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.
