Authors - Dhavasironmani R.R, Maria Joel. J, Siddharth S.V, Ajith Sundaram Abstract - Marketing research is essential to get the correct information about the consumers’ needs and their changing preferences. The evaluation of the Consumer Behaviour, attitude, perception and satisfaction level has been the subject of the market research very frequently. Health Drinks indeed are essential for every individual. The quantity of intake may vary according to the age, occupation, income level, size of the family, but everyone accepts that in order to cope up with the energy demands of the day-to-day life, and to defend oneself from the polluted environment, one should definitely consume any health drink supplementary to the food intake. Preferences get converted into a habit which is hard to change. It is evidenced from the study that certain health drinks are being consumed through generations that the customers develop a high degree of brand loyalty towards that brand.
Authors - Bhuvaneswari Perumal, Vaishnavi Moorthy, Gladius Jennifer H Abstract - According to UNICEF-India, 46% of maternal fatalities and 40% of neonatal fatalities transpire during labor or within the initial 24 hours post-delivery. Antepartum care includes routine surveillance, assessment of risks, and appropriate actions to enhance the health of the mother and fetus. Intrapartum care provides for safe labour and delivery with surveillance and appropriate management of complications by skilled personnel.This study aims at identifying the importance of holistic care in these stages and how it helps in preventing complications through the identification of high risk pregnancies which will be useful in avoiding the development of severe problems in future. The study uses analytical tools and Machine Learning models to analyze the health data and risk factors of pregnancy. In existing state of art they have inadequate early risk prediction with poor personalization. So the collected data includes several risk factors identified and classified based on their level of risk. The results of the attempts of applying various machine learning models and EDA methods to define the most important risk factors. This is a very large reduction and in line with the United Nations Sustainable Development Goals for the year 2030.The aim is to reduce maternal and neonatal morbidity and mortality. Lack of adequate management of intrapartum care can lead to postpartum problems to a large extent and thus affect the prenatal and fetal well-being.
Authors - Yash Sharma, Bramhansh Agarwal, Sindhu Chandra Sekharan, C. Kavitha, S. Umamaheswari Abstract - In recent years, interior design has played an increasingly important role in improving the look and usability of residential and commercial spaces. Although professional designers are often employed for this purpose, the process can be time-consuming and costly, with limited flexibility for personalized input. To address these limitations, an AI-assisted solution has been developed. This system employs Conditional Generative Adversarial Networks to analyze photographs of indoor environments alongside text descriptions that reflect user preferences such as desired furniture style, color schemes, and spatial arrangements. After processing the information, the tool provides a range of design suggestions tailored to the user’s specific needs. This method eliminates the need for repeated consultations and allows for rapid generation of unique, realistic interior layouts. The approach supports a more inclusive and affordable design experience, enabling individuals to explore personalized decor ideas efficiently. By merging visual data with linguistic inputs, the system presents a novel pathway for intuitive and responsive interior design support.
Authors - Puja Cholke, Om Yogesh Suhagir, Maroof Mustaq Mohammed Gadiwale, Srushti Pancham Mane, Sanika Suresh Mohite, Shreya Ramesh Phalke Abstract - Snake bites pose a severe public health risk, especially in rural and tropical regions, where delayed treatment often leads to fatalities. Existing systems struggle to classify snakes accurately based on symptoms, causing delays in administering the correct antidote. To address this issue, BiteSage (Snake Bite Antidote Suggester) utilizes data science and machine learning to classify snake bites as venomous or non-venomous based on user-reported symptoms and recommend the appropriate antidote. A chatbot interface assists users in symptom formulation and provides real-time counseling. Additionally, the system offers visualization tools to analyze global trends in snake bites, enhancing awareness and preparedness. The model ensures high precision in bite classification and antidote recommendations, backed by comprehensive data analytics. This research benefits medical professionals in remote areas and educates the public, helping to reduce fatalities and improve emergency response. By integrating AI-driven analysis, real-time assistance, and data visualization, BiteSage enhances medical decision-making and public awareness, ultimately saving lives.
Authors - Kruthiga S, Sindhu Chandra Sekharan, H.Summia Parveen, C. Kavitha, S. Umamaheswari Abstract - The transformative potential of deep learning techniques to revolutionize the landscape of medical image analysis, enabling accurate and efficient multi-disease prediction across a spectrum of critical health conditions. This work provides a solution to the early detection challenge of disease through prediction for Tuberculosis, Pneumonia, Glaucoma, and Brain Tumors using deep learning methods. By leveraging the expressive power of convolutional neural networks and transfer learning strategies, we have developed a robust framework capable of learning intricate patterns and subtle features indicative of diseases such as brain tumor, glaucoma, pneumonia, and tuberculosis. Through meticulous data preprocessing, model selection, and rigorous training and validation procedures, our approach ensures the reliability and generalizability of disease predictions, offering clinicians a powerful tool for early diagnosis and personalized treatment planning. The integration of Python programming language facilitates seamless implementation and deployment of our framework, making it accessible to healthcare practitioners and researchers alike. Overall, our study represents a significant advancement in the field of medical image analysis, with the potential to improve patient outcomes and revolutionize healthcare delivery on a global scale.
Authors - Pranay Meshram, Prakash Prasad Abstract - In the rapidly evolving digital landscape, data security has become paramount, necessitating innovative encryption techniques that balance computational efficiency with robust protection. This research introduces the New Efficient Selective Encryption Algorithm (DSEA), a novel approach to selective text encryption that addresses critical challenges in current cryptographic methods. By leveraging intelligent message analysis and strategic encryption, by providing a robust approach to safeguard valuable information at the same time as minimizing resource usage, DSEA addresses the need for privacy in a progressive manner. The approach utilizes proximity to structural properties of the message, such as the ratio of alphabetic characters, presence of vowels, and semantic connections, to inform the selection of encryption techniques. DSEA thus allows encryption to be applied at a more granular scale, using its identification and prioritization of sensitive text segments, which results in a significantly lower computation overhead compared to traditional techniques for full-document encryption. Experimental results show that DSEA has a better performance comparing with the existing selective encryption schemes, especially in the encryption time percentage, encryption processing time, and encryption proportion.
Authors - Bendre M. R., Vikhe V.P., Vanve G.B. Abstract - Within the carrier and business industries, there would be an ongoing demand for employees who are promoted to higher positions in the service and corporate sectors. The human resource team faces significant pressure to maintain employee commitment and motivation. Incentives such as promotions, bonuses, and wages are applied to motivate employees to feel closer to their work. The employee promotions are primarily deliberate, expressing gratitude for the employee's dedication to enhancing business standards, ensuring team competency, preventing talent from seeking other opportunities, and upholding the excessive degree of overall performance, all through the assessment year, human resources gather a significant quantity of facts on all elements of worker engagement events and activities. The data collected is continuously expanding in terms of employee service, but it is of little value if it does not provide meaningful insights. As a result, machine learning plays a crucial role in human resource analytics by extracting valuable information from collaborative employee data. The issue lies in the conventional approach to promotion, which is both time- and resource-intensive due to the numerous steps required for segregating and promoting employees. This had a significant impact on the smooth transition of employees into their new positions. Because of this reason, it's miles greater sensible if human assets can predict which workers are more legal and appropriate for advancement or upgrade, earnings increase, and so on. This research aims to propose or expect worker promotion. Utilizing machine learning techniques to forecast which employee might be eligible for a promotion, contingent on the data gathered and their previous achievements. To determine the likelihood of advancement probabilities the classification algorithms together with decision trees (DT), logistic regression (LR), random forests (RF), and k-means clustering are considered broadly utilized within the field. The k-nearest neighbors (K- NN), random forest (RF), and decision tree (DT) classifiers are applied to make the expected forecast.
Authors - Nishant Survase, Chitti Saharsh, Sachin Dhadwe, Krishnadeep Thakare, Yash Ishwarkar, Nilesh Pinjarkar Abstract - Railway stations, key transportation nodes, frequently have complicated layouts that disorient travelers, leading to delays. Conventional signage alone is not enough for effective navigation, particularly with increasing urban populations. Augmented Reality (AR) becomes a solution, superimposing virtual, step-by-step directions onto actual views through smartphones or AR glasses. This paper explores AR's capability to improve navigation in stations by combining GPS, GLONASS, and adaptive machine-learning algorithms. Both marker-based and markerless AR approaches, combined with realtime locationing, also offer custom guidance. Analytics of learning further refine user engagement, with increased feedback mechanisms as well as operational effectiveness. As such, AR can efficiently handle congestion, enhance accessibility for the disabled, and optimize passenger flows. Keywords: Augmented Reality (AR), Railway
Authors - Prafulla Bafna, Punam Nikam Abstract - Mental illness can be the reasons of extreme behavioral, emotional, and physical health issues. Majorly there are 4 mental disorders which are based on disposition, uneasiness, identity and insanity. Most of the times symptoms pertaining to these mental diseases are common. But remedies on each mental disorder is different. Due to the commonly existing symptoms of each disease, identifying the exact type of mental disorder is difficult. To smoothen the process of identifying exact mental disorder we use machine learning algorithms. The algorithms are executed on 1020 patient records containing nine parameters which show mental status such as l consciousness level, general behavior, and so on. To predict the exact type of mental clutter/disorder , KNN and SVM are implemented using 80 :20 ratio of training-to-testing data. SVM proved to be more accurate that is low misclassification error and greater recall. The accuracy of prediction is steady for 300 to 1020 records.
Authors - Supriya, Ananya G Bhat, Chandana B A, Niharika P Abstract - This study seeks to enhance the accuracy of credit card fraud detection by utilizing advanced machine learning techniques, with a specific focus on the XG Boost algorithm. Various ML approaches, including Decision Trees, Logistic Regression, Naive Bayes, Random Forest, and XG Boost, are evaluated for their efficiency in detecting fraudulent transactions using patterns derived from historical data. Recent advancements highlight the integration of diverse authentication methods and randomized training datasets to mitigate vulnerabilities in fraud detection systems.
Authors - Sanchit Prashant Joshi, Parth Atul Gargate, Yash Prabhakar Apotikar, Rupesh C Jaiswal, Mousami V. Munot Abstract - Social media platforms operate at top speeds when transferring image-based data. The shared and posted images and videos on WhatsApp and Instagram consume the majority of network resources. Lossless compression techniques were applied to images while maintaining image quality throughout data storage and transmission processes because this fundamental method produces perfect information reconstruction after decompression. The research evaluates Predictive Coding (DPCM) and Context-Based Coding and Arithmetic Coding and Dictionary-Based Techniques (LZW) and Block-Based Compression through analyses of their efficiency metrics and computational complexity and practical usage. New developments in JPEG2000 and LZW compression have led to increase speed and efficiency through Parallel Symbol Encoding in Arithmetic Coding and Compression Ratio Prediction. The Optimized Run-Length Encoding (ORLE) system uses dynamic compression approach adaptation according to image orientation to enhance its flexibility. The speed of real-time applications increases remarkably when using FPGA implementations. This survey examines trade-offs among compression ratio together with computational expense and suitable data sets to perform an evaluation between classical and modern methods. Future development in lossless data and image compression relies on emerging trends such as AI-driven compression models as well as hardware-accelerated algorithms and hybrid frameworksDeflate is the fastest compression technique taking about 0.043 seconds, with a Maximum compression ratio of 26.66 given by WebP.
Authors - Anuj Sudhir Kulkarni, Rama Gaikwad, Prathamesh Zad, Sai Lahane, Shivam Shelke, Saurav Jadhav Abstract - The rapid development of artificial intelligence (AI) is changing the financial landscape. It offers innovative solutions to optimize personal financial management and advisory services. This research focuses on developing an AI-based platform to improve financial decision-making by analyzing users' investments to provide insights into financial health. Key features include Portfolio Visualizer, Risk Radar, Fundamental Analyst, Price Forecaster and Financial advisory services ensure a comprehensive view of financial planning, emphasizing AI frameworks and interpretable applications. To build user trust and transparency, challenges such as mitigating bias are explored. Real-time problem solving and fine-grained scalability with a commitment to accessibility and precision This research highlights the ability of AI to democratize financial advisory services. and overcome limitations in the current system. Future directions include real-time risk assessment. Advanced portfolio management and innovative AI integration for dynamic market simulation.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room AGOA, India
Authors - Mohan S G, Abhilash K Raj, Nayana S A, Pradhaan S, Rajendra Bhat Abstract - This paper explores the application of the You Only Look Once (YOLO) v11 model for real-time object detection in Indian road conditions, addressing challenges posed by unconventional objects like animals, autorickshaws, carts, and tractors. A dataset from dashcam and mobile footage was annotated using the Computer Vision Annotation Tool (CVAT) tool and combined with COCO to train YOLO v11. The model significantly improved detection accuracy, increasing classes from 30 to 108. Its high accuracy and real-time performance make it suitable for autonomous vehicles and traffic monitoring in India.
Authors - Vani E S, Gourav Subnani, Prajwal Gupta, Shivee Jaiswal, Mihir Sahu Abstract - Plant species classification accuracy is crucial for biodiversity conservation and ecosystem monitoring. Traditional taxonomy-based methods, which rely heavily on expert analysis, can be inefficient and prone to errors, particularly when processing large datasets. This study leverages deep learning and machine learning techniques to automate plant species identification, with a strong focus on leaf vein morphology analysis. The proposed approach begins with preprocessing leaf images by converting them to grayscale, extracting significant structural features, and skeletonizing vein patterns. Key morphological characteristics, including vein distributions, textures, and geometric attributes, are then used as input for classification models. They use both sophisticated deep learning models like Convolutional Neural Networks (CNN) and more traditional machine learning approaches like Random Forest (RF), k-Nearest Neighbours (kNN), and Support Vector Machines (SVM). The Xception architecture, known for its depth wise separable convolutions, is particularly effective in capturing intricate vein structures, enhancing classification accuracy. This automated system reduces the dependency on manual identification efforts, making it scalable for large-scale biodiversity research. By integrating deep learning-driven analysis, the proposed framework provides a robust and efficient solution for plant species classification, aiding conservation initiatives and ecological studies.
Authors - Sangita Lade, Muhammad Parkar, Shreyas Nagarkar, Om Shintre, Shivam Padalkar Abstract - The rapid evolution of machine learning (ML) has transformed industries by enabling automation, prediction, and optimization for complex real-world problems. However, developing ML pipelines involves repetitive tasks such as data preparation, model building, and evaluation, which are time-consuming and prone to errors. This paper introduces an automated system for generating ML code using Jinja2 templating and supervised MLbased feature prediction. The system analyzes 5000 ML code templates to extract parameters like data type, preprocessing techniques, model architecture, and hyperparameters. A supervised ML model predicts missing parameters based on partial user input, enabling dynamic code generation. The framework supports diverse data formats (tabular, image, text) and ML tasks (classification, regression). Experimental results demonstrate high accuracy in parameter prediction and significant time savings (70-80% reduction in setup time). The system simplifies ML development, reduces errors, and accelerates experimentation, making it accessible to researchers, developers, and students.
Authors - Rohini T.V, Srikrishna Adiga G, Tejas C, Sunil Mashyale, Sunil Kumar C Abstract - DevLaunch is a cloud-native deployment platform purpose-built for MERN stack apps, using AWS Amplify to make hosting and configuration easy. The platform provides real-time monitoring, auto-resource provisioning, and a CDN-tuned Next.js frontend to abstract away deployment nuances. In addition, DevLaunch increases developer efficiency by reducing the need for manual setup and cutting deployment and debug times by 40% and 30%, respectively. The auto-scaling architecture and simplicity of the system make it a secure and highly scalable way to deploy contemporary web applications.
Authors - Namrata Jangam, Nipun Jadhav, Riya Chavan, Priya Chavan, Rutuja Surve Abstract - Time-honoured treatment has long relied on pharmaceutical plants as genuine remedies due to their bioactive compounds. With increasing demand for natural products and sustainable healthcare, accurately identifying and classifying these plants is crucial. However, distinguishing species is challenging due to similar physical traits and varying environmental conditions. Machine learning (ML) and deep learning (DL) have shown substantial ability in medicinal plant detection and classification by analysing large datasets and extracting subtle features. Image recognition techniques, particularly convolutional neural networks (CNNs), can identify morphological traits like leaf size, shape, and texture for classification. Studies have demonstrated that CNN models can achieve up to 90% accuracy in medicinal plant identification, enhancing the process for novel drug discovery and therapeutic applications.
Authors - Ananya Kini, Saranya Rubini Abstract - In recent times, there have been several advancements in computer vision and image processing, and when combined with machine learning models, is very helpful in posture recognition applications. Posture detection is a useful tool in sports and fitness, as it helps people avoid injuries caused by poor alignment and achieve optimal posture in order to stay healthy. This paper reports on ”PoseNet : A Novel YOLODriven Framework for Badminton Posture Detection and Correction”, which is a Python-based application that utilizes Roboflow for dataset construction, annotation and augmentation, YOLOv5 for custom training the model on the dataset, and MediaPipe for giving corrective suggestions to the user. The novelty of this framework lies in its dual-stage architecture, combining YOLOv5 for classification and MediaPipe for real-time correction, specifically tailored for badminton. Additionally, it leverages a badminton-specific dataset, ensuring domain relevance and precise analysis. It predicts the stance that the player is planning to achieve and then tells whether the stance is correct based on their key points. The model obtained a high classification accuracy, with mAP50 value of 96.2% and mAP50-95 value of 81.1%.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room AGOA, India
Authors - K. V. Deshpande, Sanskruti Parkhe, Varad Pawar, Vaishnavi Thorat, Rutuja Bagad, Priti R. Kale Abstract - In today's digital world, cyberattacks targeting critical infrastructure pose a significant threat to government agencies and organizations. These attacks can disrupt essential services and compromise national security, making it crucial to identify and respond to them quickly. This survey paper discusses the challenges faced in monitoring cyber threats and presents a proposed solution: a real-time cyberattack monitoring tool. This tool uses machine learning and web scraping to gather data from various online sources, storing it in a structured format for easy access. By visualizing the collected data through an interactive dashboard, cybersecurity teams can quickly identify and understand the nature of ongoing attacks. Additionally, the system includes an alert mechanism that notifies teams of high-frequency attack patterns, enabling prompt action. Overall, this solution aims to enhance the ability of organizations to protect their critical infrastructure by providing timely insights and effective incident response strategies.
Authors - Garima Ratra, Akriti Kumari, Vimmi Malhotra Abstract - Blockchain has been widely adopted across numerous industries and applications to improve privacy and security factors. However, with the rapid expansion of this technology, its significant energy consumption has become a growing concern, particularly in mobile cryptographic applications. Traditional consensus mechanisms, such as Proof-of-Work (PoW), require substantial computational power, making them unsuitable for mobile environments. This paper reviews innovative blockchain protocols that prioritize energy efficiency while ensuring security and decentralization. By analyzing alternative consensus mechanisms including Proof-of-Stake (PoS), Delegated Proof-of-Stake (DPoS) and energy optimization strategies, we assess their effectiveness in lowering power consumption. The study highlights the role of sustainable blockchain approaches in enhancing mobile application efficiency with minimized environmental impact. This will help in increasing the energy efficiency and understanding the impact and applicability of blockchain by switching to greener systems.
Authors - Kavya Soni, Sujal Rajput, Babita Tiwari, Chirag Joshi, Gaurav Kumawat Abstract - Surface water quality is essential for ecological stability and mortal health, but it faces growing pitfalls from urbanization, industrialization, and husbandry. Traditional in-situ monitoring styles are essential yet limited in their spatial and temporal compass. This paper aims to provide a comparative analysis of different techniques available for surface water quality analysis. We have analysed studies grounded on freely available satellite data from Landsat, Sentinel- 2, and MERIS to determine crucial water quality parameters similar to chlorophyll- at attention, turbidity, and dangerous algal blooms. The review demonstrates the effectiveness of various methods to use spectral imaging to predict parameters such as BOD, chlorophyll content in water. Further to this multi-sensor data integration within the pall calculating platform Google Earth Engine aids in dynamic water quality assessments. Results indicate these technologies indeed give scalable low-cost observers of submarine ecosystems and implicit means of filling gaps between in- situ measures and comprehensive water resource operation. The study identifies implicit in the integration of a Civilians approach grounded on remote seeing in climate modelling, monitoring of ecosystem health, and sustainable water governance.
Authors - G. Indhumathi, G. Saranya, S. Riju Sundar, S. Paul Joseph Abstract - Sericulture or silkworm breeding for silk is faced with the challenges of maintaining the ideal environmental conditions, feeding patterns, and disease recognition. Manual and improper monitoring lead to compromised production and quality. This project introduces the implementation of an Augmented Reality (AR)-based real-time system for sericulture management using the intersection of IoT and AI. The system keeps tracks of temperature, humidity, and feeding patterns and presents real-time visualization of data in an interactive AR platform. An AI subsystem identifies diseased silkworms via image processing, annotates them in AR, and recommends treatment. Predictive analysis also maximizes environmental conditions and feeding patterns for maximum production effectiveness. The uniqueness of the system is its interconnection of AR, AI, and IoT that provides easy monitoring, automatic detection of diseases, and data- in-formed decision-making. The utilization of the system enhances the quantity of silk yield, product quality, and saves labor, and its disruptive contribution to sericulture management is evident through innovative technologies.
Authors - Geetanjali Popat Rokade, Sonali Patil Abstract - The pressing need for secure, private, decentralized frameworks for machine learning in healthcare has been fueled by the increasingly popularization of Federated Learning (FL). In conventional FL, the aggregation is centralized, allowing potential data leakages or model-poisoning attacks against a central point of failure. A possible solution to these aforementioned impediments is Blockchain-Based Federated Learning (BDFL), as such a setup can utilize the immutability, transparency, and distributed consensus of the blockchain to enhance security and achieve better performance. Nevertheless, the existing review articles have not offered a thorough investigation of BDFL consensus algorithms, their specific applications to the healthcare sector, and an iteratively empirical performance evaluation of their efficiency, scalability, and robustness. This paper provides a systematic and empirical review of state-of-the-art BDFL consensus programs in their application to health care; it analyzes these programs' performances based on consensus efficiency, incentive mechanisms, privacy-preserving capabilities, and computational scalability. Key approaches examined in this study include Proof-of-Contribution (PoC) [2,3], Byzantine Fault Tolerance (BFT) [5], DAG-based Blockchain FL [4,13], Multi-center Federated Learning (MCFL) [24], and Proof-of-Accuracy (PoAcc) [20]. The reason for this focus is that these methods best integrate security, efficiency, and fairness in the context of decentralized health data cooperation. The results indicate that MCFL models would optimize institution-wise healthcare cooperation, PoAcc would optimize the accuracy of medical diagnosis, and the DAG-based blockchain would guarantee high throughput scalability for FL. This review sets out an extensive framework for selecting the best models in BDFL, which will encourage developments in AI-nurtured healthcare data analysis, clinical decision support, and secure EHR management. This study's findings will propel future advancement in federated learning security, quantum-safe consensus mechanisms, and hierarchical blockchain architectures for global health applications.
Authors - Parvathi NB Panicker, Bhadra R, PR Mahadevan, Vandana Madhavan Abstract - Diversity, Equity, and Inclusion have integrated into organizations through incorporations in their Strategic Plans. The presence of a globally dispersed workforce in the IT/ITES sector implies that these strategies are particularly vital in those organizations. Many organizations made pronouncements of publicly declaring their DEI initiatives; however, usually a difference exists between such declarations and the experiences of the employees. This study investigates the given DEI initiatives in IT/ITES organizations through two lenses: namely, by organizational disclosures as well as employee perception. The qualitative research methods involved the gathering of data with corporate DEI reports, sustainability statements, and employee-generated reviews through semi-structured interviews with employees. Thematic analysis reveals leading gaps of representation of leadership, equity in progression of careers, and inclusion incidences in the workplace. Diversity is preached at entry-level but drops off in representation at leadership levels. Promotion and pay equity remain as sticking issues: underrepresented groups tend to progress in their careers at slower rates. Employees considered organizational DEI commitments as more aspirational than actual, with workplace inclusion and psychological safety differing in various organizations. Employees expressed skepticism because many DEI efforts do not set measurable success metrics. The study also underscores that organizations should go beyond performative DEI efforts by incorporating employee feedback, installing structured mentorship programs, and adopting outcome-based DEI evaluation systems
Authors - Sohana R, Niharika R, Khushi Shah, Tanya Singh, M Shahina Parveen Abstract - The project majorly includes a methodology to create an AI - driven career counselling platform that can be used to recommend various career options for students (focusing on starting to give them more exposure from a younger age. So that they can incorporate the necessary skills required or in general know what is in it for them in every career option available) based on every individual's profile and varied interests. We utilize artificial intelligence to make sure we can provide personalization of suggestions. The platform takes factors like the interests of students, their strengths and what kind of work environments they would want to work in, and then evaluates a list of suitable options. There are also prevailing recent studies that indicate that such systems powered by AI have enhanced the accuracy and reliability of career counselling services by a great extent especially by analyzing extensive behavioral and educational data. Upon this our platform utilizes augmented reality for simulating real- world career environments, making sure that students get a chance to explore their potential career paths by interactively taking part in the simulations. There has also been extensive research that has demonstrated that Augmented reality-based tools on the whole improve and provide enhancement in immersion, hands on experiences and helps with better exploration for various professions. Therefore, we want to merge AI and AR to arrive at best of both worlds and hence approach this problem by providing with an innovative platform that fosters informed decision making and comprehensive career exploration among students. Ultimately our platform's mission is to spread awareness and to align the aspirations that students have with their career paths and to lead to the overall improved educational and career outcomes and job satisfaction.
Authors - Janwale Asaram Pandurang, Minal Dutta, Savita Mohurle, Vaduguru Venkata Ramya Abstract - This study investigates the classification of images of spine X-ray into three groups: Normal, Scoliosis, and Spondylolisthesis, deep learning models improves with attention mechanisms. A labelled dataset of X-ray images was working, addressed with imbalances class through oversampling techniques. Pretrained convolutional neural network (CNN) models, including Xception, InceptionV3, and DenseNet, were fine-tuned for this categorised task. The combination of attention mechanisms enhanced interpretability of model and precision score. Working with the models, InceptionV3 achieved perfect accuracy, outperforming Xception and DenseNet. The findings insides the efficacy of attention-based deep learning approaches with potential applications in clinical diagnostics, in medical image classification, for spinal conditions.
Authors - Yogesh K. Sable, Rajesh Kumar Kashyap, Sagar Satpute Abstract - Microgrids have emerged as cutting-edge and game-changing energy solutions, providing a plethora of benefits in the search for a robust and sustainable energy future. In-depth examination of the many facets of microgrids is provided in this review, with specific consideration paid to their capability in the mix of sustainable power sources, support for charge and e-portability, and contribution in a debacle readiness and flexibility. The topic of conversation is the arrangement of limited energy frameworks by means of microgrids, which might work both autonomously and related to the essential electrical network. They successfully consolidate environmentally friendly power assets, like sunlight powered chargers and wind turbines, and advance the development of electric vehicles through wise accusing and connection of the framework. Additionally, because of their intrinsic resilience, they may keep operating in the face of grid failures and natural disasters, supplying crucial backup power to crucial facilities. Case studies highlight the real-world uses of microgrids in various contexts and highlight their potential effects on environmental sustainability, cost savings, and energy efficiency. The improvement of microgrids is expected to assume a significant part in making versatile and maintainable energy framework as the globe faces rising environment related concerns.
Authors - Jaden Ekbote, Sheshank K Patil, Ramakrishna S, Nalini C Iyer Abstract - In the era of 5G, the dual imperatives of high performance and energy efficiency have led to the development of sophisticated network management techniques. This paper introduces an innovative slice-aware energy optimization framework that leverages simplicial homology to model and analyze network coverage. By representing base stations as vertices in a simplicial complex and encoding overlapping coverage as higher-dimensional simplices, the approach captures connectivity and potential coverage gaps through homological invariants. An optimization algorithm is then formulated to minimize overall power consumption while fulfilling stringent slice-specific quality-of-service (QoS) constraints for enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC). Extensive simulations in MATLAB demonstrate the viability of the proposed method, showing significant power reductions over baseline uniform allocation schemes without compromising slice performance. This work underscores the potential of topological methods in addressing the energy challenges inherent in next-generation network deployments.
Authors - Aditya Poddar, Soham Sarkar, Ananya Hegde, Shravya Reddy, Animesh Giri Abstract - As climate change accelerates, there is an urgent need for solutions that balance ecological responsibility with economic incentives. While capping carbon emissions is widely recognized as essential, it remains a challenging task to quantify carbon sequestration correctly and ensure complete transparency in carbon credit markets. The increasing demand for effective carbon sequestration measurement and transparent carbon credit trading demands an innovative approach using advanced technologies. This research focuses on applying big data using Kafka for parallel data streaming in a distributed environment, together with machine learning models to optimize the prediction of carbon capture, integrating blockchain technology which provides security and transparency in transactions involving the carbon credit market. Through our research, we aim to provide an interdisciplinary framework that will improve the accuracy and scalability of carbon sequestration predictions, building trust and accountability in carbon trading to support a more sustainable and economically viable future.
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room AGOA, India
Authors - S. T. Patil, Gaurav Sulsule, Urmila Kakarwal, Sanika Kolawale, Prathmesh Deshmukh Abstract - This paper suggests a deep learning-based solution for real-time detection of drowning and slipping accidents through computer vision. The system, which is grounded on the YOLOv8 (You Only Look Once) model, offers effective and efficient detection by analyzing video streams in real-time to detect dangerous incidents in settings such as swimming pools, building sites, and home homes. The system has a web-based user interface, real-time alerting capabilities, and SQLite database for storing data. The model was trained and tested with a large set of labeled images with an emphasis on balancing detection performance on frequent and infrequent incident classes. The results include robust detection performance with few false negatives and positives, fast response times, and effective processing of multiple video feeds. Despite problems with dataset imbalance and integration complexities, the system offers a cost-effective solution for enhancing safety, minimizing human error, and enhancing real-time monitoring capability. The research suggests the viability of AI-based solutions for safety-critical domains, with advantages of automated incident detection over conventional surveillance techniques.
