Authors - K Shailaja, Shirina Samreen Abstract - Diabetes mellitus is becoming an increasingly critical health issue across globe that demands early diagnosis and continuous monitoring. Although traditional machine learning models have been employed to predict diabetes, they frequently lack clarity and have difficulty in identifying uncommon or rare patient cases. This research proposes a novel explainable hybrid framework that combines deep learning-based temporal modeling with classical machine learning and unsupervised anomaly detection. The framework utilizes multimodal data sources, including static clinical features, time-series Electronic Health Records (HER) and wearable sensor data, for robust diabetic risk assessment. Explainability is achieved using SHAP (SHapley Additive exPlanations) and counterfactual reasoning to provide both global and local interpretability. In addition, an autoencoder-based novelty detection module identifies patients whose health patterns deviate significantly from the normal. Experimental results on benchmark datasets demonstrate improved prediction accuracy, better anomaly identification and enhanced interpretability making the model suitable for real-world clinical decision support systems.
Authors - Vishal Ambhore, Ketki Kshirsagar, Parikshit Mahalle Abstract - This survey paper thoroughly examines the landscape of access control within the context of the Internet of Things (IoT). With the rapid expansion of IoT technologies, ensuring secure access to resources and data has become a critical concern. We thus present a very wide-ranging review of existing literature, doing an all-round analysis of current access control mechanisms in order to discuss their strengths, weaknesses, and applicability to IoT environments. The result of our investigation shows few major gaps and challenges, such as issues of scalability, interoperability concerns, and a call for access policies that account for contexts. Taking this into account, we introduce new approaches and improvements to positively address these shortcomings and boost the security and efficiency of access control in IoT systems. By integrating findings from multi study research endeavors, we provide new points of view and approaches toward IoT security improvement. Furthermore, we present potential future research directions and challenges to guide development for more resilient and adaptive access control solutions in IoT ecosystems. The paper is a ready reference for researchers, practitioners, and policymakers who want to bolster the security posture of IoT deployments and mitigate newly emerging cyber threats effectively.
Authors - Garima Ahuja, Heena Hooda, Vimmi Malhotra Abstract - The combination of agriculture and Information and Communication Technologies (ICT), known as e- Agriculture, is changing farming practices, especially in regions facing limited resources and climate challenges. e-Agriculture includes digital tools such as mobile-based advisory platforms, Geographic Information Systems (GIS), Internet of Things (IoT), Artificial Intelligence (AI), and data analytics. These technologies provide timely information, improve the use of inputs, and increase access to markets and financial services [1]. Applications of ICT in farming across different regions show improvements in crop planning, yield monitoring, and risk reduction. Evidence from India, Kenya, and the Netherlands highlights positive results where digital tools are adapted to local needs. Improvements include better harvest management, lower post-harvest losses, and increased climate resilience [3]. Despite these benefits, challenges such as poor rural connectivity, low digital skills, and limited policy support continue to affect the full use of such technologies. Scaling e-Agriculture requires affordable access, local training, and supportive ecosystems. Broader adoption can support sustainable farming systems and contribute to goals such as food security, poverty reduction, and environmental protection.
Authors - Khloe Bhel, Krutthika Hirebasur Krishnappa Abstract - Healthcare data growth at an exponential rate together with rising cyber threats require sophisticated cryptographic systems to protect Electronic Health Records (EHRs) from unauthorized access and data tampering. The research develops a cryptographic system which uses simulated quantum key distribution through the E91 protocol to generate symmetric encryption keys that is encrypted using Advanced Encryption Standard (AES) in Cipher Block Chaining (CBC) mode to protect healthcare data. The system operates by using IBM Qiskit’s AerSimulator backend to create entangled qubit pairs for deriving a quantum-safe key between two communicating parties. The entropy measurement of the generated key approaches the maximum value of randomness which provides effective protection against brute-force attacks. The AES encryption process using achieves rate of approximately 6.58 MB/sec during encryption operations and 9.34 MB/sec during decryption operations. The proposed method demonstrates efficient computation and deployment potential for healthcare applications with limited resources including edge-based IoT medical devices and federated learning systems. Security analyses show that this approach gives a good protection against both classical attackers and near-term quantum attackers. The research shows how post-quantum cryptographic methods can be practically used to protect future healthcare systems.
Authors - Suprit V. Hatti, P. G. Sunitha Hiremath, Manohar Madgi, Neha Tarannum Pendari Abstract - This study presents a comprehensive analysis of the factors influencing cognitive decline in Alzheimer’s patients in the USA. The objective is to examine cognitive decline and dementia prevalence across various U.S. regions, focusing on gender, age, and race/ethnicity disparities. Using data from the Behavioral Risk Factor Surveillance System (BRFSS), collected from 2015 to 2021, 59 locations were categorized into Northeast, Midwest, Southeast, Southwest, and West regions. The original dataset contained 31 attributes. The analysis included year-wise trend examination, gender-wise, age-wise, and race-wise distribution assessments, and identification of key risk factors. The Midwest (21.98%) and West (21.83%) showed higher cognitive decline rates, with significant disparities across demographics. The ‘Overall’ age group showed the highest prevalence at 8.54%. Females were most affected, with diabetes, asthma, arthritis, depression, and cardiovascular diseases being the most correlated comorbidities.
Authors - Reshma Y Totare, Anushka Kurandale, Sakshi Kuyte, Kiran Mane, Snehal Nale Abstract - With the increasing reliance on online reviews across various digital platforms, user feedback has become a vital factor in influencing public perception and decision-making. Since users cannot physically verify products or services online, they often depend on reviews to assess quality and credibility. This dependency has led to a rise in deceptive practices, where fake reviews are used to mislead audiences—either by promoting certain offerings or undermining competitors. Detecting such fraudulent content presents a significant challenge in the field of natural language processing (NLP), due to the subtle and human-like nature of these reviews. In this project, we present an approach for fake review detection using a deep learning model that combines Long Short-Term Memory (LSTM) networks with Bidirectional Encoder Representations from Transformers (BERT). Our model utilizes LSTM’s ability to capture long-range dependencies along with BERT’s contextual language understanding to enhance detection accuracy. To improve practicality and trustworthiness, we incorporate several additional features plugin support for easy integration into various review-based platforms, multilingual capability to handle reviews in different languages, and LIME (Local Interpretable Model-agnostic Explanations) to provide word-level interpretability of predictions. We evaluate our model on publicly available datasets containing both real and fake reviews, and the results demonstrate that our LSTM-BERT approach significantly outperforms traditional machine learning techniques. This work contributes to the growing efforts in combating misinformation and enhancing the credibility of online content across diverse platforms.
Wednesday August 26, 2026 9:30am - 11:30am IST Virtual Room EGOA, India
Authors - Ajay Talele, Siddhi Shingate, Om Gaikwad, Mahek Sayyad, Sai More, Pooja Nanaware, Ashwini Borole, Pratiksha Bile, Bharat Bangar Abstract - Crime prevention is being transformed by the Internet of Things (IoT) through proactive, smart solutions. Abstract This paper presents some techniques at the time of crime prevention by the use of IoT. Through the use of IoT driven surveillance systems, integrated smart sensors, real-time data aggregation and analytics, and automated alerts to emergency services and law enforcement, crime prevention, identification, and response are improved significantly. AI in Smart City: The study also calls out real-time examples such as smart cities establishing AI powered CCTV, IoT-enabled access control, predictive policing[1] The contemporary world faces the overwhelming challenge of crime. Criminal activity exists in almost every country and the statistics for certain nations are quite alarming. The advancement of technology has been one of the most significant factors in the development of crime control and prevention measures, such as drone surveillance, GPS tracking and tagging, closed circuit television cameras, etc. Technological advances such as the IoT (Internet of Things), Machine Learning, and Edge Computing call for the attention of the scientific world in regards to the question: how can they utilize these innovations for the purpose of minimizing criminal activities globally? In conclusion, the research results reflect on the employability of AI and IoT technologies in reinforcing security and obstructing offences against state and equally, challenges and ethical issues that accompany their employments [2]
Authors - Sambhram Pattanayak, Archana Paswan Abstract - The evolving landscape of dialogue replacement in film and television production is examined, focusing on advancements and challenges in on-location Automated Dialogue Replacement (ADR) recording and the emergence of real-time dialogue replacement technologies. Advancements in portable recording equipment, microphone technology optimized for field use, and specialized software solutions have significantly enhanced the feasibility and quality of ADR conducted outside traditional studio settings. However, on-location ADR presents unique challenges related to acoustic control, environmental noise, actor availability, logistical complexities, and technical limitations. The paper also examines the current state and potential impact of real-time dialogue replacement technologies, which offer immediate solutions and increased flexibility on set and in post-production. Case studies illustrate the practical applications of on-location ADR, while the discussion of future trends highlights the anticipated integration of artificial intelligence, advancements in hardware and software, the influence of remote collaboration tools, and the potential for incorporating virtual and augmented reality into dialogue replacement workflows. This analysis underscores the ongoing evolution of audio post-production techniques aimed at enhancing efficiency and creative possibilities in modern filmmaking. The research findings presented in this study underscore the importance of ADR in film production and introduce novel approaches and technologies. These advancements provide filmmakers with state-of-the-art tools and techniques, revolutionizing their creative processes and enhancing the quality of their productions.
Authors - Mario Castro Romero, Carlos Ernesto Carrillo Arellano, Leonardo Daniel Sánchez Martínez Abstract - Software-defined wireless sensor networks are emerging as a transformative technology for industrial, research, and IoT applications leveraging their control-data plane separation. This paper analyzes routing protocols, emphasizing their advantages, limitations, and QoS-impacting factors (e.g., latency, bandwidth, among others). Through a systematic review, we identify innovative solutions to enhance QoS in SDWSNs, addressing challenges like energy efficiency and dynamic adaptability. Previous results demonstrate that centralized routing and machine learning-based algorithms significantly improve reliability in critical applications.
Authors - Manpreet Kour, Naman Jain, Neeraj Gupta, Geetanjali Bhola Abstract - Waste recycling is important both in the global economy and in the global climate as a whole. As a result, classification of recyclable waste has become a critical goal for humanity, and deep learning models have important potential to fulfill this task. In this study, six advanced folding network models of neural networks - EfficientNetV2L, EfficientNetB1, EfficientNetB0, MobileNetV2, ResNet50, and VGG16, were compared for the effects of the garbage classification task. The results show that EfficienctNetB0 gave better performance than the other models. Furthermore, data augmentation techniques were used to improve classification accuracy, as data records contained a limited number of samples. Notably, MobileNetV2 not only achieved competitive accuracy, but also became a green choice for its low carbon emissions.
Authors - Manasi Golesar, Priti Jagtap, Kamlesh Khatod, Kshitij Malode, Vaishali Pawar Abstract - This research presents a novel approach to bot detection in web applications using behavioral biometrics and machine learning. Our system leverages a Flask based web application with a registration form as a testbed to distinguish between human and automated users. The implementation collects multidimensional behavioral data including mouse movements, typing patterns, form fill speed, and browser fingerprinting to build a comprehensive user profile.Two machine learning models, Random Forest and XGBoost, are dynamically compared for performance, with the superior model being automatically selected for deployment. The system incorporates a honeypot field as a simple yet effective first pass filter and implements progressive model learning through a database backed training pipeline that continually improves detection accuracy.Key innovations include the real time behavioral analysis during form completion, automated weekly model retraining, and an administrative interface that allows for manual labeling of edge cases to enhance the training dataset. Our approach achieves high detection accuracy while maintaining a low false positive rate, effectively balancing security with user experience.This research demonstrates that integrating behavioral biometrics with adaptive machine learning provides a robust defense against increasingly sophisticated bot attacks without requiring traditional CAPTCHA challenges that often degrade user experience.
Authors - Devarsh Damodaran, Krishna Bharathi V, Dhanya M Abstract - This study explores user sentiments towards AI-powered fitness applications by analyzing user reviews from platforms like Google Play Store. With the increasing adoption of digital health solutions, understanding user satisfaction, trust, and key concerns is crucial. Using Natural Language Processing (NLP) techniques, sentiment analysis was conducted to classify user feedback into positive and negative sentiments. Machine learning algorithms like Logistic Regression and Support Vector Machine (SVM) were utilized for classification. Findings are prominent drivers of satisfaction, where usability, effectiveness, and personalization are essential drivers, while cost, technology glitches, and unrealized expectations drive dissatisfaction. These findings give interesting insights for fitness-tech business companies and app developers to drive engagement and better experience for their users.
Authors - Silpa Raj R, Durgalashmi C V Abstract - The widespread adoption of social media enables female entrepreneurs to leverage innovative tools and strategies, fostering the development of sustainable business practices. The present study analyses how the female entrepreneurs in Kerala utilize social media (SM) in promoting sustainable innovations in their business activities. Research investigates how social media affects sustainable business practices among women entrepreneurs in Kerala, with the focus of four key variables: idea generation, customer connectivity, collaboration, and sustainable outcomes. This study aims to fills the gap by exploring how women use social media for entrepreneurial practices and adoption of sustainable outcomes. This study used a structured questionnaire to collects data from women entrepreneurs in Kerala. The variables including frequency of idea generation through social media, customer or stakeholders’ collaborations and the adoption of sustainable practices influenced by digital platforms are observed. To ensure the participation of entrepreneurs actively using social media for innovation, purposive sampling techniques were employed. The hypotheses were tested using the statistical tools like chi-square, correlation and regression analysis, providing empirical evidence on impact of SM usage among Kerala’s women entrepreneurs. The study emphasizes the significance of social networking platforms in encouraging innovative methods that contribute to sustainability via three major variables. The findings suggest practical implications for policyholders, entrepreneurs and researchers. The research adds existing corpuses of research on digital entrepreneurship and sustainability focusing on how social media improve sustainable practices.
Authors - Vineet Wagh, Srushti Chopade, Sneha Patil, Vighnesh Padwal, Sarika Kuhikar Abstract - In Institutions and schools, attendance management is a crucial task for faculty to monitor class strength. Traditional methods such as manual entry, biometrics, and RFID-based systems are commonly used, but they are time-consuming and, in the case of biometrics, potentially unhygienic. This paper presents an automated face recognition-based attendance system that utilizes preinstalled CCTV cameras to monitor student presence in real-time. The system employs RetinaFace for face detection and the face_recognition library for face encoding and matching. Known face images are preprocessed to generate face encodings, which are then compared with detected faces in each frame to determine attendance. The proposed system offers accuracy, efficiency, automation, and contactless operation while seamlessly integrating with existing infrastructure. A web interface allows users to start and stop attendance tracking, remove duplicate records, and download attendance logs in CSV format. The system demonstrates its applicability in educational environments by providing a scalable, non-intrusive, and secure solution for automated attendance management.
Authors - Madhumati Shinde, Premanand Ghadekar Abstract - Cloud computing is a dynamic part of today's high-tech framework, given that frequent welfares such as cost-effectiveness, scalability, convenience, novelty, and safety. Its impact is multifaceted, transforming competition and corporate operations in the digital age. To improve speed, optimize resource usage, and support sophisticated applications, cloud computing makes use of a variety of learning strategies. A learning technique's effectiveness in the field of cloud security depends on its ability to recognize, stop, and handle security threats. In order to identify and reduce security threats, machine learning particularly anomaly detection using supervised and unsupervised learning is crucial with advancement of federated learning. Deep learning models like RNNs and CNNs process extensive datasets to uncover intricate attack patterns, while federated learning improves privacy by training models on decentralized data sources. Reinforcement learning facilitates adaptive security strategies, continually enhancing threat responses. Security is paramount in cloud computing as it safeguards sensitive data, applications, and services hosted on cloud platforms from unauthorized access, breaches, and cyber threats.This paper highlights the security concerns in cloud environment with framework to improve the performance matrix to recognize federated cloud trust.
Authors - Ajuram. P, E. Grace Mary Kanaga Abstract - Large Language Models have demonstrated great effectiveness in generating text and images. However they can become even more efficient by perfecting the prompt given to them. This paper proposes a multi LLM framework that dynamically orchestrizes several specialized LLM models in accordance with complex user prompts. First, a primary LLM analyzes the user prompt and breaks it down into multiple sub tasks. Then, for each identified sub task with respect to its type (text to text, text to image, or image to text), a suitable LLM is assigned. The context, instructions, and the output format is also provided by the primary LLM for each chosen model. The sub tasks are executed either in parallel or in sequential order. This approach automates the workflow, optimizes model utilization, and improves response relevance, making it suitable for applications requiring multi modal collaboration and processing.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room EGOA, India
Authors - Devika Vijapur, Nidhi Desai, Aishwarya Naik, Smita Ganur, Supriya Katwe Abstract - Road accidents are one of the global safety concerns leading to loss of millions of lives every year. One of the factor leading to this is over-steering. Oversteering is phenomenon that occurs when the rear wheels of the vehicle lose grip which causes the vehicle to turn more than expected. The detection of oversteering in real-time is crucial for the improvement of vehicle safety to prevent accidents as well as for advanced driving assistance systems(ADAS). This paper presents a holistic approach to over-steering detection using a decision tree algorithm. The proposed system analyzes various vehicle dynamics parameters such as lateral acceleration, yaw rate and steering angle to identify the patterns that cause over-steering. The system incorporates collection of real-time data from Inertial Measurement Unit (IMU) sensors that enhances reliability of oversteering detection under various conditions. The model is trained from the data obtained, using decision tree algorithm and obtained accuracy of 96.08%. The hardware implementation is done by placing ESP-32 integrated with MPU 6050 and Arduino Nano 33 BLE sense accordingly in the vehicle. Based on thresholds of the parameters mentioned in the paper, oversteering is detected.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room EGOA, India
Authors - Sneha S. Temgire, Y.S. Angal, Ashwini V. Waghmare, Chetana Sharma, Ashwini Gajre Abstract - Agriculture is essential for food security and economic growth, but traditional farming faces challenges such as plant diseases, inefficient irrigation, and labour-intensive monitoring. This project focuses on automated and manual irrigation in addition with plant disease detection and growth monitoring using image processing on a Raspberry Pi 3B+. By leveraging TensorFlow Lite and OpenCV, the system can analyze plant health and trigger appropriate irrigation actions. The aim is to design accurate agriculture system by reducing water wastage and improving crop monitoring. A key feature of this system is web-based monitoring, where the Raspberry Pi transmits real-time plant health data and sensor readings to an HTML-based webpage. Users can remotely access this data via a web interface, enabling continuous monitoring of plant conditions, disease status, and irrigation control from any location. By combining machine learning, image processing, IoT automation, and real-time web-based monitoring, this system reduces manual labour, optimizes water usage, and ensures early disease detection. The web interface enhances accessibility, allowing farmers and researchers to track plant health remotely and make informed decisions.
Authors - Sauvik Bose, Rina Bhattacharya, Rajeshwari Roy Abstract - Avian monitoring is a crucial component of biodiversity conservation, providing insights into population trends, habitat changes, and environmental stressors. The fast growth of mobile telephony has raised issues regarding its potential upon the avian population, their behaviors and breeding, predominantly due to electromagnetic radiation exposure. This study investigates the feasibility of using drones for avian monitoring near mobile towers in Arambagh Municipality (22.8838° N, 87.7819° E), Hooghly, West Bengal, India, which is a semi-urban landscape with rich avian diversity and has undergone a significant growth in mobile tower installation over the last few decades. Drones offer a non-invasive, scalable, and high-resolution method for ecological monitoring, surpassing traditional survey techniques in terms of not only efficiency and data accuracy but also consuming less time and effort. A drone (model: DJI MAVIC MINI) equipped with a high-resolution camera is deployed at selected base station sites within the study area. The study pattern included regulated flight patterns, periodic monitoring. Findings disclosed noticeable behavioral variations in birds near mobile base stations. The repulsion of smaller birds to the high EMR zone has been distinctly observed along with anomalies in roosting and breeding habits. A correlation was observed between radiation levels and avian health oddities, underscoring the need for further research. In the future, research ought to be performed on in-depth monitoring efforts in urban and semi-urban areas along the different geographical landscapes. Improving drone technology for ecological studies and exploring alternative communication infrastructures with reduced environmental impact is much needed.
Authors - Dhanyashree S, Keshav S, Deepak Gupta, Shobhana Palat Madhavan Abstract - This study investigates a chain reaction triggered by cliffhangers in media consumption, focusing on their role in driving binge-watching, self-regulatory depletion, binge-eating, and reduced mental well-being. Grounded in Self-Regulatory Depletion Theory, a sequential mediation model is proposed and analyzed through serial mediation regression. Data from 170 Indian respondents revealed that cliffhangers significantly predicted binge-watching, which in turn increased self-regulatory depletion. Depletion heightened binge-eating tendencies, and binge-eating negatively impacted mental well-being. Bootstrapped mediation confirmed an indirect pathway from cliffhangers to reduced mental well-being via binge-watching and self-regulatory depletion. These findings underscore the ethical responsibility of streaming platforms to mitigate compulsive viewing and highlight interventions for mindful consumption.
Authors - Manasa S, Rupam Bhaduri, Pramod Kumar Naik, Gangadhar T G, Bharath Kumar S Abstract - This paper introduces an adaptive control strategy for a three-phase Dual Active Bridge (DAB) converter, designed to facilitate efficient bidirectional power flow in electric vehicle (EV) fast-charging stations. The proposed control method effectively manages real-time fluctuations in grid conditions and the state-of-charge (SOC) of batteries, ensuring stable operation in both Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) modes. Utilizing a dq-reference frame-based decoupled controller with SOC feedback, the solution is rigorously validated through MATLAB/Simulink simulations. The design encompasses LCL filter modeling, DAB phase shift modulation, and battery interfacing under diverse loading scenarios. Simulation results reveal significant improvements in performance, highlighting the system's ability to maintain high efficiency during both charging and discharging phases. By enhancing the responsive-ness and stability of power exchange between EVs and the grid, this research aims to contribute to the development of advanced fast-charging infrastructure capable of supporting increasing EV adoption while optimizing overall electric grid performance. The findings underscore the potential of adaptive control strategies in ensuring reliable and efficient energy management within smart grid environments.
Authors - Saraswati Patil, Kalyani Rathod, Adarsh Jayfale, Wasim Pathan, Adesh Bhore Abstract - This paper looks at how object detection technology can help blind and visually impaired people. Visually challenged individuals struggle to comprehend their surroundings, especially in outdoor settings where objects constantly shift and move. Object detection solutions can help visually impaired individuals overcome difficulties in daily life. The object detecting system aims to provide a simple, user-friendly, convenient, and cost-effective solution for visually impaired individuals. This yolov11 model has the frame process rate 45 FPS on CPU and 100 – 150 FPS on GPU .The system was tested with different objects and in various environments to see how well it works. Key factors like how accurate it was, how quickly it responded, and how satisfied users were measured. The results showed that the system was good at detecting objects and giving clear instructions to the user in real time.
Authors - Aranya G, Durgalashmi C V, Nidheesh Melethadathil Abstract - This study examines the confidence levels of healthcare and IT professionals regarding the execution of artificial intelligence (AI) in the healthcare sector. Focus on understanding the perceived impacts of AI on patient safety, quality of care, and the ethical and legal implications involved, the research employed an analysis through a detailed questionnaire, gathering responses from 50 healthcare professionals and 50 IT professionals in Kerala using judgmental sampling. Survey model and percentage analysis were used in this study. The findings indicate a mixed sentiment: a substantial proportion of respondents acknowledge the potential of AI to enhance healthcare delivery and patient outcomes, yet there remains significant apprehension concerning data privacy, potential biases, and the need for human oversight. While IT professionals generally display greater confidence and familiarity in AI technologies, healthcare professionals are more cautious, emphasizing the importance of ethical considerations and human involvement in clinical decision-making. The study suggests that bridging the gap between these professional groups through targeted education, hands-on experience, and robust governance frameworks can enhance confidence and facilitate the effective integration of AI in healthcare. Recommendations include ongoing training and clear communication about AI's capabilities and limitations to ensure both ethical application and improved healthcare outcomes.
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room EGOA, India
Authors - Ritu Ramesh Vernekar, Vijeta D Chitragar, Laxmi Koutanali, Prajwal Sangalad, Hemantaraj M Kelagadi, Suhas B Shirol Abstract - The ESP32 microcontroller and the Blynk IoT application are integrated in a novel system for automatic irrigation and tank water level management. Sensors for water levels, rainfall, temperature, and soil moisture track real-time environmental parameters. Temperature readings ranged from 25°C to 31°C over the 8-day research, but soil moisture was continuously kept within ideal ranges. Water waste was reduced and timely refills were ensured by the water tank level sensor mechanism, which successfully maintained a threshold of 15 cm. Based on sensor data, intelligent algorithms control irrigation, minimize waterlogging, and maximize water usage. Convenience and operational efficiency are increased via remote management via the Blynk app. This intelligent irrigation system provides a sustainable and effective answer to contemporary agriculture by preserving water, improving crop health, and facilitating data- driven farming methods.
Authors - Yash Prajapati, Ketul Patel, Nidhi Acharya, Nidhi Dubey, Nisarg Patel Abstract - The Internet of Things (IoT) is progressively changing and offers IoT ecosystems integrated network security challenges that require sophisticated security solutions. In this paper, we discuss the hybrid model that combines Federated Learning (FL) with Random Forest (RF) algorithms along with the validation of Blockchain to provide adaptive network security within IoT frameworks. The proposed architecture merges Blockchain’s protection against unauthorized access with the automatic updates and data processing of FL, decentralizing the security measures within the IoT ecosystems while increasing detection accuracy and safeguarding sensitive infor-mation. This framework overcomes the constraints imposed by centralized machine learning intrusion detection techniques, providing solutions to real world IoT security issues.
Authors - Harsh Raj, Kanishk Tewatia, Sumeet Gupta Abstract - Channel encoding plays a vital role in modern communication systems by maintaining data integrity and reducing the impact of noise. In this paper, we propose a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to classify various channel encoders. This approach aims to improve feature extraction and classification performance compared to traditional CNN architectures. In typical scenarios, receivers are aware of the encoder’s type and configuration. However, in non-cooperative environments such as military communications, surveillance, and cognitive radio systems, this information is often limited or unavailable. To address this, we explore a deep learning-based method to identify four types of encoders: block, convolutional, Bose–Chaudhuri–Hocquenghem (BCH), and polar encoders. By integrating CNN and LSTM layers, our proposed model achieves up to 98% classification accuracy and demonstrates strong generalization. Comparative analysis reveals that the hybrid model outperforms conventional CNN-based methods in terms of accuracy and robustness.
Authors - Arunkumar V N, Agna.S. Nath, Aswathi.K. B Abstract - This study examines the disconnect between India’s cybersecurity policies and their real-world implementation, revealing systemic barriers to digital empowerment. Through qualitative analysis, the research identifies four critical challenges: inadequate awareness programs, urban-rural security divides, gender-based vulnerabilities, and educational gaps in cyber-literacy. Findings show urban users exhibit risky digital behaviours despite high connectivity, while rural populations avoid online services due to security fears. Women face compounded risks, with many dependent on male relatives for digital access. The education system largely fails to equip students with basic cybersecurity knowledge. However, community-led initiatives demonstrate promising alternatives. Localized, vernacular training programs have successfully enhanced digital safety awareness and reduced fraud incidents. These models highlight the importance of contextual, participatory approaches to cybersecurity education. The study argues for rethinking cybersecurity as an essential dimension of human development rather than just technical infrastructure. It proposes shifting from compliance-focused governance to capability-building frameworks that prioritize protective freedoms for all citizens. Key recommendations include integrating cybersecurity into school curricula, developing gender-responsive digital safety programs, and creating community-based "digital mitra" networks. By bridging policy intentions with ground realities, this research offers pathways to make India's digital growth truly inclusive and secure.
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room EGOA, India
Authors - Pratyush Jaishankar, Ayman Aftab, Divyanshu Vyas, Dhanashree G Bhate Abstract - The research proposes a distinctive method to identify unauthorized people who enter restricted areas through a combination of KLD7 millimeter wave radar systems and deep learning algorithms. Gait patterns obtained from Doppler and micro-Doppler signals are analyzed by the system which offers both privacy preservation and non intrusiveness as opposed to conventional methods like CCTV surveillance. The Random Forest Classifier shows excellence by accurately identifying authorized or unauthorized individuals at a rate of 82% while maintaining its capabilities during various challenging environmental situations. The solution provides high practicality when used for real-time monitoring deployments. Future development efforts will direct their attention to growing the dataset while making the solution work efficiently on edge computing devices.
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room EGOA, India
Authors - Shreya Kapadia, Payal D Joshi Abstract - In the era of IR, event detection has moved beyond simple keyword searches to utilize advanced techniques to extract relevant events from massive news article datasets. The rapid growth of news highlights the need for efficient information retrieval techniques to capture the most relevant events. Traditional lexical-based retrieval methods, such as Whoosh and BM25, are effective in keyword matching; however, they have some limitations in understanding the semantic events from the indexed text. To enhance this limitation, this study introduces a Transformer-based deep learning model for Natural Language Processing (NLP), such as BERT, capable of capturing contextual relationships and improving the relevance of data. This research also explores an optimized approach that seamlessly integrates Whoosh for efficient indexing, BM25 for probabilistic ranking, and BERT for neural re-ranking, designed to improve event detection performance. Additionally, Named Entity Recognition (NER) significantly enhances event extraction by accurately identifying real-world entities like individuals, locations, organizations.The results of this research indicate that the integration of lexical models(Whoosh and BM25) with neural ranking models(BERT) significantly enhances precision, recall, and relevance, thereby exceeding the performance of traditional retrieval techniques. In our experiments BERT achieved a relevance score of 62% ,outperforming BM25 , which scored 55%. This demonstrates superior ability to capture contextual and semantic relationship in text. In conclusion, this study articulates prospective directions for future research within the realm of event detection, improving the efficacy of information retrieval in rapidly evolving news environments.
Authors - Sarika Kuhikar, Kashish Mishra, Tejas Dabholkar, Tejal Narvekar, Siddharth Suyal Abstract - This paper presents the design of a control circuit for a single-phase inverter capable of generating a pure sine wave output that is accurately aligned with the desired voltage amplitude and frequency. With the global shift toward renewable energy sources, the need for efficient and reliable power conversion systems has become more critical than ever. The proposed design utilizes advanced microcontroller technology along with modulation techniques such as Sinusoidal Pulse Width Modulation (SPWM) and Selective Harmonic Elimination (SHE). These techniques help achieve higher efficiency, significantly reduce harmonic distortion, and enhance the overall reliability of the inverter. This innovative approach contributes to improved energy efficiency and supports the development of smarter, more environmentally friendly power systems. The inverter is highly suitable for integration into solar energy systems, offering a stable and clean AC power supply for both residential and commercial applications. Its modular architecture also allows easy scalability to meet varying load demands and future upgrades.
Authors - Aditya Waradkar, Anagha Galagali, Niha Solkar, Shloka Suvarna, Aparna Bannore Abstract - Urbanization has resulted in a high rise in the use of vehicles, thus increasing parking problems like extended search times, fuel consumption, traffic congestion, and user frustration. To counter these problems, this paper introduces ParkSense, an IoT-based smart parking system that combines hardware and software elements for real-time parking space monitoring and management. It uses NodeMCU microcontrollers and IR sensors for car presence detection and an LCD display for real-time on-site updates. It has connectivity with ThingSpeak cloud to provide remote data access and visualization. The frontend is built with the MERN stack (MongoDB, Express, React, Node.js), and the Tailwind CSS provides a user-friendly and responsive interface on devices. ParkSense functionalities include real-time slot monitoring, access to historical data, administrative dashboards, and secure online payments. The system has proven to be highly efficient, reliable, scalable, and easy to use during testing and implementation. It saves considerable parking search time and fuel consumption, thus helping to create a more sustainable city environment. Future developments involve AI-based predictive analytics, dynamic pricing, personalized recommendations, and integration with EV charging stations.
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room EGOA, India
