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.
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 - 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.
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
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
