parameters such as temperature, humidity, and material composition in real-time. The system achieved a 32% reduction in production cycle time, from 6.5 hours to 4.4 hours per batch, and improved material handling speed by 41%, from 120 kg/hour to 169 kg/hour. Quality control metrics showed a 78% reduction in defect rates, from 5.2% to 1.15%, while energy consumption decreased by 29.3%, from 17.4 kWh to 12.3 kWh per 1,000 bricks. The system also reduced waste by 64%, from 8.9% to 3.2%, and improved material utilization rates from 83.5% to 94.8%. These results demonstrate the potential of embedded systems to revolutionize brick manufacturing, making it more sustainable and cost-effective.
Authors - Kamini Solanki, Rahul Vaghela, Jay Panchal, Anjali Mahavar, Jaimin Undavia, Nilay Vaidya Abstract - Diabetes is a chronic illness that affects the body's ability to metabolize glucose, which is the sugar that provides energy to the body. Type 1 and type 2 diabetes are the two main types of the disease. The immune system of a person with type 1 diabetes attacks and destroys the pancreatic cells that make insulin, which causes blood sugar levels to rise. Type 1 diabetes symptoms include excessive thirst, frequent urination, extreme hunger, weight loss, fatigue, impaired vision, delayed healing, tingling or numbness in the hands and feet, and recurring infections. AI algorithms may be used to collect and analyze medical data to support early diagnosis and treatment. Typically developing in childhood or adolescence, type 1 diabetes can be managed with the injection of insulin or an insulin pump. A person with type 2 diabetes either develops an inability to use insulin or ceases making enough of it to regulate blood sugar levels.
Authors - Dipali Wankhade, Shailesh Gahane, Mrunal Meshram Abstract - Oral cancer is one of the fatal diseases present in society. Its late-stage diagnosis puts it under the list of diseases with high mortality rates. Deep learning has conceptualized the revolution in the field of medical imaging with impressive depth and accuracy in diagnosis and early detection. This review highlights the advancements in deep-learning-based methodologies for oral cancer detection and classification, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep reinforcement learning (DRL). Multi-modal learning and hybrid models combining histopathological and radiological data have increased the precision of tumor segmentation and subtype classification. The observational data will help provide insights into the factors responsible for explaining the model's decisions and the associated risks, which are most relevant for potential applications. The development of transfer learning and self-supervised learning also seems to have significantly solved some of the most serious challenges regarding the volume of clinical data. Future studies can harness standardized practices for data collection, deploy technically sound explainable AI frameworks, and conduct clinical validations as the closing gap between tremendous strides made in deep learning and practical real-world implementation. This review provides a wide-ranging overview of such AI-driven methodologies, focused on the critical challenges and future directions for improvements in early oral cancer detection and reduced mortality rates.
Authors - Ankita Mehta, Shailesh Gahane Abstract - In this research, we focus specifically on mental disorder which is bipolar disorder using machine learning techniques, utilizing a simple dataset from Kaggle for training and evaluation. The study involves applying various ML models, including R. Forest, XG-Boost, and S. Vector Machines (SVM), on the dataset. We assess the performance of these models using evaluation MSE (how far actual value to predicted value), Precision, and Fl Score to determine their effectiveness in predicting bipolar disorder. However, through extensive experimentation, we found that the combination of (CNN) and (LSTM) networks outperformed the other algorithms, achieving an overall accuracy of 95%.
Thursday August 27, 2026 9:30am - 11:30am IST Virtual Room BGOA, India
Authors - Vijeta V Shettar, SR Nirmala, Satish Chikkamath, Suneeta V Budihal Abstract - This study investigates the performance of various speaker recognition models, including SpeakerNet, TiTANet Small, and TiTANet Large, using the IITG-MV dataset for speaker verification tasks. The preprocessing steps involved resampling, noise reduction, segmentation, and volume normalization to prepare the audio data for input into the models. The models were evaluated based on their ability to correctly verify whether two audio samples belong to the same speaker or not. The evaluation metrics, derived from the confusion matrix, revealed that SpeakerNet outperformed both TiTANet variants, achieving an accuracy of 85%, while TiTANet Small and TiTANet Large achieved accuracies of 75% and 80%, respectively. Despite lacking graphical visualizations, the confusion matrix provided a comprehensive view of the models’ performance, showing how each model handled correct and incorrect speaker match predictions. The results highlight that SpeakerNet is the most effective model for speaker recognition in this setup, demonstrating superior accuracy and robustness in identifying speaker-specific features. These findings can guide future research in optimizing speaker recognition models for real-world applications involving speaker verification.
Authors - Aoudumber Londhe, Ravindra Apare, Parikshit Mahalle, Bhagwati Galande Abstract - Wireless communication technologies, particularly those based on IEEE 802.11, have significantly improved connectivity but remain highly vulnerable to Denial-of-Service (DoS) attacks. These attacks, which exploit protocol weaknesses and resource limitations, can severely disrupt network availability, particularly in mission-critical applications such as healthcare, financial services, and industrial control systems. In this research, we investigate various DoS attack techniques targeting IEEE 802.11 networks, including deauthentication flooding, disassociation attacks, authentication request flooding (AuthRF), association request flooding (AssRF), and cascading DoS attacks.To mitigate these threats, we analyze IEEE 802.11w, which provides management frame protection (MFP), and evaluate its effectiveness under different attack scenarios. The model integrates supervised learning for attack classification, unsupervised learning for detecting novel threats, and reinforcement learning for adaptive mitigation strategies. Additionally, the system incorporates IEEE 802.11w security enhancements and anomaly-based behavior analysis to strengthen network resilience. This study provides a comprehensive review of existing DoS attack mechanisms, explores recent mitigation techniques, and introduces an advanced IDS framework to safeguard IEEE 802.11 networks against sophisticated cyber threats. Finally, the analysis is organized through a survey that evaluates the articles based on publication year, research techniques, performance metrics, toolset and utilized database.
Thursday August 27, 2026 9:30am - 11:30am IST Virtual Room CGOA, India
Authors - Vedant Patel, Vidisha Pradhan, Akshita Kadam Abstract - Blockchain technology is transforming the education sector by offering decentralized, secure, and tamper-proof solutions to many of the inefficiencies in traditional educational systems. This paper explores the role of blockchain in lifelong learning, focusing on how it addresses key challenges such as learner autonomy, credential verification, and the creation of secure, decentralized education ecosystems. Through an examination of current developments and case studies—including Blockcerts, Sony Global Education, and Woolf University—the paper highlights blockchain’s applications in academic data storage, personalized learning pathways, and digital credentialing. Additionally, this study discusses the opportunities blockchain provides for improving transparency and trust in the verification of academic credentials across borders. While the technology presents promising solutions, significant challenges remain, including issues of interoperability, privacy, scalability, and legal frameworks. The paper concludes by outlining unanswered questions and future directions for research, emphasizing the need for standardization, privacy-preserving technologies, and scalable implementations to fully harness the potential of blockchain in lifelong learning.
Authors - Umesh Kumar Pandey, Mamta Santosh Nair, Shikha Gupta Abstract - Start-ups are buzzing word around the world. These start-ups need funding in their early stage with a high risk of failure and violation of the innovator's intellectual property. Any system's prime responsibility is to ensure the fund availability to the start-up and save the innovator's intellectual property since blockchain has become the chief technology in digital crypto-currencies. Bitcoin. Blockchain has become popular in finance, the health sector, social services and many more areas where transactions are recorded among the parties, known or unknown—the critical features of block Chainz. Decentralisation, distribution, immutability, transparency and audit-ability enrich the usability of this technology and increase the trust to use it. Therefore, a system is proposed here to manage start-ups utilising blockchain features. The proposed system ensures that parties to the contract have more confidence and feel safe to grow start-ups in the community and prevent unnecessary conflicts.
Authors - Smrity Dwivedi Abstract - This manuscript has oriented to new generation and new technology used for required resources and terms, which gives wide bandwidth for each and everyone’s perception. For this reason, microwave frequency area eight to 10 GHz has been explored for 5G and also 7 to 20 GHz is being explored for beyond 5G. This is why both possibilities were taken right here. First assessment among hexagonal and triangular structure complete floor were designed with CST microwave studio. Results obtained from those designs are -23.35dB for 7.77dBi benefit and -29.103dB for 7.85dBi gain for hexagonal and triangular systems respectively. For enhancing the advantage, a partial ground has been used for triangular structure and 10.5dBi has been completed at -38.65dB S11 and for 9.0988 GHz frequency. Bandwidth is increased from 0.29 GHz to 0.32 GHz. Everything is simulated and analysed by simulation software. Novelty is the simple structure gives beyond 5G applications.
Authors - A.Punidha, E.Arul, E.Yuvarani, S.Rajasakaran Abstract - Understanding how dark patterns influence user sentiment is crucial for developing ethical and user-friendly digital experiences. This study evaluates the performance of XGBoost and Random Forest in predicting sentiment (negative, neutral, or positive) based on user interactions. The models were assessed using accuracy, precision, recall, and F1-score, with results indicating that XGBoost outperforms Random Forest, achieving an accuracy of 88.4% compared to 85.9%. To enhance interpretability, SHAP (Shapley Additive Explanations) was used to break down model predictions and identify the most in-fluential features. The analysis revealed that "Number of Clicks" and "Time Spent on Page" were the strongest indicators of user sentiment, particularly in detecting frustration associated with dark patterns. The results provide valuable insights into how machine learning models interpret user engagement and emphasize the importance of transparent AI-driven sentiment analysis. By leveraging explainable AI techniques like SHAP, this research contributes to improving trust in sentiment classification models and guiding the development of more user-centric digital interfaces..
Thursday August 27, 2026 9:30am - 11:30am IST Virtual Room CGOA, India
Authors - J. Jeslin Shanthamalar, Prateesh Kumar S, Abishin J, Sindhu Chandra Sekharan, Malar Selvi G Abstract - There is a growing necessity for noninvasive and sophisticated diagnostic capabilities with the ability to very early prediction of skin conditions from the patient. Timely diagnosis is a powerful influence on patient care out comes, access to dermatologists is not, especially in rural. We propose an AI and deep learning model for improving classification of skin diseases in a highly accurate advantageous manner. This model, which follows the architecture of Convolutional Neural Network, trained on a multi-class skin disease images dataset where every image has a label per lesion. Through hyperparameter fine- tuning, the model is optimized to achieve performance from metrics that include accuracy and trade-off accuracy vs. precision/recall. With user-friendly access in mind, the model runs into the app (web or mobile) that supports a friendly diagnostic user interface. Advanced security floor work is taken within designed to reduce the effect of adversarial attacks. Multimodal processing (text, image and speech inputs) improves classification substantially resulting in accurate and robust diagnosis. The platform has been built based on healthcare professionals and patients' input to provide a easy-to-use diagnostic tool. Research to edit the ai applications in dermatology through fewer dataset bias, more human like NLP explainable models, as well as ongoing work for improved security. In the end, this system is what makes skin disease detection accessible and fast via AI- determined aids an inclusivity in healthcare.
Authors - Aravind A R, Archa A S, Gouri S Krishna Abstract - With India rapidly embracing digital transformation, initiatives like UMANG by the government are the means to achieve online public services. Although UMANG offers over 1,750 services of many departments, it has some critical accessibility concerns, particularly for differently-abled citizens. In this study, we evaluate the UMANG website for WCAG 2.2 compliance using a two-stage method: automated checking using AccessibilityChecker.org and user review analysis using Appbot. Findings identify prominent issues of poor ARIA labeling, flawed heading order, inadequate color contrast, and improper focus order as hindrances for assistive technology users. Sentiment analysis also indicates frustration with usability, login failure, and performance. For the improvement of accessibility, the present study has some suggestions that make UMANG equivalent to international standards. By making inclusive design central to e-governance, India can enjoy equal access to fundamental digital services, creating an inclusive digital space.
Authors - Thisura S. Wijesekera, Dinuka R. Wijendra Abstract - Kubernetes has become the leading container orchestration platform due to its powerful scalability features, enabling dynamic resource management and efficient workload handling in cloud-native environments. This review examines Kubernetes scaling mechanisms at both the application and cluster levels, focusing on Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and event-driven scaling with KEDA for adaptive application scaling. At the cluster level, Cluster Autoscaler (CA), Karpenter, Cluster Proportional Autoscaler (CPA), and Cluster Proportional Vertical Autoscaler (CPVA) optimize node provisioning and resource allocation. Despite these advancements, challenges persist, including reactive scaling delays, resource fragmentation, inconsistent scaling decisions across multiple autoscalers, and security vulnerabilities like Economic Denial of Sustainability (EDoS) attacks. To address these issues, emerging trends in AI-driven observability, predictive analytics, and unified autoscaling frameworks offer proactive scaling, anomaly detection, and self-healing capabilities. This review synthesizes academic research and industry practices to highlight the current state, challenges, and future directions of Kubernetes scalability, emphasizing the need for intelligent, adaptive, and secure scaling solutions.
Authors - Suruchi Pandey, Hemlata Gaikwad, Yograj Ingale, Neha Sharma Abstract - This article looks at how organisational culture, resource allocation, and decision-making procedures reflect sustainable leadership principles in different settings. A comparative investigation shows that military commanders place a higher priority on mission success and national security than do business executives, who place more emphasis on profitability and shareholder value. Nonetheless, there are similarities between the two fields, including the value of making moral decisions, flexibility in the face of change, and an emphasis on long-term goals. The role of innovation in sustainable leadership is also examined in this article, with particular attention paid to how strategy development and technology support organisational resilience. Additionally, it looks at how sustainable leadership affects worker engagement, emphasising how crucial it is to develop a feeling of dedication and purpose. This article seeks to provide a more comprehensive knowledge of successful leadership techniques by exploring the subtleties of sustainable leadership in business and defence environments. Regardless of the particular difficulties they encounter, executives looking to im-prove the sustainability and resilience of their organisations may gain a great deal of insight from identifying the parallels and variations across these industries.
Authors - G. Ram Sundar, Sindhu Chandra Sekharan, Taruni Mamidipaka, Yoga Sreedhar Reddy Kakanuru, Priyadharshini M Abstract - The traditional sarees in India represent a rich history both culturally and artistically, as their patterns are created from regional influences along with modern fashion. Making sarees requires detailed skill, and traditional techniques are highly laborious and time-consuming. In this research, we have developed Vastra Kalpana, an AI-driven saree design generator that uses generative deep learning models to automate textile pattern creation. Users can now provide voice commands, and they are converted to text prompts by our integrated OpenAI Whisper speech-to-text software, which are then transformed into structured textual descriptions. These descriptions serve as instructions for the Stable Diffusion's high-resolution saree design generator. Our research results suggest that this automated saree design generator is both solution oriented and efficient, proving the generative techniques offer a novel approach for saree design while tackling the challenges of overreliance on handmade designs. This research mainly contributes to the field of fashion design and establishes a framework for future advancements in automated textile pattern generation.
Authors - Ajit Patil, Amol Potgantwar Abstract - In the era of Industry 4.0, accurate time series prediction is crucial for extracting valuable insights from high-frequency sensor data in Industrial Internet of Things (IIoT) applications. This paper presents EERA: A Hybrid Ensemble Regression Model designed to improve predictive accuracy for time series data in IIoT environments. EERA combines the strengths of multiple base models, including REPTree, SMOreg, and Multi-Layer Perceptron (MLP), through a weighted ensemble approach to achieve better overall performance. The model was tested using a real-world dataset that captures heat index data (temperature and humidity), which has diverse applications in areas such as agriculture, weather forecasting, and enterprise maintenance. Comparative analysis shows that EERA outperforms individual models, achieving a Mean Squared Error (MSE) of 4.150960 & R-squared value of 0.872540, demonstrating high predictive accuracy. These findings suggest that EERA is a dependable &1 effective solution for time series prediction in fast-paced IIoT data environments.
Thursday August 27, 2026 9:30am - 11:30am IST Virtual Room DGOA, India
Authors - Manikrao Dhore, Parth Mahajan, Pratik Meshram, Ashish Nikam, Samarth Otari Abstract - Deforestation and improper plantation of trees are the key issues in realizing sustainable environmental management. The AutoForest PlantBot, an autonomous robot system, is introduced in this paper, which makes use of advanced image processing, path optimization, and real-time navigation for efficient tree plantation. The system employs the Deep Forest Package for 92% accurate tree detection and uses Dijkstra's algorithm to find optimal routes, cutting tree removal by 40% as compared to traditional straight-path approaches. The hardware system consists of an Arduino-controlled rover with BO motors, a GPS module, ultrasonic sensors, and an automated drill mechanism, providing accurate plantation with an accuracy of ±2 cm. The outcomes validate the system's potential for large-scale reforestation applications. Future developments will emphasize integrating reinforcement learning for adaptive path optimization and using renewable energy sources for sustainable operation.
Authors - S. Rahul, Anusha Preetham, Aniketh Patil, Abhishek Nimbal, Sahana Meti Abstract - This paper introduces Dr. BOT, a comprehensive web-based healthcare application designed to overcome language barriers in medical communication across diverse linguistic environments. While initially trained to predict several diseases including diabetes, heart disease, kidney disease, liver disease, and breast cancer, the system's architecture enables expansion to detect and interpret a wide range of medical conditions. Dr. BOT employs robust machine learning algorithms (Random Forest, Support Vector Machine, and Logistic Regression) trained on validated datasets, with special emphasis on multilingual functionality through a hybrid approach combining NLP with neural machine translation models specifically finetuned for medical terminology. The platform operates effectively in low-connectivity environments through innovative offline capabilities, offering preventive healthcare guidance and localized medical resource information in users' native languages, thereby supporting both individuals and healthcare providers in improving health outcomes globally.
Authors - Prasad Chaudhari, Ritesh V. Patil, Parikshit N. Mahalle Abstract - The Agricultural Productivity Enhancement System leverages data-driven pattern classification and machine learning-based fertility detection to improve farming efficiency. The architecture integrates IoT sensors, satellite imagery, and soil analysis to collect crucial agricultural data. A preprocessing module ensures data cleaning and feature extraction, storing refined data in an agricultural repository for further analysis. Machine learning models, including pattern classification and fertility detection, process this data to assess crop health and soil fertility. A decision support system then provides real-time recommendations to farmers, enhancing precision agriculture. Researchers and data analysts contribute to model refinement, ensuring scalability and adaptability. This system optimizes resource allocation, reduces wastage, and increases crop yield by enabling real-time, AI-driven decision-making.
Authors - Shiva Jyoti, Samriddhi Ganguly, B Sri Soumya, Nachiyappan S Abstract - This paper presents a comprehensive study on the Clinical Readiness Score (CRS), a structured evaluation metric for assessing AI models used in lung cancer diagnosis. The CRS incorporates multiple criteria such as interpretability, efficiency, clinical validation, and accuracy, employ- ing the Analytic Hierarchy Process (AHP) for weight assignments. This study discusses the methodology behind CRS, validates its consistency, and explores its practical implications. Additionally, graphical represen- tations of AHP weight distribution, sensitivity analysis, and CRS factor contributions are provided for better comprehension.
Authors - Harjas Singh Bajwa, Lokesh Jayakar, Abhiyanshu Singh, Prafulla Bafna, Mukta Deshpande Abstract - Builders often open sales of their property in India as soon as they purchase out land, they do this in order to secure funds to carry out their construction operations, for this they often make rendered photos and videos of concept property. Traditional property marketing techniques like images and videos lack interactivity and fail to provide a 360-degree view of properties. This research work explores the role of metaverse-driven, gamified, interactive property tours in enhancing pre-construction sales. Unlike previous studies focusing on metaverse real estate as an investment platform, this research emphasizes its ability to engage buyers, boost confidence, and aid decision-making. By combining insights from virtual real estate, Augmented Reality (AR) , virtual Reality VR, gamification, and Artificial intelligence (AI) customization, a metaverse-based property visualization framework is proposed. The study highlights how interactive walkthroughs, real-time customization, and immersive storytelling increase trust and engagement. Gamification elements, such as virtual staging, achievement systems, and AI-led personalization, deepen buyers’ connection with properties.
Authors - Ajay V, Sharon P S, Philomina Simon, Ambily George, Mehanas Shahul Abstract - This work delineates an inquiry into how artificial intelligence identifies multiple emotions in texts. Unlike mere sentiment analysis, which is a simple positive, negative, or neutral classification of text, multilabel emotion classification requires a more intense understanding of the text. The paper examines various challenges in multi-label emotion classification, where emotions often overlap (e.g., joy and surprise) and have varying frequencies in datasets. Traditional machine learning and deep learning based models such as BERT and other transformer-based models, show sufficiently strong performance in capturing nuances of emotional expression in text. Moreover, it addresses the issue of how this task can be distorted by linguistic and contextual diversity and diversity and therefore how such systems should be evaluated with respect to these variables.
Thursday August 27, 2026 9:30am - 11:30am IST Virtual Room DGOA, India
Authors - Gowri Shaju, Lekha S Nair Abstract - Histopathology refers to the study of a disease at a cellular level which stands as a golden method of predicting breast cancer. In this paper, a comparative study of the performance of machine learning models trained using optimized feature sets is done. The experiments are conducted using two datasets. The first is the Wisconsin Breast Cancer dataset, which contains 30 extracted features of cell nuclei. The second is the MITOS-ATYPIA 14 dataset, consisting of histopathology images, from which hand-crafted features have been extracted. Population based metaheuristic optimization algorithms are used to optimize and choose the key features from the available feature set to increase the efficacy of the model. Support vector machines, logistic regression model and other classification models are tested using this optimized feature set. To evaluate the impact of feature optimization, accuracy, precision, recall, and F1 score are assessed using both the full feature set and the optimized subset from two datasets. The results demonstrate how model performance varies with different feature sets, underscoring the significance of optimization techniques in enhancing machine learning-based breast cancer diagnosis in medical imaging.
Authors - Kalyanasundaram V, Keerthi AJ, Krishnaa RK, Thirumurugan A, Joshua Sunder David Reddipogu Abstract - The volatility of the stock market offers a big challenge to traders depending on timely and correct information for informed decision-making. Security risks, including fraudulent practices and theft of identity, threaten online trading platforms. The present paper presents an AI-based stock trading app that tackles these issues by using predictive analytics with robust security features. The system also employs Azure AutoML to work through historical stock data, identify market trends, and generate livestock predictions to enable traders to respond proactively to fluctuations. For security reasons, the app employs Azure Document Intelligence for live Know Your Customer (KYC) verification to ensure that only valid users have access. Additionally, the platform automates document processing using AI-powered text extraction, minimizing errors from manual input and increasing efficiency. Developed with Flutter for smooth cross-platform use and backed by Azure cloud infrastructure for scalability and dependability, this software solution offers an intelligent, secure, and user-friendly trading experience. Through the integration of AI-based forecasting with robust security measures, this work helps develop more efficient, reliable, and technologically sophisticated stock trading platforms.
Authors - George Sebastian, K. Ananda Krishnan Menon, Migheal Newton, Sonal Shaju, Haneesh K. M Abstract - Deep Vein Thrombosis (DVT) is the formation of blood clots in the lower limb because of prolonged immobility. Such critical medical conditions can be avoided by regularly using compression cuffs on the limbs; however, traditional compression devices lack adaptability, cause patient discomfort, and have inconsistent pressure application. This study presents a smart wearable and portable compression system integrated with sensors to receive real-time feedback. A DC motor, controlled by an H-bridge converter, inflates the system’s inflatable sleeves. The DC motor speed controls the pumping pressure, and a solenoid valve controls the inflation rate. Pressure, temperature, and moisture sensors are embedded in the inner part of the cuff to monitor the physiological parameters. An Arduino-based control system was used to control the inflation rate, air pressure, and duration of compression, optimally ensuring patient comfort. The designed pump was tested and shown adaptability when the sensor data changes. An AI-based control framework is also proposed in this work to enhance the performance and to make the pump autonomous and user-friendly. The response of the proposed AI-based control was validated through simulations of the model developed from fundamentals. The simulation results suggest that the AI-based DVT pump is more adaptable to the physiological parameter variations, even when the parameters change rapidly. The AI-driven model provides faster and more precise control of inflation and deflation patterns, preventing overheating, over-compression, and sweating. This study highlights the feasibility of a smart, wearable DVT pump that can adapt to the compression requirements while ensuring safety and comfort.
Authors - Rachit Chetankumar Mehwala, Angshuman Kishore Mahato, Patil Sujit Maruti, Surendra Solanki, Gaurav Kumawat, Ravindra Kumar Soni Abstract - Disaster whether it is natural or man-made most of time led to significant challenges to society, often resulting in loss of life, economic instability, infrastructure damage. Effective "Disaster Management" requires seamless communication and good connectivity under extreme conditions. 6G technology with its capabilities such as ultra-low latency, large bandwidth, and terahertz frequencies offers an evolutionary approach to real-time disaster management. This paper explores how 6G technology can be harnessed to build reliable system capable of alleviating the impacts of disasters. By using 6G’s unparalleled communication capabilities we propose framework that ensure strong connectivity and efficient resource allocation in real-time and monitoring during disasters.
Thursday August 27, 2026 9:30am - 11:30am IST Virtual Room EGOA, India
Authors - Kumkum Saxena, Akshay Rathod, Shagun Gupta, Archie Shah, Deep Prajapati Abstract - Professional scarcity, the absence of individualized treatments, and access to mental health assistance only in limited regions creates problems in mental health care. These issues can fundamentally be solved with the introduction of AI. AI has powerful applications in the realm of mental health care, including systematic classification and analysis of data, as well as facilitating tracking and predictive treatment that leads to personalized medicine. Chatbots, predictive analysis and cognitive computing try to give precise diagnosis by facing the unsolved challenges in the empiric domain of cognitive sciences. This review attempts to highlight the need for multidisciplinary collaboration and more research so that mental health care AI systems are more inclusive.
Authors - Amruta Amuna, Hardik Rokde, Anuj Gosavi, Gaurang Gulhane, Ishaan Chepurwar, Arnav Jadhav, Vinayak Musale Abstract - In today’s digital age, most of the work has become sedentary. Whatever you want to do is now available at the tip of your fingers, reducing your physical activity and causing individuals to suffer from chronic health issues, such as back pain and postural disorders. Yoga has been publicised throughout the world, and now all of us are aware, and many of us even perform yoga regularly. But doing yoga is not enough. We must do it efficiently & with the correct posture to get the benefits of yoga. It has been observed that more than 40% of people doing yoga do it incorrectly. This motivated us to develop a smart, Iot-enabled solution that addresses this problem during yoga practice. The system integrates Force Sensing Resistors (FSRs) to measure the force applied on the sensor, LED indicators to guide the correct position for palms and feet for every yoga pose, and a buzzer to give auditory feedback for misalignment. By combining all of these along with the Arduino Uno microcontroller board, this solution bridges the gap between self-guided yoga practice and expert supervision, making each yoga pose more efficient, easier, and more effective for the user’s body.
Authors - Sahana S, Umabharati H, Rakshita.G, Vaishanvi.K, Nikita Patil Abstract - In a populous nation like India, one fundamental need is travel so travel encompasses road, rail and water. Road transport is most utilized and also the prime reason people get to lead simple lives. Even after studying the present situation, we found a major problem on the roads: potholes. These potholes have become a source of harm to the condition of the roads and an additional threat of accidents on the roads. The detection of potholes is vital for safety on roads. The best method for pothole detection is using the real-time accurate efficient YOLOv10 model. A Raspberry Pi Camera Module can record real-time video and images of the road. Further, the Raspberry Pi can be integrated with a GPS module to find the precise coordinates of any potholes. The data generated by the GPS module is helpful in making repairs and guiding drivers in choosing routes. The system relies on a Convolution Neural Network (CNN) model, which assists in pothole detection using YOLO models.
Authors - Jai Ramani, Tanay Kelkar, Darshil Shah, Divanshu Maheshwari, Archana Nanade Abstract - Anonymized employee reviews on platforms like Ambition-Box offer insights into workplace experiences such as salary, work culture, working hours, and management quality. However, manually analyzing large volumes of reviews is challenging and time-consuming. To overcome this limitation, an automated system is proposed to collect, process, and present employee sentiment in a structured and meaningful way. Using the technique of Aspect-Based Sentiment Analysis (ABSA), the system classifies reviews as positive or negative while identifying sentiment across key concerns such as salary, work-life balance, career growth, management quality, etc. To identify the keywords and their corresponding sentiment, this study utilizes the T5 model that is fine-tuned using the InstructABSA framework. Data is gathered through web scraping, ensuring coverage of employee opinions from multiple platforms. The resulting analysis highlights areas where companies excel or need improvement, providing actionable insights to enhance the workplace.
Authors - Dhaval Shah, Shivani D. Anjaria, Bhupendra Fataniya Abstract - Hardware Security is the key aspect of the integrated circuit’s life cycle; Any malicious modification in the system design at the foundry is a significant concern for hardware threats, known as a Hardware Trojan attack. These Trojans are very difficult to detect in the real world, even during manufacturing and testing. In this article, the impact of Hardware Trojan on the performance of the cache memory is presented. Insertion of Trojan demonstrated in the cache replacement policy, which replaces the original cache replacement (least recently used, first in first out, and least frequently used) policies with another replacement policy (most recently used). The performance was analyzed in the gem5 simulator after inserting a Trojan. It was clearly evident that Trojan insertion degrades cache performance and affects overall processor performance. It is observed that the impact on the Trojan was a savior on LFU compared to other cache replacement policies, since LFU persists in its counter-based memory.
Authors - Ajay Talele, Revati More, Satej Patil, Shreya Bedre, Gokarn Nemade, Harshwardhan Vanmore, Dipak Parvate, Sakshi Dhumale, Varun Deshmane, Vedika Dange Abstract - An Advancement in Effective Parking Solutions: The Smart Car Parking system. In cities, parking congestion results in wasted time, fuel, and irritated drivers. By offering real-time parking availability updates, expediting the procedure, and lowering traffic in parking lots, the Smart Car Parking System provides an answer. The system, which was constructed with an Arduino Uno microprocessor, uses infrared (IR) sensors to identify whether a car is in each slot. To assist drivers in making educated judgments, a linked LCD shows real-time data on available and occupied spaces. Entry and exit barriers are controlled by servo motors, which grant only permitted access. The technology automatically updates the slot status when cars enter or exit. This improves traffic flow, lowers pollutants, saves fuel, and lessens the need for manual supervision. The system is perfect for public lots, workplaces, malls, and residential areas. It can be improved with AI for space optimization and IoT-based apps for slot reservations, increasing the sustainability and efficiency of urban parking. [5]
Authors - P Sanjana, Smrthi Harits, Divyadarshan C.S Abstract - This project aims to solve the issues faced by Bharatanatyam dancers in accessing the translations and interpretations of compositions used for Bharatanatyam performances. Understanding these compositions is crucial in depicting an accurate vision of the composition. Through structured user interviews, it was discovered that due to the non-preservation of these translations and limited access to scholars, accurate translations, and music resources, dancers face significant issues. This hinders their creative process and restricts their creative freedom. Compared to experienced dancers with access to vast resources, the upcoming artists have very few such connections and guidance, making them more vulnerable to this problem. This necessitates a solution to reduce the accessibility issue faced by the upcoming dancers. Various design methodologies were employed to tackle the issues faced by Bharatanatyam dancers. To bridge this gap, Kalaahithaa, a digital service platform, was designed to provide a repository of verified translations, connect dancers to scholars, musicians and teachers to help them understand the compositions, create new compositions and choreographies, and foster collaborative opportunities. This solution was further developed using a service design blueprint, user flows, site maps, and low-fidelity and high-fidelity prototypes. The platform supports personalised interactions, enabling dancers to seek expert guidance, upload and access translations, and engage in meaningful exchanges that enhance their understanding of compositions while promoting monetary ethical practices. By centralising resources and fostering connections with a usercentred approach, Kalaahithaa serves as a vital tool for dancers to refine their art while preserving the integrity of Bharatanatyam.
Authors - Sonali Antad, Kalyani Ghuge, Prakash Sharma, Vaishnavi Chirawande, Aditi Gade, Shweta Ahire, Sharvari Jadhav Abstract - This study describes an AI healthcare chatbot that automatically assesses patients’ medical needs, conducts inter- views and performs thorough health analyses. Using several state-of-the-art natural language processing (NLP) models, including sentence transformers and Llama-based large language models, the system analyzes user symptoms and classifies them into specific medical domains like diabetes, blood pressure, skin and stomach disorders. It uses Gemini API’s generative AI. This dynamically creates several questions and suggests some medical diagnoses. The platform’s improved fishbone diagram considerably aids root cause analysis by visually showing how potential causes relate to user-reported symptoms. Our project creates efficient, interactive healthcare assistants that improve access to preliminary medical advice.
Authors - Rhucha Deodhar, Tanya Gadwal, Ananya Bhat, Aditi Hinge, Shilpa Pant Abstract - This paper presents a real-time, vision-based system for Indian Sign Language (ISL) recognition and translation, aimed at enhancing communication between the deaf community and non-signers. The system combines a CNN-LSTM architecture for static gesture recognition, achieving an accuracy of 98.47% and introduces GestureNet, a bidirectional LSTM model trained on a custom dynamic gesture dataset, which attains 96.83% recognition accuracy. Ad-ditionally, a Generative AI framework is integrated to convert recognized ges-tures into semantically coherent and contextually appropriate sentences. By em-phasizing real-world applicability and high recognition performance, the pro-posed system advances sustainable and accessible communication technologies, with potential impact in education, public services, and digital inclusion, partic-ularly in developing regions.
Associate Professor & Head, Department of Computer Science & Engineering, CSPIT, Charotar University of Science & Technology (CHARUSAT), Gujarat, India
Thursday August 27, 2026 11:30am - 11:32am IST Virtual Room DGOA, India
Assistant Professor, Department of Computer Science and Engineering (Artificial Intelligence & Machine Learning), Vishwakarma Institute of Technology, Pune, India
Thursday August 27, 2026 12:28pm - 12:30pm IST Virtual Room CGOA, India
Authors - Darshan L.M, Nagasundara K.B Abstract - Nowadays, most of the people alters/or conceals their true facial appearance intentionally and/or unintentionally by wearing various disguise accessories such as sunglasses, artificial beard and moustache, face make-up, and many more fancy items. Since, these accessories obscures the prominent facial features, the traditional face recognition systems have not shown a notable recognition performance and thus its performance is challengeable in the various applications fields such as immigration and border control, national security, surveillance, and many more. In literature, IIIT-DDFD, IMFDB, and FDB are disguise datasets are available for Indian ethnicity. Our analysis indicates that, these datasets are not sufficient with enough facial samples to meet the current trends. Therefore, we have introduced an Indian celebrity disguise face dataset (ICDFD), which includes the samples with wide range of complex disguise variations combined with pose, illumination, and expression. Initially, we analyze the performance of these datasets using holistic approaches and followed by deep learning models. From the experimental analysis, it reveals that the deep learning models have shown an optimal performance over holistic approaches. It is observed that, the disguised faces are continue to pose wide open challenges for the researchers in the area of computer vision.
Authors - Harini S, Shamila Ebenezer A Abstract - The document introduces a protected blockchain application for patient care that improves system visibility and operational speed. The application allows patients to book appointments which hospital administrators check before authorization. Following permission by administrators, patients may evaluate their appointment schedule and doctors analyze medical records for medical assessments. Specialist approval must authorize the download of prescription reports. Through blockchain technology physicians can perform decentralized transactions as well as track assets and exchange secure data which leads to lower operational costs and better trust and platform collaboration between patients and specialists and administrators.
Authors - Vishwesh Kumbhre, M. L. Dhore, Pradhynan Lohar, Ajinkya Lende, Om Popade, Shivkumar Padalwar Abstract - Augmented Reality has contributed in many important fields like Education and other fields like Medical and Military, which needs 100% accuracy and focus while performing certain operations. These tasks can be perfected if you have a thorough practice of every situation and gain knowledge about everything being used in that instance. This requirement is fulfilled by Augmented Reality where it creates virtual objects on a real-world background which gives us a real time experience, as if we are actually performing these tasks using solid objects. And the responses are as legit as they would be, in real experiments. Using these features, we can make different software which guides the students and cadets in real life situations.
Authors - Anjali Mahavar, Atul Patel, Ashish Patel Abstract - When given to the human body, different substances and products might present varying health hazards. Concerns regarding toxicity have caused fewer new medicines to access the market throughout the years via the conventional drug development path. The choice of lead compounds and ADMET research depends much on the use of in silico toxicity prediction techniques as ethics, time, money, and other resources often limit in vitro and in vivo approaches. In this regard, we propose a variety of toxicity tools that use structural and physicochemical- based characteristics in the form of molecular descriptors and fingerprints to assess the carcinogenicity of five distinct imidazo[1,2,a]pyridine ligands, including Protox-3, VenomPred, PkCSm, etc. According to the results of the in silico toxicity tool, ligand-1 (IP-1) has a low likelihood of carcinogenicity (0.56%) and excellent accuracy (67.38%) when compared to other ligands. As a consequence, it can be the first choice for medication development in the treatment of cancer.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room AGOA, India
Authors - Atharv Khunte, Vishakha Dhotare, Divya Pawar, Nikita Dandgavhal, V.M Kokane Abstract - For increasing cyberattacks, web applications require robust and scalable security mechanisms. We suggest a centralized firewall structure that effectively detects and blocks attacks on multiple hosts simultaneously. The system is a command center that collects information about attacks from clients not yet attacked. Information collected is transmitted to the afflicted client only after receiving all necessary details. The system works by detecting and neutralizing various forms of cyberattacks such as SQL injection (SQLi), cross-site scripting (XSS), and distributed denial-of-service (DDoS) attacks. As soon as malicious activity is detected, the system will automatically block the attacking IP address, preventing further intrusion. This process enhances real-time protection, limiting the likelihood of repeated cyberattacks on interconnected web applications. By employing a single defense method, this centralized firewall maximizes threat intelligence sharing, rendering all the connected clients secure. Unlike standalone firewalls in the past, this approach consolidates security policies and enhances cybersecurity resilience on various platforms. Further, this system not only secures web applications against emerging threats but also ensures that organizations meet cybersecurity compliance requirements by hosting a neat and responsive security mechanism. The centralized structure of the fire-wall provides early attack detection, largely reducing downtime, data loss, and monetary loss.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room AGOA, India
Authors - Prajakta Prasad Kohale, Michael Savariapitchai Abstract - Communication is fundamental for quality healthcare. It is the bridge between the patients and the provider. The basis of any group teamwork and an important factor in an efficient healthcare system is communication. The objective of this paper is to trace the history of communication’s evolution from basic, traditional models to the complex systems that are present in modern-day healthcare. Effective communication helps in establishing trust and confidence which motivates both the patients and the care teams to work together and take accountability for their health. The most favorable health outcomes are shown in patients who feel most sincerely cared for.
Authors - A.Punidha, E.Arul, E.Yuvarani, S.Rajasakaran Abstract - Device drivers are a critical component of modern computing but are increasingly targeted by attackers to gain unauthorized access, execute malicious code, or escalate privileges. Traditional malware detection techniques, such as signature-based and heuristic methods, struggle against advanced threats that employ evasion tactics. To address this, we propose a Graph Neural Network (GNN)-based framework that leverages feature engineering and graph-based learning to detect malicious drivers with high accuracy.By modeling system execution as a graph, our approach captures complex dependencies between API calls, memory accesses, and kernel interactions. We employ Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) to analyze these relationships, enabling detection of even stealthy and obfuscated malware.Experiments on Windows, Linux, and Android driver datasets demonstrate that our model achieves a 95.8% accuracy, outperforming Random Forest, XGBoost, LSTM, and CNNs. The model is also robust against adversarial evasion techniques, making it a scalable and effective solution for endpoint security, malware sandboxing, and kernel protection.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room AGOA, India
Authors - R Saiprithvi, Sindhu Chandra Sekharan, Summia Parveen, Ajanthaa Lakkshmanan, Jesline D Abstract - Diseases of lungs like asthma, Chronic obstructive pulmonary disease, lung cancer are among the leading causes of death across the world. Ensuring better outcomes for patients with any medical condition requires an early diagnosis which is, unfortunately, technologically impossible in many regions. This work presents a multimodal Conversational Artificial Intelligence approach based on X-ray imaging, respiratory sound processing, and conversational interfaces that can help with the early and easy diagnosis of lung health. A Convolution Neural Network Processes X-ray images and reliably captures abnormalities. A Random Forest classifier examines MFCC features of lung sounds to confirm presence of asthma, bronchitis, and other diseases. The last method involves using a conversational AI chatbot that makes the collection of symptoms more convenient and provides the user with further information. with the capture of volumetric imaging, sound, and text, healthcare accessibility, efficiency, and diagnostics can be achieved using Artificial intelligence in imaging technology. Lung health is of primary importance, but millions of people worldwide live with easily preventable respiratory disease. EWHO alone estimates that more than 300 million people around the globe suffer from chronic lung disorders, including asthma, Lung
Authors - Job Joseph, Shivaprakash S, Rahul, Rojalin Patri Abstract - This research investigates the impact of financial influencers (finfluencers) on investment decisions of students using trust, perceived risk, and ethical concerns. Correlation and regression analysis reveal strong inter-linkages among them. The implications are drawn noting students' increasing utilization of social media as a source of personal finance information and both its benefits and risks. This work informs financial literacy scholarship and provides recommendations for policy change to contain the influence of finfluencers. Additionally, findings from current research show that finfluencers are not only educators but also business entities that act with self-interest, influencing market behaviour and investment decision-making among retail investors.
Authors - Dheeraj Hegde, Aishwarya Kalatippi, Prajwal Shiggavi, Satish Chikkamath, Nirmala S.R Abstract - This study presents a novel approach to image representation, utilizing wavelet transforms to compress image information into a compact latent space. Wavelet transforms offer a multi-resolution analysis, decomposing images into different frequency components at different resolution scales, emphasizing the spatial and frequency attributes. By leveraging the hierarchical structure of wavelet coefficients, we construct a latent space representation preserving essential features while reducing dimensionality. Experimental evaluations on benchmark datasets demonstrate competitive performance in tasks such as compression and classification compared to traditional deep learning approaches. Waveletbased representation offers promise for addressing challenges in highdimensional data while retaining crucial image information for diverse processing tasks.
Authors - Madan Kumar Sharma, Nadir Kamal Salih Idries, Abdullah Said Alkalbani, Satyanarayana Degala, Gopal Rathinam, Ankit Sharma Abstract - Multi-resonance (MR) Microwave sensors have emerged as a promising sensing device for mineral-based materials characterization due to their high sensitivity and precision. This research presents a novel microwave sensor for mineral-based material characterization. The sensor's resonating structure comprises a 3 × 3 array of circular-shaped complementary split-ring resonators (CSRR) coupled with four rectangular defected structures etched around the CSRR array. This unique configuration enhances electromagnetic interaction with the material under test (MUT), leading to precise characterization based on S-parameter analysis. To validate the sensor’s efficacy, simulation-based investigations were conducted on the mining-based materials, including chrome, copper, and quartz. The obtained results demonstrate distinct resonance shifts and attenuation variations corresponding to each mineral, highlighting the sensor’s capability to differentiate and analyze their dielectric properties. The proposed MR-sensor design provides a robust and efficient method for non-destructive material characterization, offering potential applications in the mining industry, quality control, and geophysical exploration.
Authors - Geethu Lakshmi G, P. Nagaraj, P. Chinnasamy Abstract - Lung cancer detection constitutes a paramount process in the diagnosis and management of one of the predominant contributors of cancer-induced humanity worldwide. The significance of early screening is underscored by its essential role in enhancing survival rates through the identification of disease during a stage amenable to treatment. Diagnostic methodologies, including imaging modalities, are routinely utilized for diagnosis. Furthermore, advancements in the realm of molecular biology have facilitated the emergence of biomarkers and genetic examines, thereby enabling a more accurate identification of lung cancer. The prompt and defined detection of lung cancer facilitates timely therapeutic interventions, which significantly influence both the efficacy of treatment and overall outcomes for patients. The primary objective of this research is to develop an optimization-based hybrid deep learning methodology for the detection of lung cancer. The initial phase involves pre-processing of input images through techniques such as color space transformation, data augmentation, resizing, and normalization. Subsequently, features derived from Slime Mould Algorithm-based Convolutional Neural Network (SMA-CNN) are employed for the detection of lung cancer, with CNN being trained utilizing SMA, extracted from pre-processed images. Finally, the Squeeze-Inception V3 model, which integrates SqueezeNet and Inception V3, leverages SMA to train the classifier. Consequently, the proposed SMA-based hybrid SqueezeNet-Inception V3 is utilized to classify instances as normal or abnormal. Empirical results designate that SMA-based hybrid SqueezeNet-Inception V3 attained an accuracy of 97.3%, a specificity of 96.1%, and a sensitivity of 98%, thereby underscoring its efficacy in the detection of lung cancer.
Authors - Narayan Gupta, Pawan Kumar, Prince Kumar Singh, Priyabart Kumar, Parampreet Kaur Abstract - For investors, accurately predicting stock market prices is a critical part of financial analysis. This work studies a wide variety of machine learning algorithms specifically designed for the task of predicting stock prices using a variety of techniques and the latest technologies available as of the time of study. The study critically compares a suite of algorithms, including Support Vector Regression (SVR), Random Forests, Decision Tree models, and Long Short-Term Memory (LSTM), each with differing strengths. Moreover, it investigates a variety of approaches that are focused on understanding the complex connections found in the past price data. The dataset used for this study includes extremely long-term stock price representatives over a time range from 2010 to 2024 for Tata Consultancy services (TCS), containing a wide range of information, including opening and closing trading prices, trading volumes, and a variety of calculated indicators reflecting market behaviour. To understand the first experiment carried out for this study, it can be seen that in combination mode, for the best-performing model, Random Forest has the best interpretability. In addition, both Support Vector Regression (SVR) and Decision Tree algorithms deliver both impressive short-term prediction results and clear explanations behind their decisions.
Authors - Sandeep Shinde, Samarveer Moray, Aditya Sakhare, Prathamesh Salokhe, Kedar Sathe Abstract - The exponential growth of digital documents, particularly PDFs, presents significant challenges in efficient information retrieval and extraction. Traditional methods often struggle with the complexity and variability inherent in large PDF documents. Recent advancements in Natural Language Processing (NLP), especially Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), offer promising solutions. This paper presents a comprehensive system for efficient information extraction from large PDFs using RAG and LLMs. We propose a robust and scalable pipeline addressing challenges such as document segmentation, dynamic retrieval, and response contextualization. Through extensive experiments across multiple domains—including legal analysis, technical documentation, and scientific literature—we demonstrate that the proposed methodology significantly outperforms existing approaches in terms of accuracy, scalability, and efficiency. Our research lays the groundwork for integrating RAG and LLMs in various domains, offering a valuable tool for extracting knowledge from complex documents.
Authors - Rahul Pethe, Parag Puranik, Abhay Kasetwar Abstract - Wireless Sensor Networks (WSNs) have been designed and developed by numerous researchers over time, evolving based on emerging demands and environmental conditions. Over the years, various enhancements and improvements have been proposed, with multiple protocols introduced to address design challenges. In parallel, Mobile Ad Hoc Networks (MANETs) have witnessed tremendous growth in this wireless era. While many researchers have focused on protocols like DSR and hybrid approaches for energy-efficient clustering, these solutions have often proven to be time-bound and context-specific. To overcome these limitations and enhance the performance and lifetime of WSNs, we propose a new scheme based on the AODV (Ad hoc On-Demand Distance Vector) protocol within a mesh networking framework. Our approach achieves optimal results, including 100% throughput, minimal jitter, and significant improvements in network life-time.
Authors - Pranav Mittal, Prerna, Dhruv Bansal, Nikhil Panwar, Krish Tyagi Abstract - Stock market prediction is an intricate process in financial analysis, as its main aim is to predict the price trends and thus help to make trading strategies. But the stock market is so unpredictable with various factors affecting it, and thus predicting whether the value will rise or not becomes a difficult task. This study aims to predict the stock market prices with historical data that will be achieved via Deep Learning techniques in particular LSTM networks. Due to its strength in learning long-term dependencies and preserving the sequence information, LSTM (one of the variants of RNNs) is ideal for time-series data. Our approach leverages a dataset of daily stock prices from various financial indices over multiple years. The data is preprocessed using normalization techniques to improve model accuracy. The LSTM model is then compared to a traditional feed-forward neural network to demonstrate the superiority of LSTM in predicting shortterm stock trends. The models are optimized using the Adam optimizer, The results indicate that LSTM significantly outperforms the conventional models in forecasting accuracy. Moreover, this research introduced a hybrid LSTM-CNN method to extract features and prediction firmly. This study will contribute to financial forecasting by utilizing deep learning techniques and real trading scenarios. The research work was carried out using the programming language known as Python, deep learning tools such as TensorFlow and Keras, data management libraries such as Pandas and NumPy, and data representation software such as Matplotlib. It was found that the LSTM model has a much higher level of success when time series patterns are approximate than any other type of neural network, hence stock price predictions are more accurate. This study helps in how LSTM networks can be useful for forecasting in finance hence would be helpful to traders and market analysts.
Authors - Sheetal Phatangare, Komal Potdar, Yash Mahajan, Mandar Pandagale, Vivek Nikam Abstract - University students frequently encounter challenges in retrieving relevant academic information due to the limitations of traditional search engines. This research introduces KnowledgePilot, the first 1-bit Large Language Model (LLM) specifically designed to support university students. Leveraging the BitNet b1.58 architecture, which employs ternary parameterization (-1, 0, 1), KnowledgePilot achieves high performance with reduced computational costs, making it both resource-efficient and fast. The system integrates Retrieval Augmented Generation (RAG) pipelines, enabling it to access external academic data sources, thus minimizing hallucination issues common in LLMs and providing accurate, context-specific responses. The research also encompasses the development of tools for file conversion, dataset creation, model pretraining and fine tuning. Comprehensive evaluations will measure the system’s performance and user satisfaction, demonstrating its potential to significantly enhance student access to academic resources, while setting the stage for future advancements in low-bit AI technologies for education.
Authors - Yash Chavan, Arnav Sonawane, Arpit Pattiwar, Aditya Nagdive, Kaushalya Thopate Abstract - In today's fast-moving world, consumers rely on packaged foods. This is extremely important for easy access to detailed and personalized nutritional information. This project focuses on developing mobile applications for barcode scanning. This includes extensive food details, including ingredients, nutritional value, allergen warnings, and personalized consumption recommendations based on a person's health. Applications written with Python and Kivy provide a seamless user experience, allowing individuals to scan barcodes and upload images of ingredients to extract and analyze related information. Additionally, it includes optical character detection (OCR) using Tesseract, which extracts text from photos to allow users to analyze the ingredient list and nutritional name, even if barcode scans are not possible. By taking into account user nutritional limitations or illnesses such as diabetes, lactose intolerance, or gluten sensitivity, this application provides tailor-made health advice and helps individuals make found food decisions appropriately. A secure user authentication system improves the experience by storing your preferences and receiving recommendations created by tailors. The main goal of this project is to enable consumers to choose food in real time and promote healthier consumption habits. The combination of barcode scanning, OCR, and a structured database causes applications to close the gap between the complexity of food indicators and user understanding. Future improvements include mechanically learning-based ingredients, integration into real-time product databases, and expansion of several platforms beyond Android. This initiative represents an important step in using technology to improve consumer health awareness and ensure safer and sounder decisions for food consumption.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room BGOA, India
Authors - Evangeline R C, Krupa Nirmal, Laasya P, Aishwarya K Abstract - Real-time pedestrian trajectory prediction is essential for enhancing safety and urban mobility, particularly in dense and dynamic environments. This paper introduces a video data processing system that accurately predicts pedestrian movement by analyzing sequences of video frames in real time. The system effectively handles challenges such as overlapping individuals, partial occlusions, and diverse walking behaviors, making it suitable for real-world deployment. The architecture is designed to be both modular and scalable, allowing for seamless integration into various applications such as traffic management, urban planning, and pedestrian safety enhancement. A user-friendly interface provides real-time visualization of the predicted trajectories, enabling accessibility for both technical and non-technical stakeholders, including urban planners and public safety officials. Extensive experiments conducted on multiple diverse datasets demonstrate the system’s reliability and accuracy across various conditions, including crowded scenes and irregular pedestrian movement. The system successfully captures complex behavior patterns and provides predictive insights that can help reduce pedestrian-related accidents. This research contributes significantly to the field of intelligent pedestrian monitoring systems. By combining real-time responsiveness with accurate trajectory prediction, the proposed system supports the development of smarter and safer urban infrastructure, fostering proactive decision-making and improved pedestrian safety in modern cities.
Authors - Nisha Dubey, Randeep Singh Abstract - Facial profile classification and acknowledgment have different applications in security, perception, and identity affirmation. This paper proposes a novel approach utilizing Convolutional Neural Frameworks (CNNs) to classify and recognize facial profiles. The proposed system utilizes a significant CNN designing to remove solid highlights from facial profiles, taken after by a classification layer to recognize profile classes (e.g., cleared out, right, frontal). The illustrate is ready on a tremendous dataset of facial profiles and finishes tall accuracy in classification (95.2%) and affirmation (92.5%) errands. Test comes around outline the system's quality to assortments in lighting, pose, and expression. Besides, the proposed system outflanks existing techniques in facial profile classification and affirmation. This work contributes to the movement of facial examination development, engaging its course of action in real-world applications such as identity affirmation, get to control, and perception. In particular, facial profiles provide an interesting challenge due to the distinctive variety of lighting, attitude, expression and disorders. Furthermore, large data records and accessibility of arithmetic violations have made it possible to prepare violent .CNN models for facial profile classification and detection
Authors - Prranjali Jadhav, Varsha H Patil Abstract - Recommendation systems play a crucial role in personalized content delivery across various domains such as e-commerce, streaming platforms, and healthcare. This survey presents a comprehensive analysis of recommendation frameworks, emphasizing their architectures, methodologies, challenges, and future directions. The provided framework integrates hybrid models, deep learning, collaborative filtering, and content-based filtering, processed through a multi-layered architecture. The data processing layer handles preprocessing, feature extraction, and data collection, while the model selection layer chooses an appropriate recommendation technique. The recommendation engine ranks and scores predictions before delivering final recommendations. A critical component is the user feedback & continuous learning module, incorporating explicit and implicit feedback to dynamically update the model. Challenges such as scalability, data sparsity, and real-time adaptation are explored, along with emerging advancements like knowledge graphs and reinforcement learning. The paper highlights future research opportunities to enhance recommendation accuracy and user experience.
Authors - Amit Budhodkar, Rupali Umbare, Nihar Ranjan, Shubham Udgirkar, Sakshi Suryawanshi, Pradnya Aher Abstract - As mock interviews are essential for job interview preparation, the resources available cannot evaluate both technical and non-technical skills. This paper outlines the AI Mock Interview Platform which interfaces with learners and simulates truthful interviews by assessing non-technical competencies such as body language, confidence, emotional expression, and technical knowledge. The platform is capable of dynamically generating interview questions relevant to a candidate's particular role using AI technologies. In addition, AI technologies enable real-time feedback provision. With regard to feedback generation, the system utilizes AI technologies to consider specific features unique to the candidates’ voices, movement, and gaze direction. It utilizes Dlib’s human body posture detection library and video sentiment analysis for facial expression recognition with AffectNet dataset for Convolutional Neural Network faces as well as videos. With the Courses feature, learners can focus on varied topics and the platform automatically selects suitable instructional content consistent with the candidates’ preferred method of learning
Authors - SHALINI S, C NANDINI, LAKSHMI MR, KOUSTAV BISWAS, L DIVYASHREE, MITAYI AJAY KUMAR, MONIKA V Abstract - This research work uses Artificial Intelligence for early detection and targeted intervention in the case of Autism Spectrum Disorder (ASD). Through sophisticated language analysis and pattern identification of interactions, the platform detects signs of autism to facilitate early intervention. Relying on these findings, the platform tailors developmental programs in pivotal areas of communication, daily living, and adaptive learning through fun, interactive modules. An integrated chatbot powered by AI improves user experience through conversational assistance, responding to questions, and assisting individuals with autism, as well as their caregivers. Ongoing interaction develops a greater familiarity with the resources available on the platform and encourages active involvement in skill development exercises. Structured with users from every age group, the platform places strong emphasis on ethical use of AI and protecting data, offering a secure and reliable environment. Through its fit to the singular developmental path of each user, it fosters autonomy, skills development, and social integration. The platform is an integrated system of care and empowerment for the autism community. It seeks to respond to the broad range of individual needs on a universal, adaptable, and empathetic level, facilitating personal development and increased autonomy for individuals on the spectrum.
Authors - Abhay Pratap Singh, Aanya Mittal, Ashmit Tyagi, Kanan Agrawal, Avdhesh Gupta Abstract - Communication is one of the major attributes of human life[1]. The system discussed in this research paper focuses on developing a novel and efficient way of communicating between a deaf-mute person and any other person who is normal (does not have deaf or dumb handicaps). Advanced technologies used in the design will support the conversion from voice to Indian Sign Language using Natural Language Processing Machine learning algorithms and computer vision techniques and vice versa. It translates audio messages into sign language images with text in real-time, trying to basically eliminate the conventional dependency on interpreters as a means of communication for every person. The main idea of the research is the solution of urgent problems connected with communication of the deaf-mute people and, at the same time, to be able to solve this task with the use of modern technologies, keeping in mind the principles of inclusiveness and independence. The design, implementation, and potential of the system to improve the living standards of deaf-mute people by bringing them closer to society are discussed. The aim is to plead for inclusion by raising the awareness of educators, policymakers, and the public at large regarding the demand for communication resources addressed specifically to the deaf and mute community. The consciousness of the demand for ISL interpreters and the promotion of video datasets will be helpful in bridging the gap in communication, as seen in the research on the lack of certified ISL interpreters and the demand for automated sign recognition systems.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room CGOA, India
Authors - Debabala Swain, Monalisa Swain, Sharmistha Roy, Debabrata Swain, Jayanta Mondal, Prachee Dewangan Abstract - The information stored or transmitted digitally is vulnerable to unauthorized access. The authentication of digital images is a critical issue in the era of digital advancements, given the ease with which any image can be altered. Consequently, methods for verifying the credibility of images are gaining widespread recognition due to their relevance in various societal domains, such as government, military, forensics, and electronic commerce. The significance of protecting images from manipulation has escalated, recognizing that even a minor tampering incident could lead to severe consequences. Hence, safeguarding images from alterations has become increasingly essential. Literature has seen the development of numerous approaches to ensure the genuineness and integrity of digital images. This study offers a comprehensive overview of both domain-based and AI-based watermark techniques for authenticating images, providing the capability to detect tampering and pinpoint the specific manipulated areas within an image.
Authors - Pooja Singh Chaudhary, Nirav Bhatt, Purvi Prajapati Abstract - Object Detection and the Object Pursuit are the fundamental and the emerging tasks in the Machine learning and in the computer vision to detect the object and then too track the object in all the real and the dynamic environments. The latest trends which are emerged in this area, highlighting the embedding of deep learning techniques has transformed the field of object detection and tracking. Methods like Convolutional Neural Networks, Deep SORT, You Only Look Once and Region-Based Convolutional Neural Networks have significantly improved accuracy and efficiency. We examine the shift towards more robust and measurable and the scalable solutions, with particular focus on multi-object tracking, real-time processing, and handling challenging Challenges like occlusion, variations in scale, and varying in illumination. The survey also addresses key challenges that remain, including computational efficiency, accuracy in complex scenarios, and the development of algorithms. Furthermore, we discuss the applications of object detection and pursuit across industries like autonomous driving, robotics, surveillance, and augmented reality, while offering insights into future research directions that may overcome existing limitations and drive the field forward. These recent advancements, combined with the evolution of tracking algorithms, have made it possible to detect and track objects in real-time with high precision.
Authors - Akhil K J, Saurabh Shrivastava, Harish R Abstract - The increasing concerns over online privacy and the growing prevalence of internet censorship have driven many users to seek greater anonymity through tools like proxies and virtual private networks (VPNs). While peer-to-peer (P2P) networks provide a decentralized way for users to communicate securely across multiple nodes, they are not immune to security threats. One of the major vulnerabilities in P2P networks is the risk of man-in-the-middle (MITM) attacks, where malicious actors intercept communication between nodes. In these attacks, attackers can manipulate, inject, or even remove data being transmitted, compromising the integrity of the information. Another rising threat within these networks is cryptojacking—a tactic where attackers surreptitiously use a website’s resources to mine cryptocurrency, often without the knowledge or consent of the website visitors. This malicious practice has gained attention due to its increasing prevalence on popular sites. In the context of P2P networks, the exploitation of exit nodes poses a significant risk, as attackers can inject mining scripts into the HTTP responses sent from these nodes. These risks highlight the need for robust security protocols to safeguard decentralized networks and prevent malicious interference, ensuring the security, privacy, and integrity of online communication systems. Effective measures are vital to protecting users and maintaining trust in these technologies.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room CGOA, India
Authors - Kamini Solanki, Nilay Vaidya, Jaimin Undavia, Krishna Kant, Jay Panchal, Anjali Mahavar Abstract - The rapid growth of internet-based applications, such as social media platforms and blogs, has led to an increase in comments and reviews about everyday activities. Sentiment analysis involves collecting and analysing people's opinions, thoughts, and perceptions on various topics, products, services, and subjects. These opinions can provide valuable insights for businesses, governments, and individuals in making informed decisions. However, the process of sentiment analysis faces several challenges that make it difficult to accurately interpret sentiments and determine the correct sentiment polarity. Sentiment analysis extracts subjective information from text using natural language processing (NLP) and text mining techniques. This article provides an in-depth overview of the methods used to perform sentiment analysis, along with its applications. It also evaluates and compares different approaches, discussing their advantages and limitations. Finally, the article examines the challenges in sentiment analysis and proposes future directions for the field. Sentiment analysis, also referred to as opinion mining, is a vital area of research in natural language processing (NLP) that focuses on identifying the sentiment expressed in text. This paper reviews various sentiment analysis techniques, explores its broad range of applications, and discusses the challenges within the field. The goal is to provide a thorough understanding of the current state of sentiment analysis and its potential future developments.
Authors - Botcha Divya, Yelavarti Kalyan Chakravarti, V. Esther Jyothi, A. Satya Kranthi Abstract - Software-Defined Networking (SDN) has transformed contemporary network topology by separating the control plane from the data plane, allowing the network to be centrally and dynamically managed. Its central design, however, also presents enormous security threats that must be mitigated using efficient Intrusion Detection Systems (IDS). This paper proposes an intelligent IDS framework for SDN networks utilizing machine learning algorithms. The proposed method employs the UNSW-NB15 dataset, preprocessing with advanced methods, SMOTE-Tomek resampling, and multi-class classification by XGBoost for attack detection and classification of different attacks. Interactive Streamlit-based dashboards and packet simulation allow real-time observation, filtering of attacks, and visualization of anomalies in detail. Experimental results demonstrate enhanced detection accuracy of 84% using the top 20 features selected that outperform conventional classifiers in precision and responsiveness. The addition of real-time prediction counters, attack distribution graphs, and downloading capability allows for tremendous flexibility when used in live SDN contexts. The project tries to minimize the theoretical/practical implementation gap found among existing IDS models and live deployments with its suggested scalable, interpretable, and effective intrusion detection solution.
Authors - Deepesh Sudhan Arunachalam, Dennis Andrew, K. S. Gayathri, A. Shahina, V. Durgadevi, A. Saravanan Abstract - This work introduces a deep learning-based framework for 3D pressure mapping to assess sleep quality and body strain. 2D pressure maps suffer from loss of depth information, poor spatial context, posture misclassification errors, and limited accuracy in capturing regional pressure variations. To overcome these limitations, the framework constructs 3D pressure maps that enable precise region-wise pressure estimation with anatomical landmarks to analyze body strain. Sleep quality is monitored by tracking frequent posture changes with converting pressure maps to point clouds achieved 99.26% accuracy with PointNet and 99.49% with PointCNN.
Authors - Gunjan Tewari, Divyanshi Verma, Simran Negi, Richa Jain Abstract - Cloud computing has completely revolutionized the way of data storage and management in a scalable environment with cost efficiency and scalability. However, the shift from physical to cloud infrastructure raises significant concerns related to security. The major security issues in cloud storage are: data breaches, unauthorized access, hacking of data, and insider threats, various types of cyber-attacks, etc. Many advancements have been made in cloud security but despite that several challenges still exist. Many security frameworks rely on third-party providers creating potential risks of data exposure. This paper addresses these challenges by proposing an approach for secure file storage in the cloud having multiple layers of security. The key derivation for encryption is done uniquely and then AES-256 is used for encryption and decryption. At the time of decryption, OTP authentication is done using RSA signing which provides multi-factor authentication. The method can also be used to encrypt all multimedia data. To provide security, the file password and OTP information are not stored in any database. The proposed work provides a very secure data storage solution that protects the data from any kind of brute-force attacks and other cryptanalysis attacks.
Authors - Jalindar Nivrutti Ekatpure, Dinesh Bhagwan Hanchate Abstract - This review paper gives a thorough look at all the current methods and uses of AI in crop prediction with aerial images. With the development of drone technology and high-resolution satellite imagery, gathering data on farming has been easier. This paper completely analyses the use full uses of Artificial Intelligence techniques in agricultural functions. The real-word ex-ample shows that how artificial intelligence techniques used in aerial imagery technology it may be accurately applied in different agricultural fields. These examples shows capability to develop observing, expect yields, and assist farmers with correct decisions. The next research enterprises have been suggested to address current difficulties and increasing artificial intelligence application in the crop cultivation techniques. This paper aims to train farmers experts, educators, and those who are to know how to use artificial intelligence in the precision farming.
Authors - Rohit Bajirao Khedkar, Bhakti Dudile, Laksh Rupesh Khobragade, Pawan Babanrao Avhad, Santosh Kumar Abstract - This paper presents a comprehensive study on the development and implementation of a virtual mouse system using hand gestures. With the rapid advancement of human-computer interaction (HCI) technologies, touchless interfaces have gained immense popularity. The proposed system eliminates the need for physical input devices by leveraging computer vision and machine learning techniques to interpret hand movements, translating them into cursor control and command execution. The research explores various methodologies, including hand tracking, gesture recognition, system integration, and deployment strategies, highlighting advancements, challenges, and future directions in the field. The objective of this study is to design an intuitive user interface, develop a robust gesture recognition model, and ensure seamless deployment across various platforms to enhance accessibility and usability.
Authors - Mariela Todorova, Tihomir Dovramadjiev, Darina Dobreva, Tsena Murzova, Mariana Murzova, Iliya Iliev, Ventsislav Markov Abstract - The pursuit of enhancing the quality and precision of final design models has become increasingly vital in modern production processes, particularly in the realm of university information signage. This research explores the application of advanced laser engraving and cutting technologies to achieve maximum accuracy in geometric shapes, fine details, and textual elements. A systematic methodology has been developed and implemented, focusing on the production of custom-designed metal information signs tailored to university environments. The article presents a comprehensive overview of the production stages, including optimized workflows for managing digital data, selection of appropriate file formats, and precise laser machine settings. By integrating digital design tools with laser technology, the study demonstrates how to streamline processes while maintaining exceptional quality and durability of signage. The research outcomes not only emphasize the technological advantages of laser systems—such as high-speed production, cost efficiency, and unparalleled precision— but also highlight their potential to transform design practices in educational settings. This study aims to contribute to the scientific and practical development of digital fabrication methods, inspiring wider adoption of laser-based innovations across design disciplines.
Authors - Devashish Sanjay Gaikwad, Aadit Kisanrao Palande, Prasad Padmakar Joshi, Aditya Atul Kode, Rachana Yogesh Patil Abstract - Assessing the solar potential of rooftops is crucial for optimizing photovoltaic (PV) installations and promoting renewable energy adoption. This study presents a methodology for estimating rooftop solar potential using advanced geospatial and machine learning techniques. The proposed framework integrates Mapbox GL for spatial visualization, PVGIS for solar radiation data, and Scikit-Learn for predictive modeling. A web-based application is developed using React.js, HTML, and TailwindCSS for the frontend, with Node.js and Express.js handling backend processes. The system allows users to input rooftop data, analyze solar potential, and generate estimations of energy output based on historical and real-time solar radiation data. By leveraging machine learning algorithms, the model enhances prediction accuracy and enables better decision-making for solar energy investments. The results demonstrate the feasibility and effectiveness of this approach in providing precise and user-friendly solar potential assessments. This research contributes to the growing field of smart energy solutions and supports the transition to sustainable energy sources.
Authors - Shaveta Thakral, JyotiVerma, Suchita Ganage, Dharmendra Ganage, Monika, Shankar Amalraj Abstract - Cognitive Radio (CR) is a revolutionary technology aimed at optimizing the utilization of the electromagnetic spectrum, a limited and valuable resource. Despite its promising potential, the deployment and widespread adoption of CR face several technical, regulatory, and practical challenges. This paper presents a comprehensive study of the key research challenges in Cognitive Radio Networks (CRNs). We begin with a chronological literature survey, highlighting significant advancements and ongoing research efforts. Subsequently, we delve into the current research challenges, including spectrum sensing, dynamic spectrum access, security, energy efficiency, interoperability, and regulatory issues. We also explore potential research opportunities that could address these challenges, thereby paving the way for more robust and efficient CRNs. This review aims to serve as a foundational reference for researchers and practitioners in the field, offering insights into future research directions.
Authors - Sheela Chinchmalatpure, Atharva Bondarde, Atharva Joshi, Archit Bagad, Samyak Dawle, Rajeshwar Chintawar Abstract - Proper waste management is essential for public health and urban sanitation. Conventional garbage collection systems tend to be absent of verification checks to confirm the emptying of waste bins and instead depend on manual records. To improve monitoring of waste collection, this research suggests a smart, technology-based solution that combines Near Field Communication (NFC) with computer vision through a TensorFlow Lite-based YOLO model. The system includes a mobile app that scans NFC tags on trash bins, logging the time and staff member who serviced the bin. The app also includes a lightweight YOLO model in TensorFlow Lite format to check if a bin is full or not using real-time image processing. NFC scanning is only allowed after successful model verification to avoid fraudulent reporting and ensure accountability of sanitation workers. This two-in-one system serves as both a real-time waste collection monitoring system and an automated worker attendance tracking system. Evaluated on a bin image dataset, the solution showed encouraging accuracy in empty and full bin detection. Through the use of AI and IoT-based tracking, this system promotes accountability, transparency, and effectiveness in waste collection, and makes it a scalable and affordable model for smart cities.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room DGOA, India
Authors - Vishal V. Mahale, Sanket R. Malode, Sudarshan M. Pagare, Punit Chaudhari Abstract - Personality prediction plays a key role in understanding human behavior, decision-making, and social interactions. The OCEAN model—comprising Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism is widely used for assessing personality traits. With the rise of machine learning, predicting personality using this model has become a growing interdisciplinary field. This survey paper reviews existing machine learning approaches, such as K-Means and Gaussian Mixture Models, used to analyze personality traits from questionnaire data. It also highlights the limitations of past studies, including lower prediction accuracy and challenges in model interpretation. The aim is to provide a clear overview of current methods and explore how machine learning can improve personality prediction and reveal deeper links between personality traits and behavior.
Authors - Poonkuzhali S, Shreya Sai Prabakar, Sriram Venkat P Abstract - The airline industry is characterized by fluctuating demand, intricate pricing, and inventory management. In that regard, this study is done regarding AI-driven revenue management systems, where many authors engage quite heavily with techniques of demand forecasting, pricing optimization, and inventory control. With Random Forest Regression, Catboost and LightGBM passenger demand can be predicted using fare class, lead time, and seasonality, optimizes seat allocation by fare category so as to maximize revenue. Evaluation is done using several datasets regarding booking patterns and market behavior in order to critically assess the accuracy, efficiency, and adaptability of the model. This study will demonstrate the strengths and trade-offs of AI techniques, indicating to the airline the power of using data for real- time decisions. Its strength lies in the improvement of demand forecasting combined with dynamic pricing and inventory management, maximized profitability, efficiency, and customer satisfaction.
Authors - Vijayalakshmi B, Jayasheela C S Abstract - Postmenopausal women (PW) are at a significantly increased risk of fractures, largely due to estrogen deficiency leading to osteoporosis and altered bone quality. Fracture risk assessment is critical for early intervention and prevention strategies. This review explores advancements in fracture risk evaluation, highlighting experimental methodologies, clinical applications and emerging technologies. Key advanced imaging approaches include the use of dual-energy X-ray absorptiometry (DEXA) for bone mineral density (BMD) measurement, high-resolution peripheral quantitative computed tomography (HR-pQCT) for micro-architectural assessment and biochemical markers like C-terminal telopeptide (CTX) and procollagen type I N-terminal propeptide (PINP) for monitoring bone mass density. Fracture risk assessment (FRA) tools such as FRAX and the Garvan calculator provide practical frameworks for estimating fracture probability by integrating clinical risk factors and BMD data. However, challenges remain. including limited access to advanced imaging, variability in biochemical marker and under representation of diverse populations in validation studies. Future directions emphasize integrating artificial intelligence, expanding population specific validations and combining imaging with dynamic bone mass density data. This comprehensive review emphasizes the significance of a multidisciplinary approach in FRA, aiming to enhance precision, accessibility and clinical outcomes in postmenopausal women.
Authors - Arokiaraj S, Amudha T, Swamynathan R Abstract - Technology has become the driving force of progress and development all around the world. The recent development of Generative Artificial Intelligence has led to a revolution in the field of Education, Employment and Human Resources. Securing a dream job or building a suitable career is the goal of every student. Likewise finding the right candidate for the job is the goal of every employer. LLMs (Large Language Model) comes to the rescue, through dynamic question content creation for a selected topic and test the candidate for that set of skills. A candidate’s unique set of skills and abilities are understood and tested by the Generative AI where conventional methods fall short to meet this criterion. This paper proposes an automated question generation framework built using LangChain, LLM model -GPT Turbo 3.5 from OpenAI API and Streamlit application development tool. This application successfully tests the skills of the candidates by asking customized multiple-choice, true/false and open-ended questions based on their chosen topic, knowledge level, number of questions and time limit. Results indicate that this framework can create a challenging environment for the contenders thereby facilitating the interview process and selection of highly suitable candidates.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room EGOA, India
Authors - Shailaja Uke, Mohit Garg, Suyash Chandolikar, Swayam Chandak, Shriraj Nelekar Abstract - Chest X-rays are the most common tool for diagnosing various thoracic diseases. However, manual interpretation is time-consuming and prone to human error. This paper presents a deep learning approach for automated pathology detection in CXRs using the Customized DenseNet-121 model. The model performs binary classification to identify 14 pathologies, including cardiomegaly, pneumothorax, mass, and edema. To address class imbalance in medical imaging datasets, weight normalization is applied. Additionally, the visualization technique of Grad-CAM enhances interpretability by pointing out the most critical regions influencing the model's decisions, which helps healthcare practitioners assess. Toward further refinement of segmentation and improvement in precision of localization, we incorporate a customized U-Net model to enhance better delineation of regions of interest. Our model achieves an overall AUC of 87%, showing the highest accuracy. The customized U-Net integration improves seg-mentation performance, reducing localization error by 15%. This approach not only enhances diagnostic accuracy but also provides transparent decision-making, making it a valuable tool for medical professionals.
Authors - Aditya Gaura, Mandeep Kaur, Kimmi Verma, Monali Gulhane, Nitin Rakesh Abstract - This cutting-edge research paper introduces a paradigm shift in parking management, underpinned by an intricate network of technology and user-centric design. The system's hallmark feature is its advanced slot allocation mechanism. Users can make reservations via the mobile or web application, with the system autonomously assigning slots based on a holistic evaluation of user characteristics, such as vehicle type and duration of stay. Leveraging IoT integration, the system employs a sophisticated array of sensors and cameras to monitor parking slot occupancy in real-time, resulting in a fluid entry and exit process. The user experience is paramount in this system. It offers a tailored approach based on user type, streamlining the process for faculty, students, and visitors. Notably, the reduction in the time spent hunting for parking spots has the potential to mitigate the perennial issue of urban traffic congestion. This, in turn, aligns with environmental conservation efforts, as the system indirectly lowers emissions and the carbon footprint associated with circling for parking spaces. Moreover, this system's role as a data aggregator is invaluable. It collects and processes a wealth of data, offering parking operators unprecedented insights into daily usage patterns, peak periods, and favored slots. This data-driven approach empowers operators to make informed decisions about slot management, maintenance, and resource allocation.
Authors - Balwinder Kaur, Jaswinder Singh, Deepika Abstract - Nature-inspired optimization algorithms constitute a class of computational techniques that derive their underlying mechanisms from biological, ecological, and physical systems. By emulating processes such as evolutionary adaptation, collective swarm behavior, and decentralized decision-making, these algorithms offer robust solutions to complex optimization challenges across engineering and computational domains. Notable methodologies include Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization, each demonstrating efficacy in handling both single and multi-objective optimization problems, including those involving high-dimensional search spaces and non-linear constraints. Within the field of Automatic Speech Recognition (ASR), nature-inspired optimization techniques are instrumental in refining critical system components. Their application spans feature selection, acoustic model training, language model optimization, and efficient decoding strategies. By leveraging adaptive search mechanisms, these algorithms enhance model accuracy, reduce computational overhead, and improve generalization in ASR systems. This research study presents a systematic examination of nature-inspired optimization methods, focusing on their theoretical foundations and practical implementations in ASR. Furthermore, it critically evaluates existing challenges, such as sensitivity to hyperparameter tuning, computational scalability with large-scale datasets, and the absence of comprehensive convergence guarantees. Addressing these limitations is essential for advancing the applicability of nature-inspired optimization in next-generation speech recognition systems and related domains.
Authors - Anchal Saini, Nitin Kulshrestha Abstract - In the rapidly digitizing financial landscape, the ability to effectively use digital tools has become essential for financial well-being. This study examines the impact of digital competence on financial resilience within dual-income married couples, adopting a dyadic perspective. Drawing on the Actor-Partner Interdependence Moderation Model(APIMoM), it studies both actor and partner effects of digital competence. As well as the moderating role of each partner’s attitude toward FinTech. Data was collected from 107(214 individuals) working couples in Gurgaon, India. Covariance-Based Structural Equation Modeling using SmartPLS revealed that digital competence significantly influences both individuals' and their partners’ financial resilience. Moreover, attitudes toward FinTech were found to moderate these relationships, strengthening the positive effects of digital competence. Notably, the husband’s attitude had a stronger moderating impact on the wife’s resilience than vice versa, indicating potential gender-based dynamics. The study marks the importance of addressing both digital skills and relational attributes in aiding household financial resilience. Practical implications suggest that digital literacy programs should consider couple-based interventions that target both digital competence and attitude change.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room EGOA, India
Authors - Rasika Ransing, Kaushik Sakre, Neha Kudu, Shivam Shinde, Siddhi Talkar Abstract - The introduction of Automated Essay Scoring systems brought better assessment methods into education through standardized scoring systems that operate at scale while being time efficient. The current AES models function exclusively with English content while neglecting multilingual evaluation, particularly in the Hindi and Marathi languages. A multilingual AES framework has been developed using transformer models XLM-RoBERTa, MuRIL, DistilBERT, and mBERT for conducting context-based essay assessments throughout English, Hindi, and Marathi texts. Through multilingual embeddings combined with fine-tuned models, the system maintains cohesive and coherent, and argumentative quality in essays. The assessment by QWK and RMSE metrics demonstrates both high accuracy and reliability of the system. The highest performance emerged from XLM-RoBERTa and Google MuRIL at 0.78 QWK and 0.77 QWK, respectively.
Authors - Sarvesh Shinde, Tarun Kurakula, Venugopal Murugan, Tanmay Patil, Aparna Bannore Abstract - Network security is a difficult topic these days, with threats appearing quickly and everywhere. According to the study "Network Security Using Graph Embedding," connections are visible when jumbled network data is transformed into transparent graphs. First-order graphs have direct node links, while second-order graphs have nodes that share neighbours. DeepWalk, Node2Vec, and sense-making tools. Node2Vec, choice-based, tight groups or large network view, and modified random walks. DeepWalk is a straightforward, sequential structure mapping method. Both embeddings are feasible in terms of network layout. A graph as opposed to the outdated equal-link techniques, Attention Network, GAT, and anomaly hunt use attention tricks for important connections. fresh activity in the dataset, labeled data, normal versus odd markers, and real-time data. Strange spikes, unusual nodes, rules established, and threats identified. In continuous networks, not data crunch for kicks, quick catch, hackers, or weak spots.
Authors - Atharva Shirbhate, Jay Sutar, Om Tathed, Bhupal Shelke, Geeta S. Navale Abstract - The development of Artificial Intelligence (AI) has led to significant advancements across numerous domains, including finance, healthcare, and customer service. Recent progress, particularly in the field of Natural Language Processing (NLP), has been driven by the emergence of Large Language Models (LLMs). These models utilize transformer architectures and vast datasets to perform a wide range of tasks, such as language translation, text generation, and complex data analysis. As AI technology continues to evolve, it offers the potential to streamline decision-making processes, enhance data management, and provide personalized recommendations. This study focuses on leveraging AI to address specific challenges in the financial sector. The objective of this paper is twofold: firstly, to develop a system capable of recommending bonds based on user-specific requirements, thus aiding investors in making informed decisions; and secondly, to collect bond data from sellers and integrate it seamlessly into an existing database. Through a systematic review of recent AI advancements and prompt engineering techniques, this paper aims to provide insights into how these technologies can be harnessed to improve financial data integration and recommendation systems
Authors - Kumkum Saxena, Ayesha Nagdawala, Esha Nemani, Jatin Mawa, Mamta Gupta Abstract - Even in the modern days of digital era, reporting crimes like those of corruption or misconduct is still difficult owing to the fear of retaliation. Some traditional reporting mechanisms are available, but they often do not allow enough anonymity or security, preventing tipsters from reporting. Many whistleblowers face serious consequences, including job loss, legal action, or even physical threats, making them reluctant to report wrongdoing. SHADE is a blockchain-based solution that seeks to overcome these challenges by providing a decentralized and tamper-resistant medium for anonymous tip-offs. SHADE stands apart from conventional systems, which store centralized databases vulnerable to breach, providing full anonymity and data integrity with encryption. This paper explores SHADE’s architecture, which integrates blockchain for immutable data storage, cryptographic encryption for secure communication, and smart contracts for automated processing.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room EGOA, India
Authors - Suhas Bhise, Ketki Kshirsagar, Vivek Deshpande Abstract - In the context of Industrial Edge Computing, the growing deployment of IoT and mobile devices has resulted in an explosion of real-time, high-velocity time series data. This paper investigates statistical and machine learning approaches for time series forecasting in such environments, where latency, bandwidth, and computational efficiency are critical constraints. We evaluate traditional methods like Simple Moving Average (SMA), Holt-Winters Exponential Smoothing, and ARIMA, and contrast them with machine learning models such as Logistic Regression and XGBoost. Experiments conducted on the Microsoft Azure Predictive Maintenance dataset demonstrate that SMA and ARIMA offer comparable baseline accuracy, while XGBoost outperforms them in terms of forecast quality for multivariate series. We also explore the effectiveness of SMOTE for improving failure prediction using logistic regression. The findings suggest that lightweight models like XGBoost with lag feature engineering can be viable for forecasting in edge environments.
Assistant Professor, Department of Computer Science and Engineering (Artificial Intelligence & Machine Learning), Vishwakarma Institute of Technology, Pune, India
Thursday August 27, 2026 2:30pm - 2:32pm IST Virtual Room CGOA, India
Associate Professor and Head, Department of Artificial Intelligence and Data Science, Vidyavardhini's College of Engineering and Technology, Maharashtra, India
Thursday August 27, 2026 3:28pm - 3:30pm IST Virtual Room FGOA, India
Authors - SnehalBalasaheb Salve, Harsha Bhute Abstract - Developing a precise and strong model for forecasting energy utilization is importantaim for the management and functionality of smart buildings. The previous studies have researched different models for forecasting various load prediction schemes. The combined effects regarding data enrichment and machine learning approach in energy predictions have not been fully examined. This research proposes a novel approach, an ensemble model enhanced by generative adversarial networks (GANs) for predicting the usage of energy in big buildings that are commercial. This combined system integrates various single models using ensemble method with stacking. Furthermore, a GAN is utilized to capture the distribution of samples from the main dataset, generating top-notch specimens to augment the dataset from the training data. This expanded dataset allows the model to train with a wider range of samples, increasing its resilience. The experimental series evaluate the method that is proposed, using three variants of GAN and assessing performance with metrics such as mean absolute error, root mean square error, and coefficient of variation of root mean square error. This proposed approach demonstrates practical results that develop a model for power utilization prediction in application of real world.
Authors - G Roopa, H.L.Suresh Abstract - The work outlined here provides a new method to handle fault of Power Electronic Traction Transformer (PETT) switch using reverse charging Cascaded H Bridge (CHB) and Dual Active Bridge (DAB) topologies. Precisely, the main purpose is to improve the speed of detection, determining the location, and recovery of faults from the existing system using feature extraction and Machine Learning algorithms. The traditional approaches in achieving fault tolerance are defective in detecting faults in good time, isolating faults inadequately, and using backup hardware. In order to solve these problems, the proposed methodology actively reassigns control signals to backup modules resulting in the exclusion of faulty elements while preserving a stable system performance with moderate loss in efficiency. The feasibility of the suggested approach is confirmed through simulation outcomes for fault detection precision, which is increased to 98 percent; the fault localization time of at most 5-10 ms; and system throughput of 5-8 percent. Furthermore, the work investigates how CHB and DAB function in fault conditions and enshrine a novel reverse charging method for maintaining the DC voltage of the redundant module. The startup process of the PETT system is also managed with optimization of voltage and transient, which leads to enhance the general system initialization. Besides increasing the dependability and fault tolerance of PETT systems, the above methodology also reduces the system’s embedded hardware duplication and elevates system performance and scalability , which consequently leads to the decrease of the total system cost by 15 percent. These results point out that the proposed solution has potential for the development of the next generation of fault-tolerant power electronic systems.
Authors - Santushti Betgeri, Rohit Rathod, Sakshi Rathod, Sanskar Raut, Anisha Sadanshiv Abstract - AUTOHUB is an integrated solution for essential services regarding vehicles, as well as vehicle expenses. This is a solution with both a native Android app and the response web interface that can easily integrate, especially with dynamic time slot selection for service bookings and an extensive expense tracker for fuel, repairs, tolls/fines, and others. It also has a cloud Online Document Manager for safe document storage, automatic expiration reminders, different dashboards for service providers, and more. It is developed using Android Studio, React, Node.js with Express, Firebase Firestore for real-time data sync, and Razorpay for secure payment processing. The platform has a modular microservices architecture and is scalable and easy to maintain. This integrated solution not just tackles the current challenges posed by fragmented automotive service management, but it also builds the base upon which future extensions can be realized, placing AUTOHUB well ahead of its time as a platform for automotive care.
Authors - Ashwini Matange, Harsha Talele, Pratik Nagare, Vineet Morankar, Aniket Gavkare, Moin Shaikh Abstract - Accurate segmentation and prognostication of brain tumors are critical for effective diagnosis, treatment planning, and patient management in glioma. In this work, we present a unified framework built upon the BRATS2020 challenge data that integrates deep learning-based segmentation with radiomics and machine learning for overall survival prediction. First, we employ a 3D-UNet architecture to perform robust segmentation of brain tumors from multi-modal MRI scans, achieving a mean Intersection over Union (IOU) of 86%. This segmentation not only delineates tumor sub-regions effectively but also provides the basis for subsequent feature extraction. Leveraging the pre-trained 3D-UNet, we extract deep features from the MRI scans, and in parallel, perform radiomics feature extraction on the corresponding tumor masks. These features are then combined with clinical and demographic data provided in the BRATS2020 challenge dataset. A random forest classifier is subsequently trained on this comprehensive feature set to predict overall patient survival, achieving a classification accuracy of 70% in stratifying patients into survival categories. Our approach builds on recent advances in brain tumor segmentation—incorporating ideas such as ensemble learning, multi-modal imaging, and uncertainty quantification—to enhance both the segmentation accuracy and prognostication performance. The promising results demonstrate that the integration of deep learning segmentation with radiomics and traditional machine learning methods can serve as a robust tool for personalized treatment planning and risk stratification in glioma patients.
Authors - Vaishnavi Moorthy, Jagadeesan Moorthy, Shubhradip Saha, Anshuman Kumar Abstract - In the medical field, the readability of important information on medicine packs, like the expiry date, is of prime importance for maintaining patient safety. Yet, a number of reasons like damage, blurring, and printing defects may hide this important information on medicine strips. To solve this problem, we suggest a deep learning-based solution for medicine strip denoising and enhancement, making important information such as expiry dates more legible. Our approach utilizes image denoising, specifically designed to correct blurry or partially readable expiry dates on the packaging of medicines. This solution not only helps healthcare workers and patients validate medicines but also makes a contribution to the pharmaceutical industry.
Authors - Shweta Kumar, Saru Dhir, Ashish Kumar Mourya Abstract - Healthcare informatics has many difficulties due to the complexity of varied medical data. Clinical notes, imaging, and genomic data are instances of unstructured data that is more flexible and has more depth than organized data, such as digital records, which are easier to use. Combining different healthcare data sources is difficult due to interoperability issues and semantic variability. Despite the emergence of standardization projects such as HL7 (Health Level 7) FHIR (Fast Healthcare Interoperability Resources) and SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms), inefficient processes and unreliable vocabulary continue to impede seamless communication of information. The dispensation of natural language, or NLP (Natural language processing), methods enable the extraction of important information from uncontrolled health information. Furthermore, instantaneous data analysis and scalability are enhanced by online computing, and blockchain technology is being investigated as a safe, independent method of sharing medical data. This study examines the challenges of managing a variety of healthcare information as well as the possible benefits of contemporary technologies. Future research focuses on improving interoperability frameworks, developing AI-driven data analysis, and ensuring confidentiality and security of data in order to provide effective and data-driven healthcare options.
Authors - Arun N, Rithika J Prabhu, DHANYA M Abstract - Sustainability in India has been become a driving force behind the growth of the green energy sector and the economy's transition to cleaner energy. The research paper investigates the use of machine learning models to predict stock prices of green energy companies in India. It deliberates on the rapid growth of the green energy market and the potential for ever-advancing technologies making accurate prediction in finance for supporting the nation's sustainable development goals. Using machine learning, it generates useful insight for stock performance for the benefit of investors and policy makers in arriving at decisions.
Authors - M. Chaitanya Raju, Maddu Reshma, V. Anvesh, Lekha S. Nair Abstract - Waste classification and management are important for healthier planet Earth. In this paper we are proposing an integrated approach for waste detection and classification using object detection along with natural language processing (NLP) techniques. which introduce a YOLO-based model to detect and classify waste in images by using Bootstrap Language-Image Pretraining (BLIP) for scene understanding and contextual analysis. The workflow involves, feeding the waste images into a preprocessing stage (image), captioning image data with Natural Language Processing (NLP) to produce descriptive captions, and analyzing the textual features of detected captions that exist in the waste (waste elements). The classification of the detected object is performed by a custom trained YOLOv8 model which is fine-tuned on a specific waste class dataset. Experiments show that the model recognizes garbage, recyclables and litter with high accuracy. This system showcases the potential of combining visual and textual modalities to enhance waste detection accuracy, offering a robust tool for automated environmental monitoring and management.
Authors - Juttiga Rohita, B Teja Sree, Ibrapatnam Anusha, Mohammad Sharmila Begum, Nirjogi Mahathi Abstract - Social media platforms have become increasingly vulnerable to online threats, making safeguarding the internet an increasingly difficult task. Why? This project showcases an artificial intelligence-powered system that can detect and filter out inappropriate text and images in real-time. Machine learning and natural language processing (NLP) are utilized by the system to detect hate speech, toxic terminology such as slang, and explicit imagery while maintaining document integrity. TF-IDF, LSA, and Word Embeddings are utilized in text filtering to improve the understanding of context. In image filtering, deep learning models using convolutional neural networks (CNNs) and pre-trained NSFW classifiers detect and remove explicit content. This balances scale with accuracy and provides a robust, automated content moderation system that improves both safety and compliance on the Internet.
Authors - Shahedhadeennisa Shaik, Abhinav R B, Chaitra S P, Sagari S M Abstract - Video traffic surveillance has become an essential tool for various applications, including security, transportation planning, and traffic management. Recent advancements in deep learning have opened new possibilities for enhancing the performance of vehicle detection and tracking in these systems. This paper addresses the challenges of online action detection in surveillance scenarios by focusing on enhancing multi-object tracking (MOT) performance. Recognizing the limitations of current MOT methods in handling real-world surveillance complexities, we propose a methodology that integrates appearance model extraction directly from the object detector, adaptive adjustments of confidence thresholds and input resolutions, and the incorporation of color information into ReID embeddings. We aim to bridge the gap between motion-based and ReID-based tracking methods, improving both speed and accuracy. Our proposed techniques, including scene-based and object-based adaptation through reinforcement learning, and advanced feature fusion for ReID, are designed to enhance robustness and efficiency. We evaluate our methodology using publicly available datasets, focusing on surveillance-specific challenges. The enhancement in MOT performance is challenging and paving the way for more reliable and efficient surveillance system.
Authors - Meena Rani, Randeep Singh Abstract - The exponential growth of smart devices and advanced image editing tools has made detecting and localizing image forgeries critical for ensuring digital content integrity. This paper focuses on developing a robust and scalable model for passive image forgery detection using convolutional neural networks (CNNs). Leveraging datasets like CASIA1 and MICC-F220, the study aims to identify tampered regions in digital images by analysing noise patterns, pixel-level anomalies, and compression artefacts. The proposed methodology integrates preprocessing, model training, and validation using diverse datasets to enhance detection accuracy and scalability. Compared to traditional techniques, the deep learning-based approach shows significant improvements in detecting complex forgeries, including splicing and copy-move manipulations. Applications of this research extend to digital forensics, media authentication, and cybersecurity. The findings underscore Deep learning systems' show promise to tackle new issues in picture forgery detection and localization
Authors - Kritika Benjwal, Rishika Agrawal, Rashi Gupta, Dinesh Kumar Saini Abstract - The ublic distribution system (PDS) plays a vital role in eradicating hunger and ensuring food security across the world[1]. However, there are certain challenges like beneficiary identification, inconsistent transaction, diversion of grains during procurement at different stages to the open market, ration shop owners selling subsidized goods at higher prices and most importantly the paper focusses on supply chain leakages.[2] This paper proposes a conceptual model for implementing blockchain in PDS and eradicating all the supply chain leakages and making PDS fair[3]. It first focusses on operations of PDS, then assessing all the possible loopholes and implementing blockchain for removing these loopholes. It proposes the idea in which it leverages the benefits of using smart contract and consortium-based ecosystem that can bring efficiencies in PDS.
Authors - Sandhya Borkar, Shital Patil Abstract - The rapid development of the Internet of Things (IoT) has made the provision for innovative solutions in various sectors, including fuel management. The research presents the design and analysis of a smart IoT-based system for fuel dispensing, aimed at improving the efficiency, transparency and security of fuel distribution. The proposed system uses microcontrollers, sensors and real-time data communication technologies to automate fuel dispensing, monitor fuel levels and prevent theft or misuse. Key components include flow sensors to measure fuel output, RFID modules for secure user authentication and cloud-based platforms for remote monitoring and control. Additionally, mobile applications provide users with instant transaction records, fueling history and alerts. The system undergoes performance evaluation to ensure precise fuel measurement and seamless data synchronization. The results demonstrate significant improvements in operational efficiency and customer satisfaction, reducing manual errors, fuel theft and fraud, operational downtime, high maintenance and labor cost. This smart fuel dispensing system offers a scalable and cost-effective solution suitable for fuel stations, logistics companies and industrial applications, contributing towards smarter resource management and enhanced energy distribution practices.
Authors - Pravin Game, Shubham Bhingardive Abstract - Deepfake technology, which enables manipulation of images and videos, poses serious threat to media integrity and cyber security. Existing detection models often struggle with accuracy due to data complexity and variability. This study introduces a hybrid deepfake detection model that combines MobileNetV2, EfficientNetB7 and Vision Transformer (ViT) to enhance feature extraction and classification. ViT provides strong pattern recognition, EfficientNetB7 offers scalable accuracy and MobileNetV2 ensures lightweight processing. The model is trained on a publicly available datastet from Yonsei University, consisting of real and fake facial images. Techniques such as data augmentation and image resizing improve generalization. Experimental results show that the proposed model achieves 94.64% accuracy, 93.55% precision, 96.67% sensitivity, 92.31% specificity and 95.08% F1 score outperforming precious methods. These improvements ase statistically significant (p < 0.05). The results highlight the effectiveness of multi-model fusion for robust deepfake detection offering a scalable and reliable solution for applications in digital forensics, information verification and cybersecurity.
Authors - Pravin Game, Shubham Bhingardive Abstract - The correct identification of heart disease is essential for successful treatment and management. In this work, we assess different machine learning algorithms' predictive power for diagnosing heart disease. On the dataset, we employed the method of principal component analysis (PCA) to choose features, we got top 9 principal components out of 13 features. Then, applied the hippopotamus optimization algorithm on that 9 principal components then trained and tested the model on eight different algorithms: Bagging, Boosting, Naive Bayes, K - Nearest Neighbors (KNN), Random Forest, Decision Tree, Support Vector Machine (SVM), and Logistic Regression(LR). The algorithm’s accuracy ranged from 86.81% to 94.53%, The most accurate methods were SVM, KNN and random forest. These findings show that machine learning algorithms may be able to help with heart disease and focus on the need of choosing suitable algorithms for exact and trustworthy clinical decision-making. Future research will concentrate on using sophisticated on feature selection and ensemble learning strategies to further increase model accuracy.
Authors - Ganesh Shivaji Pise, A D Londhe, Hrushikesh Jaivant Joshi, Bhagwan Dinkar Thorat, Yashita Parikshit Mahalle, Pankaj Chandre Abstract - Data leakage poses a significant threat to modern security systems, leading to unauthorized access and privacy breaches. Deep learning models have shown promise in detecting such anomalies; however, their black-box nature raises concerns regarding trust and interpretability. This paper explores the role of Explainable AI (XAI) in enhancing transparency and trust in deep learning-based data leakage detection. Various explainability techniques, including SHAP, LIME, and Grad-CAM, are integrated into a security framework to provide interpretability while maintaining detection accuracy. The proposed architecture bridges the gap between AI-driven security solutions and human decision-making, enabling security analysts and compliance officers to make informed assessments. Additionally, the study evaluates different XAI approaches based on accuracy, interpretability, and scalability to identify optimal techniques for real-world security applications. The findings highlight the importance of balancing explainability with performance to ensure robust and trustworthy cybersecurity solutions.
Authors - Priya Surana, Soham Jadhav, Janhvi Jathot, Arnav Joshi, Roshani Kadam Abstract - Landslides are natural disasters posing great risks to life, infrastructure, and the environment. Timely and accurate predictions are highly beneficial to reduce such impact. The advent of machine learning (ML) and deep learning (DL) has significantly improved the state of landslide prediction models. The review outlines the various ML and DL techniques adopted for landslide prediction and gives a brief account of methodologies, applications, benefits, and limitations. This is mainly the melding of ML and DL techniques, such as Random Forest (RF), Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, for the enhancement of the predictive ability of such models. Key challenges in landslide prediction, such as data availability, model interpretability, and computational complexity, alongside future directions, will be discussed to contribute to the robustness of landslide prediction models. Finally, inferences will be drawn as to the significance of hybrid ML-DL approaches in pushing forth landslide prediction models into better accuracies and reliability (Khuc, T.D., et al, 2023)(Wu, X., et al, 2023).
Thursday August 27, 2026 3:30pm - 5:30pm IST Virtual Room BGOA, India
Authors - A.Punidha, E.Arul, E.Yuvarani, S.Rajasakaran Abstract - With the rapid expansion of Internet of Things (IoT) and smart device ecosystems, security threats such as malware attacks have become a critical concern. Traditional signature-based malware detection methods struggle to detect evolving and polymorphic threats, necessitating the development of intelligent, data-driven cybersecurity mechanisms. This study proposes a novel malware detection framework that integrates optimized feature engineering and deep neural networks (DNNs) to classify malware in smart devices with high precision. The approach focuses on behavioral feature extraction, including API call sequences, network activity logs, and application permissions, followed by feature selection techniques to reduce dimensionality while retaining key discriminative attributes. A comparative analysis of various machine learning (ML) models, including Random Forest, Support Vector Machine (SVM), and Deep Learning models, demonstrates that the proposed feature engineering-enhanced DNN model achieves 96.1% accuracy, outperforming conventional methods. Extensive experimentation on a real-world dataset of 10,000 smart device applications showcases the robustness and scalability of our framework. This research contributes to enhancing security in smart environments by providing an adaptive and computationally efficient malware detection system.
Thursday August 27, 2026 3:30pm - 5:30pm IST Virtual Room BGOA, India
Authors - Jolou Vincent M. Jala, Everly A. Nacalaban, Nenon Roy A. Sandinao, Ryan Boyd D. Origines, Randy Joy M. Ventayen, Neilson D. Bation Abstract - Artificial intelligence (AI) has completely transformed enterprises and organizations all over the world due to its propensity to spur innovation and mimic operational efficiency (Jala, J.V.M. et al., 2024). Through its capability to boost learning outcomes, promote inclusion, and streamline operations, artificial intelligence (AI) is revolutionizing higher education. This study investigates the influence of artificial intelligence in higher education in the Philippines. The study specifically seeks to understand how artificial intelligence (AI) can be used in higher education institution in terms of personalized learning amidst large class sizes, access to education in rural areas, solving job skills mismatch, modernizing administrative procedures in inadequate resources institutions, artificial intelligence powered innovation and research, challenges of embracing artificial intelligence such as digital literacy and infrastructure, social and ethical implications and resistance to change and faculty development Moreover, this work also examines the ethical considerations in employing arti-ficial intelligence in higher education in the Philippines, precisely in terms of security and data privacy, fairness and algorithmic bias, digital divide, human supervision and accountability, autonomy and consent, influence on staff and faculty roles and intellectual property and academic integrity. To realize this objective, the proponents essentially examined 170 publications in the literature that were indexed by Scopus to look at artificial intelligence in the context of higher education. This finding highlights artificial intelligence’s essential role in embracing challenges and improving higher education in the Philippines while emphasizing ethical considerations such as fairness and data privacy. (Jala, D.J.V., 2025).
Authors - Kishan Raj, Samyuktha Vimal, V Shini Abstract - In recent years, the Indian online fashion e-commerce industry has experienced significant transformations due to fast-paced technology, consumer behavior changes, and a growing e-commerce landscape. Competition is fierce, especially in the e-lifestyle market as its payoff will exceed $30 billion by 2025, developing brand equity understandably becomes a key factor in maintaining long-term success. It is understood that brand equity is a significant factor as it directly affects consumers' trust, buying decisions, and consumer retention, which are also vital elements in preserving brand equity in an industry where distinctions matter. This study's goal is to examine the impact of marketing mix variables 7Ps including Personalization, which has just recently emerged as a particular critical factor in the e-commerce business, on brand equity in the Indian online fashion e-commerce industry. Quantitative research methods were adopted to analyze data provided by consumers regarding the influence of such factors. The analyses indicate that Personalization and Place affect brand equity substantially, whereas traditional elements such as Price and Promotion do not exert such influence in online fashion retailing. Indian consumers seem increasingly to make a shift toward a digital-first shopping experience, the marketing strategies of brands must follow suit by creating engaging, personalized, and innovative interactions. These research findings will provide some strategic recommendations to online fashion retailers, marketers, and industry gurus seeking to consolidate their brand positioning in this ever-evolving and highly competitive marketplace.
Authors - Yash Tekade, Mayur Shinde, Bhumika Lipane, Nikita Patil, Suhasini Bhat Abstract - The paper presents the idea and methodology of development of a real-time threat detection system designed to enhance women's safety across various environments using AI technology and CCTV surveillance. The system consists of features like real-time person detection, gender classification, and SOS gesture recognition, all connected to an alert system for law enforcement authorities. It effectively identifies potential threats, including a lone woman at night or a woman surrounded by men, enabling proactive actions before incidents escalate. Additionally, the system maps hotspot areas where previous incidents have been recorded, allowing authorities to allocate resources efficiently. It also alerts security personnel about low-light conditions in an area, ensuring surveillance even in challenging environments. By combining these capabilities, the system aims to create a safer atmosphere for women, promoting proactive measures that can significantly reduce crime rates and contribute to enhance overall safety strategies.
Authors - Ritveek Rana, Manisha Manoj,vAnitha Dhanasekaran Abstract - This research endeavors to apply artificial intelligence to estimate past energy statistics and forecast future energy consumption patterns in India. The research utilizes energy indicators such as access to electricity, the share of renewable energy, CO2 emissions, and economic development to develop a model to forecast future energy needs and renewable energy share. The future energy consumption patterns and the share of renewable energy are forecast using regression analysis. The intention is to provide insights into energy transition required in order to ensure sustainability by reducing the reliance on fossil fuels and increasing renewable sources.
Authors - G.B. Sambare, Sankarsha Shelke, Sahil Wawdhane, Harshad Wable, Abhinav Thube Abstract - The KYC Powered by Blockchain for decentralized, secure, and more efficient Know Your Customer (KYC) system using blockchain. This system solves the inherent inefficiencies of traditional KYC by allowing institutions to share validated customer data, mitigating redundancy among KYC providers, and reducing both costs and compliance time. Tamper-proof architecture of blockchain allows for strong data privacy, security, and compliance of AML and GDPR regulations. Customers gain full control over their personal data, with the ability to grant and revoke access dynamically, reducing risks of breaches and fraud. The framework integrates off-chain storage for sensitive data and combines advanced cryptographic methods like AES and ECC for encryption and security. Smart contracts automate data handling and permissions management, ensuring secure, transparent, and immutable data sharing across institutions.
Authors - Sakshi G. Wagh, Snehal S. Shirsath, Vaibhavi V. Pujari, Shrirang A. Sonawane, Milindkumar B. Vaidya Abstract - Diagnosing brain tumors is a complex task due to their intricate characteristics and variability in presentation. Timely and accurate detection plays a vital role in ensuring effective treatment and improving patient prognosis. This study presents the development of an automated system for brain tumor detection and segmentation using Convolutional Neural Networks (CNNs). The model is trained on annotated MRI datasets to distinguish between normal and tumorous brain tissues with high accuracy. The proposed approach involves a comprehensive pipeline that includes image preprocessing to enhance MRI quality, training a CNN-based model for tumor recognition, and applying post-processing techniques to refine the output. By automating the diagnostic process, the system aims to support radiologists by increasing accuracy, reducing diagnostic delays, and minimizing manual interpretation errors. Furthermore, the project incorporates various image processing techniques and data augmentation strategies to strengthen the model’s performance and generalizability across diverse imaging conditions. The result is an intelligent and accessible diagnostic tool intended to assist healthcare professionals in delivering more precise and efficient brain tumor diagnoses, ultimately contributing to better clinical decision-making and patient care.
Authors - Monali P. Deshmukh, Dhanashri Arjun Ghadage, Mrunali Sunil Rangankar, Prajakta Dattatray Supugade, Deep Isane Abstract - This research paper presents an AI-assisted web-based coding platform, Code Understanding using Sherlock, that integrates a real-time compiler with an AI-powered chatbot. The chatbot provides contextual assistance based on selected code snippets or general programming queries. Users can toggle between a standard chatbot mode and a code-aware mode, where the chatbot analyzes selected code portions to answer relevant questions. The system enhances the coding experience by providing explanations, debugging help, and execution functionalities. By leveraging AI and NLP techniques, the chatbot can understand syntax, logical structures, and common programming errors, offering detailed feedback and solutions. The platform streamlines the development process by reducing debugging time and enhancing code comprehension. Additionally, the system provides a seamless file management experience, enabling users to create, edit, and organize their projects efficiently. This integration fosters an interactive learning and development environment, making it valuable for both beginners and experienced programmers.
Authors - Anandhukrishna A S, Santanu Mandal, Raghu Raman Abstract - Digital Twin (DT) technology offers unprecedented capabilities that are transforming supply chain management (SCM), delivering a new level of system-wide resilience, real-time insights and predictive analytics. Yet, the nascent research still lacks in terms of cohesion, with most studies being heavily centralised around technical solutions and missing strategic, managerial and empirical perspectives. In light of this gap, the current study provides a thorough bibliometric analysis of 99 peer-reviewed articles published from 2016 to 2024 selected from Scopus with analytical tools of Biblioshiny R package. The results clearly showed that there was a higher growth of DT-related SCM research after 2020, indeed due to significant intercontinental disruptions and the demand of resilient, sustainable, and intelligent infrastructure systems. Resilience in the supply chain, sustainability, interoperability, and AI-driven optimization are core themes. Importantly, while China, Germany, and the USA dominate in terms of number of papers produced, institutions such as The Hong Kong Polytechnic University are also leading in productivity metrics here. However, the analysis reveals important gaps — notably a lack of cross-border cooperation and empirical case studies as well as longitudinal research. Less developed but rich prospects, like the integration with blockchain, extended reality and physical internet also emerge as compelling themes. This work offers actionable insights into the way forward for researchers, policymakers, and industry leaders, calling for cross-disciplinary partnerships, real-world pilots, and frameworks for applying overarching compliance. This also advances the role of Digital Twins as a strategic enabler of a resilient and future-ready supply chain.
Thursday August 27, 2026 3:30pm - 5:30pm IST Virtual Room CGOA, India
Authors - Jhalak Bansal, Janvi Jain, Sukti Jain, Harsh Chaudhary, Vikas Srivastava Abstract - Traffic accidents, a leading cause of death worldwide with nearly one million fatalities annually (WHO), are often driven by fatigue-related drowsiness. Our project introduces a real-time drowsiness detection system leveraging technologies like OpenCV, Python, and machine learning to enhance safety and accuracy. Using a camera, the system monitors facial features and eye movements, Using facial landmark detection to identify 68 key points, the system calculates the Eye Aspect Ratio (EAR). Extended periods of eye closure activate an alert, and GPS-enabled location tracking enhances response by sending automated emails with the vehicle’s real-time location to pre-registered contacts. The methodology integrates image processing, real-time facial landmark detection, and a dynamic scoring system to evaluate drowsiness. With an accuracy target of over 85%, the system addresses the limitations of existing solutions while introducing innovative location-based intervention. Results highlight its potential to reduce drowsy driving incidents, ensuring safer roads.
Authors - Aoudumber Londhe, Ravindra Apare, Parikshit Mahalle, Ravindra Borhade Abstract - Aqua status quality prediction is a vital part of environmental monitoring, with significant implications for public health, ecosystem sustainability, and Aqua resource management. Traditional methods for evaluating aqua quality, is like taking the manual sample and to perform the laboratory analysis, are often labour-intensive and limited in scope. Recent developments in deep learning have transformed this domain by empowering the expansion of predictive models accomplished with analysing non-linear relationships in Aqua quality. Hybrid deep learning models, merging Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and Gated Recurrent Units (GRUs), have verified superior performance in apprehending spatial and temporal dependencies in Aqua quality data. Optimization algorithms such as Particle Swarm Optimization, Grey Wolf Optimization, Sparrow Search Optimization (SSO), and Beluga Whale Optimization (BWO) have been integrated to enhance model accuracy and efficiency. Attention mechanisms and feature selection techniques have further improved model performance, while the integration of IoT has enabled real-time monitoring, addressing the limitations of traditional methods. Despite these advancements, challenges related to model interpretability, computational complexity and most important part data availability remain as it is. This review explores the pragmatic augmentation in hybrid deep learning models for Aqua quality prediction, focusing on their architecture, optimization techniques, and real-world applications.
Thursday August 27, 2026 3:30pm - 5:30pm IST Virtual Room CGOA, India
Authors - Rakhi Bharadwaj, Mohit Deo, Pratham Jain, Ashishkumar Jha, Harsh Bachhav Abstract - This study introduces a novel open-source educational website that makes use of AI-powered 3D environments and interaction with historical individuals to deliver immersive historical learning experiences. The site offers both contemporary views of these environments using 3D Gaussian splatting technology and offers precise historical recreations using Pixel Streaming. Interactive conversation with AI-powered historical individuals, dynamic quizzes to validate the knowledge of users, and AI-powered historical narratives are all among the offerings. To support knowledge about historical events and cultures from the past, the system merges interactive learning and storytelling for a fun and educational experience. Advanced natural language processing (NLP), speech-to-text, and AI-powered tour guides are all included as part of the platform architecture to provide personalized historical tours without necessitating complicated personal details.
Authors - A. Harshavardhan, Konkathi Nihal, Ramini Srinidhi, Konda Poojithasai, Gochika Bhanu prasad, Dhanraj Sai Ganesh Abstract - This paper presents a novel, dual-layer secure steganographic system that combines hybrid cryptography and steganography to ensure the confidentiality, integrity, and security of secret communications. Initially, the sender inputs their message and selects a cover image. The message is encrypted using a hybrid substitution (playfair cipher and columnar transposition cipher) and transposition cipher, and then embedded in randomly selected pixel positions of the image using Least Significant Bit (LSB) steganography. A position file that records these embedding locations is generated and encrypted. To obfuscate the presence of the stego-image, multiple duplicate images are created alongside the steganographic image. On the receiver's side, a ResNet50-based feature extractor followed by K-means clustering is used to identify the stego-image from the duplicates. The encrypted position file enables accurate message extraction and subsequent decryption. Experimental results show excellent performance with high imperceptibility (MSE: 0.0175, PSNR: 65.69 dB, SSIM: 0.9994) and strong resilience to brute-force and statistical steganalysis.
Thursday August 27, 2026 3:30pm - 5:30pm IST Virtual Room CGOA, India
Authors - Dhruv Aswani, Aman Sande, Praful Pradhan, Rajveer Tolani, Pallavi Saindane Abstract - Security concerns and the empowerment of women remain highly pressing challenges in India, with significant issues evident across urban, semi-urban, and rural regions alike. Women’s mobility is often constrained by the fear of harassment, crime, and social barriers which slows down their movement toward true empowerment. To address these problems, this study focuses on the major drivers of women’s safety and empowerment which include social norms, presence of crime, supportive structures, and women participation in technology. To address these challenges, this paper proposes Agati, an Android application that offers safety and empowerment features specifically tailored for women. Agati combines real-time safety alerts, location tracking, community support networks, and financial literacy modules to foster both security and economic independence. By leveraging data analytics and user feedback, the app aims to build a personalized, data-driven solution that bridges the gap between security and empowerment. Through this integrated approach, Agati seeks to create a safe, supportive environment that promotes social power and holistic growth for women.
Authors - Nikhil Vaishya, Amey Sawant, Mayank Shukla, Suhani Pandey, Vaishali Kosamkar Abstract - Augmented Reality (AR) is transforming the learning experience in anatomy and biology [1, 2]. by providing an engaging and interactive alternative to traditional teaching methods. Understanding complex anatomical structures has historically been challenging due to the limitations of textbooks, static models. AR overcomes these challenges by enabling students to explore high-fidelity 3D representations of the human body in real-time, fostering deeper spatial understanding and retention. The technology allows learners to interact with anatomical structures, receive immediate feedback, and learn at their own pace, beyond the constraints of the classroom. In addition to AR visualization, this project integrates an AI-assisted quiz and learning platform to further enhance anatomy education. By leveraging machine learning algorithms such as ensemble methods like Random Forests and Support Vector Machines (SVMs), coupled with SMOTE for class imbalance handling and cross-validation for robust generalization, the system offers adaptive quizzes, personalized learning recommendations, and real-time feedback. The platform dynamically adjusts to user interactions, ensuring a tailored and effective learning experience. Developed using Unity for AR functionalities, JSON for data management, and machine learning for prediction models, the system bridges the gap between theory and practice while promoting active and self-paced learning. This paper details the design, development, and evaluation of the AR-based Anatomy Learning Platform, highlighting its potential to revolutionize anatomy education by offering an accessible, immersive, and personalized approach. The platform is designed primarily for medical students, but it also supports general learners seeking to enhance their anatomical knowledge through immersive technologies.
Authors - Shital Pawar, Parag Dolhare, Saish Fatangare, Harshdeep Gawhale, Aditya Gadgil Abstract - Environmental, social and governance (ESG) criteria have become essential to assess the sustainability and social impact of companies. This article presents the development of an automated ESG ranking system that uses natural language processing (NLP), sentiment analysis, and machine learning techniques to rank and rate companies based on ESG metrics. Using a pre-existing database of news articles, we used the VADER sentiment analysis tool to assess the polarity of the text data, categorizing it as positive, negative or neutral. Sentiment scores were converted to numerical scores for each ESG component. In addition, Node2Vec is integrated to create network graphs that represent the relationships and interconnections between companies, allowing a comprehensive analysis of potential impacts. The results were visualized with Altair to provide a clear view of ESG trends and relationships that impact the company's performance over time. This study demonstrates the utility of combining NLP and advanced graph analytics for scalable data-driven ESG assessment.
Authors - Chalamalasetty Nishitha, Yelavarti Kalyan Chakravarti, V. Esther Jyothi Abstract - Sensitive domains such as healthcare institutions are increasingly relying on Federated learning for data security. Irrespective of this approach, they are gullible to adversarial attacks such as poisoning attacks and confidentiality breaches. To overcome these hindrances, Blockchain driven Federated learning is put forward, which integrates Secure Multi-Party Computation (SMPC) with Zero-Knowledge Proofs (ZKPs). This framework strives to ensure confidentiality in a distributed training environment. The individual entities train their local AI models with their exclusive datasets and generate Zero Knowledge Proofs to assert the accuracy of the model updates. The SMPC protocol encrypts the model updates, which are later aggregated to enable computing that guarantees privacy. Later, Smart Contracts are used to immutably store these adjustments on the Blockchain ledger, ensuring impenetrable model ensemble. To improve the trade-off between model dependability and precision, privacy noise is dynamically adjusted by employing adaptive differential privacy, based on individual client’s reputation. Extensive experiments prove the fact that the proposed system prominently reduces computing overhead in comparison to the established system while strengthening the attack detection rates. This architecture establishes a benchmark for information security in delicate areas like healthcare systems while designing its data-sensitive Al models.
Authors - Nandana R, Rithika Kannan, Ramgeeth N Nair Abstract - The rise of fintech applications has revolutionized financial decision-making, yet the determinants of risk-taking behavior in these digital platforms remain a critical research area. This study investigates the role of gamification, financial knowledge, and psychological influences in shaping users’ risk-taking behavior. Using a quantitative approach, an Ordinary Least Squares (OLS) regression analysis was conducted on a dataset of 200 fintech users. The results indicate that gamification has a significant positive effect on risk-taking behavior (β = 0.1414, p = 0.001), suggesting that game-like elements in fintech apps encourage users to take greater financial risks. However, certain gamification effects exhibit a negative influence (β = -0.1272, p = 0.005), highlighting that not all gamification strategies lead to in- creased risk-taking. Financial knowledge also emerged as a significant determinant (β = 0.1965, p = 0.001), implying that financially literate users tend to take more calculated risks. Among psychological factors, risk tolerance (β = 0.2754, p < 0.001) was the strongest predictor, demonstrating that individuals predisposed to risk-taking in general extend this behavior to fintech platforms. Additionally, social efficacy (β = 0.2461, p < 0.001) and social influence (β = 0.1598, p = 0.004) significantly contribute to risk-taking, emphasizing the role of self-perceived competence and peer influence in financial decision-making. The model explains approximately 48.1% of the variance in risk-taking behavior (R² = 0.481), confirming the robustness of these deter- minants. The findings underscore the importance of designing fintech applications that balance engagement with responsible financial behavior. Future research should explore the ethical implications of gamification and assess long-term user behavior to ensure sustainable financial decision-making in digital finance ecosystems.
Authors - Ramesh Babu Mutluri, Vinit Kumar Singh, D Saxena Abstract - Rural areas in developing countries are still refrained from continuous and uninterrupted power supply to power their household and run small industries. Thus, we can say that these rural areas are weakly connected to the utility grid. The main reasons for poor power supply are weak infrastructure, lack of adequate generation to fulfill the demand-supply gap, dependency on long-distance transmission, frequent load shedding, and distributed generation. This demand-supply gap can be minimized by installing renewable energy sources with the local load forming rural microgrid and connecting to the utility grid. The grid connection would help to maintain the power supply due to the variable output characteristics of renewable energy sources thus also acting as a buffer to the local power system. This paper presents a novel approach towards modeling of utility connected rural microgrid comprising renewable energy sources considering control architecture for marinating frequency-voltage interdependency. Accordingly, a frequency-based voltage controller is introduced. Further, the model has been verified in view of various scenarios with a fluctuation in load demand and power input to renewables. The controllers are tuned such that in case of increase in load or decrease in power generation, power demand is met from the utility grid, and in case of surplus generation, the power is fed to the grid, therefore, developing microgrid as business unit applicable for power trading. The model has been developed in Simulink/MATLAB. An integral square error criterion has been used for tuning the controllers to mitigate the oscillations.
Authors - Ashwitha A Shetty, Naganna Chetty, Antony P.J Abstract - The poultry industry is a significant and prominent business sector. As the daily intake of chicken meat and eggs is rising globally, poultry farming is gaining significance for providing protein. Additionally, this industry raises the nation's revenue despite being a less expensive protein source. Numerous diseases that harm the chickens are the main issue affecting the poultry business. Due to the high cost of vaccinations, poultry owners are unable to adopt these expensive methods. Consequently, this strategy cannot be used because it requires continuous investment. This paper aims to present one of the prevalent chicken diseases, coccidiosis and the different detection techniques used. In this regard, the study introduces multiple strategies that can be used in tandem to identify coccidiosis-affected fowl hens automatically. The idea behind studying chicken activity monitoring is that it directly connects to the health condition of the chicken. The enhanced future research could result in a system to monitor chicken activity and detect coccidiosis among them
Thursday August 27, 2026 3:30pm - 5:30pm IST Virtual Room DGOA, India
Authors - Piyush Sharma, Harish Patidar, Anuj Kumar Abstract - This research introduces a ResNet-based framework for multiclass classification of mammographic density and mass regions. The framework was rigorously tested using two prominent mammographic datasets, INbreast and DDSM, and benchmarked against other models, including CNNs, Random Forest (RF), Support Vector Machines (SVMs), Logistic Regression (LR), and K-Nearest Neighbors (KNN). ResNet demonstrated superior performance across all critical evaluation metrics—accuracy, precision, recall, F1-score, and AUC—outclassing the comparative models on both datasets. Its proficiency in extracting complex hierarchical features and addressing multiclass classification tasks positions it as a robust choice for breast cancer diagnosis. This framework offers a reliable and efficient tool for automating diagnostic processes, with the potential to significantly improve clinical decision-making and patient care.
Authors - Pranav Bagal, Bhavesh Patil, Shounak Muglikar, Yash Sonavane, Prajakta S. Shinde Abstract - The study addresses dental caries detection and classification using state-of-the-art deep learning architectures. We implemented and compared three pre-trained convolutional neural network models: VGG19, DenseNet169, and ResNet101, to automatically identify and classify dental caries from intraoral clinical image. Our research focused specifically on pediatric populations aged 1 to 14 years, where caries remain a significant health concern despite global prevention efforts. The models were trained and validated on a comprehensive dataset of dental images. Performance metrics demonstrated that DenseNet169 model achieved superior results with an Validation accuracy of 72.22%. These deep learning approaches show promising potential to augment traditional diagnostic methods, particularly in resource-limited settings where expert dental practitioners may be scarce. By enabling earlier and more accurate detection of carious lesions, our proposed system could help address disparities in oral healthcare accessibility and contribute to more effective intervention strategies, especially for underprivileged populations where caries prevalence continues to rise. This research establishes a technological framework that could be integrated into portable diagnostic tools for use in diverse clinical environments.
Authors - Govinda Sambare, Lalit Deore, Harsh Itkar, Onkar Jadhav, Sarthak Joshi Abstract - This research presents the development of an intelligent stock recommendation system that utilizes advanced machine learning models for informed long-term investment decisions. The system addresses the complexities of the stock market, where traditional methods often fall short in accessibility, accuracy, and efficiency. By automating fundamental analysis with models like Long Short-Term Memory (LSTM) networks and the CNN-GRU-XGBoost hybrid model, the system integrates key financial ratios, macroeconomic indicators, and sector performance, providing data-driven insights. The proposed framework optimizes stock selection using XGBoost and forecasts future stock prices with LSTM, offering precise and scalable solutions for diverse investment portfolios. The literature review highlights modern methodologies like TRAN, Bi-LSTM, and hybrid models, which improve stock forecasting and trading strategies by incorporating temporal dependencies and inter-stock relationships. The algorithmic analysis explains LSTM's ability to handle sequential data and the hybrid model's powerful feature extraction and prediction capabilities. This hybrid approach enhances decision-making, saves time, and democratizes financial insights, making advanced analysis accessible to individual investors, robo-advisors, and educational institutions. While offering benefits like scalability and reduced biases, the system also faces challenges, such as computational costs and market volatility. Backtesting results confirm the system's adaptability to dynamic market conditions, ensuring sustainable investment strategies. This project showcases the transformative potential of AI/ML in financial analytics, laying a strong foundation for long-term, informed investment decisions.
Authors - Vedant Chandore, Niranjan Pardeshi, Sai Sinare, Samruddhi Akude, Sahil Dhawane, Kartik Gawande, Rahul Sadgir, Shravani Nigade, Ajay Talele Abstract - For transportation infrastructure to be safe, effective, and long-lasting, road condition monitoring is essential. Conventional techniques, which depend on human inspections, are frequently ineffective and prone to mistakes. To overcome these constraints, this study suggests a machine learning-based smart road condition monitoring system. Utilizing cameras installed on vehicles, the system gathers pictures and videos of the state of the roads, which are subsequently processed by sophisticated machine learning algorithms. These algorithms categorize surface conditions, identify irregularities in the road, and offer information on repair requirements. Through comprehensive field testing and data analysis, the study shows how effective the system is, showing notable gains in both the efficiency of maintenance procedures and the accuracy of identifying road issues. By concentrating on image and video analysis, this smart monitoring system offers a revolutionary solution for urban infrastructure management, opening the door for safer, more intelligent, and sustainable road maintenance procedures. This strategy not only lowers operating costs but also improves road safety and infrastructure sustainability.
Authors - Sai Himagnya Parisaneni, Vemula Surya Teja, Revanth Guthula, Sushama Rani Dutta Abstract - This study presents an optimized approach for detecting mental disorders by integrating support vector machines (SVM) enhanced through Minimum Bayes Error Rate (MBER) optimization. The proposed framework uses MBER Optimization and refines classification boundaries through SVMs improve decision-making. Unlike conventional deep learning approaches that rely solely on CNN based end-to-end learning, our method uses SVM for classification that minimizes errors, enhancing model robustness and generalization. The experimental evaluation on EEG-based datasets assesses the effectiveness of the hybrid approach in terms of accuracy, computational efficiency, and scalability. The results provide insights into the potential of MBER-optimized SVM models for real-world applications in mental health diagnostics.
Authors - Satish Chikkamath, Shreya Pattanashetti, Pooja V Gadad, Vidya Revanakar, Bhoomika Hosamani Abstract - Emojis serve as an established means for people to express emotions and sentiments while interacting on social media. This paper examines the task of emoji prediction from text by developing accurate classification methods. The model uses a pre-trained and fine-tuned BERT framework on a dataset consisting of text sentences along with their corresponding emojis. This structured data allows the model to capture contextual meaning and emotional nuances, which are crucial for practical applications. Challenges associated with emoji usage are addressed through tokenization techniques in text preprocessing, while performance advances are achieved using stemming and feature extraction. Research conclusions indicate that the BERT-based model outperforms traditional deep learning approaches like LSTM. This study spotlights how NLP and sentiment analysis contribute to emoji prediction and shows its practical applications in social media monitoring, sentiment analysis, and enhancing user experiences.
Authors - Sagar Janokar, Krish Deshpande, Krishna Masane, Shriyash Kothe, Varad Kulat, Krish Chabria, Rushikesh Kuchekar Abstract - This project demonstrates how a machine learning based approach can revolutionize the analysis of unstructured text data in defense intelligence. By automating key processes, the system will enable faster and more accurate identification of threats and patterns, improving decision-making and operational efficiency. This innovative application highlights the transformative role of technology in addressing real-world challenges in intelligence gathering.
Authors - Dhanaselvam J, Dhanalakshmi R, Prashaanth S, Hariprasath S, Harish R Abstract - India, with three-fourths of its population dependent on agriculture, is plagued by severe crop loss due to pest infestation, particularly in staple crops like rice, wheat, maize, and soybeans. This paper proposes an embedded system of real-time pest detection and precise pesticide spraying to enhance productivity. The system employs deep learning with a Residual Neural Network (ResNet) and Quadra-attention, residual, and dense fusion techniques for enhanced pest image classification. High-resolution images of crop leaves are captured, pre-processed, and analyzed for pest detection. Upon detection, the system selects the appropriate pesticide and activates an autonomous robotic sprayer. Driven by an Arduino NANO-based module with an L293D motor driver, the robotic system automatically navigates through fields, ensuring precise pesticide application without waste and infrastructure costs. With IoT integration and 99.80% validating accuracy, this system optimizes pesticide use, enhances crop health, and enhances yield, offering a cost-effective automated pest management system for sustainable agriculture.
Authors - Nikita Bhatt, Nirav Bhatt, Purvi Prajapati Abstract - In today’s data-rich world, we often deal with multiple types of information such as images, text, and audio. Traditional deep learning models usually focus on a single type of data, but real-world applications need systems that can understand and connect across these different formats — a concept known as multi-modal learning. This paper explores cross-modal retrieval, where a user can input one type of data (like an image) and retrieve another (like related text). To make this possible, we map different data types into a common space using deep learning methods like CNN for images and LSTM for text. One of the key challenges in this area is comparing vectors of different lengths, which affects similarity estimation. Most traditional methods use inner product similarity, which is not ideal for vectors with varying magnitudes. To overcome this, we normalize the vectors using cosine similarity, which focuses only on the angle between vectors, not their length. This improves retrieval accuracy by reducing noise caused by vector size differences. We also discuss the benefits of using deep learning to jointly learn features and generate hash codes for faster and more accurate retrieval. Experiments on datasets like Google News show that cosine similarity outperforms Euclidean distance in terms of retrieval performance, especially when combined with models like CBOW.
Authors - Ria Ashish Gawali, Christopher Sachin Chopde, Aryan Gupta, Tashmeet Kaur Jasbeersingh Hora, Rachna Karnavat Abstract - MediaGPT is a Generative AI system that combines natural language and image synthesis to create unified, visually appealing media content. By combining strong language models such as ChatGPT and Phi-3 with image synthesis models such as Stable Diffusion and ControlNet, MediaGPT facilitates intelligent text-image alignment on an interactive canvas. The layout can be easily customized along with semantic coherence and aesthetic balance. Developed for designers, educators, marketers, and content creators, MediaGPT improves the creative process by facilitating effortless multi-modal integration and providing easy-to-use tools for creating high-quality, contextually appropriate content.
Authors - Lokesh Khedekar, Atharva Kassa, Kartavya Sharma,Tejas Kedar, Sarthak Kasar, Kaustubh Kelgandre, Sharad Kasralikar Abstract - Natural Disasters have been a major threat to the living beings, environment and the infrastructure, in mainly areas where they have poor access to early warnings systems. This paper provides AI-based Natural Disaster Response System which helps to evaluate the impact of natural disasters and gives better of the existing systems. The system has historical Geographic Information System (GIS) datasets with real-time data from Internet of Things (IoT) sensors and predictive modeling to check out the natural disaster’s magnitude, area of impact, and resources. The methodology includes data preprocessing, feature extraction, and machine learning model training to achieve effective predictive accuracy. A Convolutional Neural Model (CNN) model was created and tested which further achieved 93% accuracy of predicting the impact of the disaster incident. The system was then compared with other machine learning models, then was proved to be more effective. The suggested method gives efficient, cost-effective and scalable way of utilizing the emergency resources at the maximum.
Thursday August 27, 2026 3:30pm - 5:30pm IST Virtual Room EGOA, India
Authors - Prasanna Lakshmi T, Shankar Lingam. M Abstract - This paper explores the intersection of emerging technologies and ICT policy evolution in India, with a focus on Artificial Intelligence (AI), blockchain, the Internet of Things (IoT), and 5G technologies. As India navigates its digital transformation through initiatives like Digital India, the paper examines how the nation's ICT policy framework has adapted to accommodate these disruptive technologies. Using a theoretical approach based on Technological Innovation Systems (TIS), the study traces the historical development of India's ICT policies, from early telecom regulations to the modern-day focus on digital infrastructure and smart technologies. Challenges such as the digital divide, cybersecurity, and data privacy are also analyzed. By identifying key policy milestones and evaluating India's current efforts in integrating emerging technologies, this paper provides insights into the future direction of ICT policy in India. The findings highlight both opportunities and barriers to sustainable technological advancement and offer policy recommendations to better align ICT governance with global trends.
Authors - Agatsya Yadav, Renta Chintala Bhargavi Abstract - Large Language Models (LLMs) offer powerful capabilities but their significant size and computational requirements hinder deployment on resource-constrained mobile devices.This paper investigates Post-Training Quantization (PTQ) for compressing LLMs for mobile execution. We specifically apply 4-bit PTQ using the BitsAndBytes library via the Hugging Face Transformers framework to Meta’s Llama 3.2 3B model. The quantized model is further converted to the GGUF format using llama.cpp tools for optimized mobile inference. The proposed PTQ workflow achieved a 68.66% reduction in model size through 4-bit posttraining quantization, enabling the Llama 3.2 3B model to run efficiently on a standard Android device. Qualitative validation confirmed the 4- bit quantized model’s ability to perform inference tasks successfully. We demonstrate the feasibility of running the final quantized GGUF model on an Android device using the Termux environment and the Ollama framework. PTQ, particularly down to 4-bit precision combined with mobile-optimized formats like GGUF, presents a viable pathway for deploying capable LLMs directly on mobile devices, balancing model size and functional performance.
Thursday August 27, 2026 3:30pm - 5:30pm IST Virtual Room EGOA, India
Authors - Dwayne Nixon, Shaun Menezes, Ramya Kulkarni, Phiroj Shaikh Abstract - In today’s fast-paced development environment, where efficiency and speed are paramount, manual tasks such as taking screenshots, converting files, rebooting systems, and managing repositories have become increasingly tedious and time-consuming. These routine activities disrupt developer workflow and hinder productivity, consuming valuable time. To address these inefficiencies, this work proposes a comprehensive automation tool that extends beyond handling basic tasks to streamline workflows and optimize productivity. Firstly, this tool centralizes a wide range of operations, including automating code generation, creating detailed reports, and developing websites. By integrating these functionalities, developers can eliminate redundant tasks and focus on high-level problem-solving. Secondly, the automation tool enhances accuracy and consistency across development projects, ensuring higher standards of work and reducing errors associated with manual processes. Furthermore, the tool aligns with evolving technological demands, enabling teams to adapt to increasing project complexities while maintaining efficient workflows. This solution represents a transformative approach to software development, combining automation and centralization to reduce manual workloads and optimize developer productivity. The implementation of such an all-in-one automation platform promises to significantly improve efficiency and foster innovation in the industry.
Authors - Piyali Karmakar, Pabitra Mitra, Manjira Sinha Abstract - Communication is fundamental to human connection. Individuals with complex communication needs (CCN), such as those with cerebral palsy, often face significant barriers to speech and language expression. Augmentative and Alternative Communication (AAC) systems address these challenges through the use of graphical symbols. In this work, we strengthen AAC capabilities by creating specialized datasets that support bidirectional translation between symbolic language and natural English text using NLP techniques (Sym2NL). The datasets are enriched with tense and narrative features to improve contextual accuracy.We also present PictoGen, a text-to-picture generation module designed to visually represent unfamiliar words or concepts. Together, these contributions support more natural, expressive, and accessible communication.
Authors - Pallavi Patil, Mansing Rathod Abstract - The rapid evolution of malware, including polymorphic and fileless variants, has weakened traditional detection methods. This paper looks at sophisticated malware detection frameworks that use deep learning and machine learning to assess important malware features such network anomalies, opcode sequences, and API requests. Signature-based techniques are effective at identifying known dangers, but they are not very effective at thwarting zero-day assaults. While they offer improvements, alternative strategies including behavior-based, cloud-based, and deep learning techniques also have drawbacks. The current detection frameworks unify real-time threat information with two IDS detection approaches to enhance security capabilities. The review extends its analysis to model interpretability and evaluates the computational burden. The assessment of experimental findings helps researchers enhance adaptable malware security through the display of improved detection precision and resilient capabilities versus evolving cyber threats
Authors - Pragathi Guduru, Ramya S, Anitha H Abstract - Hand sign recognition systems play a crucial role in bridging communication gaps for people with hearing and speech impairments. This review paper explores various methodologies and algorithms employed in previous research on hand sign recognition, analyzing their performance, accuracy, computational efficiency, and effectiveness in real-world applications. Special emphasis is given to algorithms related to the Discrete Fourier Transform (DFT), including the Hebbian Classifier, Radial Basis Function (RBF) networks, and Self-Organizing Maps (SOMs), which have been utilized for feature extraction, pattern recognition, and classification. The study also examines deep learning approaches such as Convolutional Neural Networks comparing their strengths and limitations. Additionally, the paper highlights how these advances contribute to assistive technologies in healthcare, aiding doctors during medical procedures, and improving accessibility for individuals in need. By providing a comparative analysis of these techniques, this review aims to offer insights into the most effective strategies for enhancing hand sign recognition systems, paving the way for future research and innovation in the field.. . .
Authors - Ajay Talele, Sujal Tawale, Tushar Ghorpade, Nikhil Wagh, Aryan Sable, Pallav Vaniya, Yashashree Mehare, Parishnav Thokal, Samruddhi wayal, Vedant Motale Abstract - Book discovery in the digital age is extremely difficult which is the result of various factors such as users struggling with information overload as well as trying to find the content most closely to their needs. Albeit there are many recommendation systems available in the market, most of them are just based on general ratings and neither do they use the rich metadata that is available from external book sources nor do they provide the functionality of exploring related work in the best way. Through the use of our proposed book recommendation system, the constraints that are in place currently can be very easily overcome effectively by the use of more advanced reinforcement machine learning and data integration techniques. What the model would do is to analyze users' reading history and preferences and combining data from bookstores so that an efficient and effective model would be built which would give accurate suggestions of books to the users according to their preference. The Python language is chosen as the basis for development and for the backend, the Flask framework is used while for finding the most appropriate document for the reader, TF-IDF vectorization, and cosine similarity are employed. Moreover, the linkage of outside APIs not only makes it more in-depth to look at but also increases the system's accuracy. Our approach enables the users to discover new and personalized books in a simple and efficient manner. Our project is a direct contribution to a highly interactive reading journey and it also contributes to increasing love for literature by giving the users the opportunity to find books that will truly engage them.
Authors - Mariya Joseph, Vinod Kumar K Abstract - Greenwashing, the practice of brands making false or exaggerated environmental claims, has become a major concern across industries, especially in the food, fashion, and beauty sectors, as consumer demand for sustainable and ethically made goods rises. This study examines how consumers react to greenwashing, with a particular emphasis on their awareness, attitudes, and behaviors in the face of false sustainability promises. This study investigates how consumers recognize and interpret greenwashing, the emotional and cognitive elements affecting their reactions, and the actions they take in response—such as boycotting brands or looking for more transparent alternatives—by analyzing consumer surveys and existing literature. The paper also delves into the role of brand trust, social media, and regulations in shaping consumer reactions to green-washing. Results indicate that although consumers are become more conscious of greenwashing, there is still a sizable gap in their capacity to recognize false claims. The study emphasizes how crucial third-party certification, brand openness, and consumer education are to reducing the damaging effects of greenwashing. In the end, the study urges consumers and brands to take a more proactive and knowledgeable stance inorder to guarantee that sustainability initiatives are sincere and significant.
Authors - Ajay Talele, Omkar Shinde, Shrey Rai, Shreyash Mutha, Rohan Shelke, Sumedh Malode, Soyam Maykar, Siddhesh Manjare Abstract - With the increased demand for secure and efficient ways to transfer files, peer-to-peer systems have developed as good solutions. Seen with client-server systems, they can be very successful, however, typically suffer from bottlenecks and single points of failure, along with being less efficient for large quantities of data exchange - P2P networks tend to instead successfully allocate workloads over different peer nodes and distributed information along with concurrent and fault-tolerant characteristics. This paper details the design and implementation of a multi-threaded file-sharing system using Java Sockets, multi-threading concepts, and other relevant networking ideas. This system allowed multiple users to exchange files in a secure way over a network efficiently, allowing for concurrency in this system through efficient thread synchronization. Each peer operated independently as both a client and a server, allowing for collaboration and facilitated file transfers across nodes without a central authority.
Authors - Ayushi Sensharma, Ashish Sharma, Swati Agrawal Abstract - The gender discrimination is a significant issue in the labor market. “Motherhood Penalty” is one of the important contributors to this issue. This study aims to find the evidence of impact of parenthood on employment to population ratio and mean nominal monthly earnings concerning factors – household structure and number of children under age six. Using interactive multiple linear regression models, we have derived meaningful conclusions from data collected from the International Labor Organization (ILO). Our findings reveal that there is a significant motherhood penalty in India. Women’s employment probability decreases by 12.4% with one child and up to 19.09% with three or more children. Meanwhile, men experience a fatherhood bonus, with employment rates rising by up to 24.79% as they have more children. Wage disparities are also evident—mothers with two or more children earn substantially less than childless women, whereas the fatherhood wage premium is weaker than in developed economies. Wage disparities are also evident. Mothers with two or more children earn substantially less than childless women, whereas the fatherhood wage premium is weaker than in developed economies. Through this study, we also see the probable reasons behind the results observed from the models. Lack of institutional support for working moms, workplace prejudice, and deeply rooted gender stereotypes are some of the main reasons attributing to the “Motherhood Penalty”. This disparity is further exacerbated by strict work rules, poor childcare facilities, and lax paternity leave regulations. Overall, the motherhood penalty is a serious phenomenon affecting the lives of many mothers and degrading their standards of living.
Thursday August 27, 2026 3:30pm - 5:30pm IST Virtual Room FGOA, India
Authors - Rupali Parte, Vaishali Kapure, Pranav Bankar, Shrutika Mandharne, Avadhoot Khandagale Abstract - This research presents a cloud-integrated machine learning (ML) system designed to enhance e-commerce operational efficiency. Leveraging Microsoft Azure’s data services (Azure Data Factory, Databricks, ADLS Gen1 and Gen2, and Power BI), along with a Streamlit user interface, the system processes large-scale transactional data for real-time analytics and decision-making. Four specialized ML models address key challenges: logistics clustering optimizes shipping routes; sales forecasting improves inventory management; fraud detection strengthens security; and order cancellation prediction enhances customer retention. The automated data pipeline ensures efficient ingestion, transformation, and storage, minimizing latency and maximizing data accessibility. The interactive Streamlit interface allows users to select and deploy models, while Power BI dashboards provide dynamic visualizations. This integrated approach demonstrates the potential of cloud computing and ML to improve logistics, enhance fraud prevention, and optimize revenue forecasting.While offering scalability, the system necessitates robust security measures to address data privacy concerns. The reliance on historical data also necessitates continuous model monitoring and retraining to mitigate potential biases. This research contributes a practical framework for e-commerce businesses seeking to leverage data-driven insights for improved performance.
Authors - Vangala Thanusree, Rekapalli Bhagya Srilakshmi, Kanikireddy Harshitha, Sushama Rani Dutta, A. Pranathi, Boga Sudharshini Sree Abstract - Crime is a persistent social issue that impacts public safety and urban development. With the rise in data availability and machine learning techniques, predictive modeling of crime rates has become a valuable tool for law enforcement and policy planning. We suggest a combination machine learning strategy in this paper that integrates both spatial and temporal data, alongside social and economic indicators such as decographic and Economic Indicators rate, literacy, and income levels, to enhance crime rate prediction accuracy. We evaluate the effectivness of multiple models, including RF, XGBoost, and a hybrid ensemble of both, on a real-world dataset comprising crime statistics from multiple Indian states. Our results demonstrate that integrating socio-economic factors significantly improves model performance, offering deeper insight into crime patterns and enabling data-driven intervention strategies. The proposed model outperforms traditional single-model baselines, achieving higher accuracy and F1 scores across various crime categories. This approach serves as a robust framework for smart policing and proactive crime prevention in high-risk zones.
Authors - Varun Mittal, Madhan Kumar Srinivasan Abstract - Artificial Intelligence (AI) is revolutionizing industries with its capabilities in automating tasks, enhancing decision-making, and providing predictive insights. A clear way to frame the current state of AI is to acknowledge that it’s still a technology. To fully leverage its benefits, whether for business or personal purposes, one must understand and learn to use it effectively and adapt workflows to align with its strengths and weaknesses. Organizations all around the world are transforming their existing systems and building new systems to leverage the power of artificial intelligence, but these advancements to enhance their businesses come with significant security challenges. These security threats pose a challenge to both the service providers (developers) as well as the customers. This paper delves into the security issues within AI that organizations and their users can face with AI systems, categorized under state-of-the-art AI security taxonomies.
Authors - Santhameena S, Shaunak Agrawal, Shobith R Prabhu, Shaurishail M Awanti, Siddharaj Dhegaskar Abstract - This work focuses on military vehicle detection using Synthetic Aperture Radar (SAR) images from the MSTAR dataset. Challenges such as speckle noise, limited data size, and classification accuracy are addressed using preprocessing techniques, dataset augmentation via Spectral Normalization GANs (SN-GANs), and a custom-designed Convolutional Neural Network (CNN). The proposed methodology achieves an accuracy of 98.1%, showcasing the potential of GAN-augmented SAR datasets in target recognition tasks.
Associate Professor and Head, Department of Artificial Intelligence and Data Science, Vidyavardhini's College of Engineering and Technology, Maharashtra, India
Thursday August 27, 2026 5:30pm - 5:32pm IST Virtual Room FGOA, India
