Authors - Keesari Abhinav Reddy, Vanaparthi Sai Charan, Md. Sufiyan, Puvula Kiranmai, Madhuri. T, M. Venugopala Chari Abstract - The major challenge for road safety and traffic regulation continues to be categorized traffic offenses that include speeding, running of red lights, improper parking, and distracted driving. Recent innovations in artificial intelligence (AI) and machine learning (ML) have made it possible to develop automated systems that can detect and classify varied traffic violations in detail. This paper analyzes studies that have emerged recently, focusing on advanced technologies, including those such as YOLO-based object detection, OCR, integration with IoT, and real-time monitoring. The paper evaluates datasets, performance metrics, and methodologies covering violations including helmet use, lane changing, and the use of a mobile phone while driving. Significant challenges that have been touched upon in the review include issues of data privacy, high computational requirements, and environmental limitations. Some of the encouraging solution includes use of sophisticated deep learning models, big data analytics, sensor fusion, and edge computing as pathways to enhance scalability and reliability. Future effort will include improvement of real-time systems, reduction of false positives, and addressing socio-technical problems. Using approaches that merge existing advances, this paper has suggested some pathways for using AI-driven systems towards the improvement of road safety and adherence to traffic rules.
Authors - Indushree Shetty, Prerna Agrawal, Savita Gandhi Abstract - The Chronic respiratory diseases, including Chronic Obstructive Pulmonary Disease (COPD), Cystic Fibrosis, Chronic Bronchitis, Interstitial Lung Disease (ILD), Pleural Effusion, Pneumothorax, and Mesothelioma contribute significantly to global mortality and morbidity. The lung diseases in India are influenced by various demographic, environmental, and lifestyle factors like air pollution, high smoking rates, climate change and weather patterns, genetic and hereditary factors, etc. This paper highlights the current scenario of various lung diseases affecting Indian population, highest incident being of COPD to the extent of 89%. The study in this paper surveys the comparison of detection of different lung diseases using machine learning in an Indian Scenario with respect to different parameters like diseases predicted, dataset used, source of dataset, findings, limitations, future score, methods used and accuracy. Based on the comparative study, this paper also highlights various research gaps for future scope in an Indian Scenario. By prioritizing the solutions to the identified research gaps, medical practitioners would be able to handle better India's high respiratory disease burden, increasing the likelihood of more dependable and inclusive healthcare solutions.
Authors - Siddhi Mulewar, Abhijay Patil, Gauri Patil, Nikhil Chame, Smita Kulkarni Abstract - E-commerce has completely transformed traditional retail by lowering operating expenses and enabling worldwide access. Online shopping experiences have been further changed by the integration of artificial intelligence (AI) and machine learning (ML), especially with the advent of Fashion Recommendation Methods (FRM) that employ deep learning techniques. This research introduces a unique FRM that uses a single image input to provide tailored fashion suggestions based on user preferences, improving the quality of the shopping experience. Collaborative filtering (CF) is preferred method in this research work, which encourages users to explore a wider range of content and become more engaged. In this research work ResNet50 pre-trained neural networks proposed to extract information from photos, enabling precise and customized fashion recommendations. Comparative studies show that ResNet50 performs better than other CNN models, leading to increased personalization and accuracy. In the highly competitive world of e-commerce, this study emphasizes the potential of AI-driven suggestions to improve the online shopping experience, stimulate user engagement, and foster loyal consumers. VITON is a Virtual Try-On Network that uses images instead of 3D data to overlay clothes on a person’s image. It creates and refines photo-realistic images with natural clothing deformation using a coarse-to-fine strategy.
Authors - Manasa S Desai, Nirmala M B, Veeresh Kumar Y M, Varsha G C, Vinnet Gokhale, Sushma E Roa Abstract - Electronic Know Your Customer (e-KYC) system is essential for banking and identity providers to verify customer identities efficiently. With the widespread adoption of cloud computing, due to its resource efficiency and high accessibility, many sectors have implemented their e-KYC systems on the cloud. This shift, however, raises significant concerns about the security and privacy of e-KYC documents stored in the cloud. Blockchain technology, a recent innovation, offers potential solutions to enhance various application domains, including digital identity verification. This project proposes a Blockchain-based e-KYC system to address these concerns. This system provides a secure, efficient, and reliable method for identity authentication, which is particularly beneficial in sectors such as banking, tele communications, and government services. By utilizing a distributed ledger to store and verify customer data, the proposed e-KYC framework ensures data integrity and minimizes fraudulent activities. In this framework, customer data is stored on a distributed ledger and encrypted to enhance security. This encryption safeguards sensitive personal information from unauthorized access and cyber threats. This project combines the Ethereum blockchain with Zero-Knowledge Proof (ZKP) technology to provide strong digital identity verification, maintain data integrity, and reduce fraud. The decentralized nature of proposed e-KYC system not only boosts security but also reduces reliance on central authorities, thereby accelerating the verification process and lowering operational costs. This approach offers arobust solution for secure digital identity verification.
Authors - Chilakala Sudhamani, Akula Spoorthi, B. Srilatha Abstract - In today’s world, women face numerous safety challenges, including harassment and molestation. In this paper, we proposed a self-defense stun gun as an effective and efficient solution for women’s safety. This portable device contains a high-voltage generator, GSM and GPS module, panic and taser button and an Arduino Uno with Atmega328 AVR microcontroller. When the device is activated in a dangerous situation, it immediately sends an SMS with the user’s location and distress signal to pre-selected contacts. It also generates a 1000kV electric shock to temporarily immobilize an attacker, allowing the user to escape or seek help. This device aims to enhance the safety and security of women in urgent need or dangerous circumstances for proactive measures against gender-based violence.
Authors - Riddhi Sonawane, Ganesh Bhutkar, Swarup Vishwas, Vivek Badade, Akshay Shingote Abstract - Traditional persona classification methods rely on static, time consuming techniques like surveys and interviews. To address this limitation, we propose F², a novel approach that leverages facial recognition and digital footprint analysis for dynamic persona classification. By integrating real-time data from various digital platforms, F² creates more accurate and up-to-date user profiles. Our system prioritizes user privacy and adheres to relevant data protection regulations. Through robust facial recognition and advanced machine learning algorithms, F² effectively categorizes users into distinct personas, enabling tailored experiences and personalized interactions. This innovative approach has the potential to revolutionize user modeling and enhance digital experiences across diverse domains.
Authors - Aafiya Anjum Abdul Rafique, Martin H Mollay, Shailesh Gahane, Deepak S. Sharma, Pankajkumar Anawade Abstract - There have been remarkable adoptions and uses of Information Technology (IT); therefore, there has been a significant surge in energy consumption and carbon emissions in recent times. While most industries are increasingly relying on digital technologies, IT operations are also increasing their impact on the environment, thereby making green IT a vital necessity. Green IT is an all-encompassing method of managing the environmental footprint of IT through the reduction of energy consumption, electronics waste, and optimum resource efficiency. This paper discusses, from a critical perspective, the role of Green IT in reducing the carbon footprint of IT operations through sustainable technologies and practices. Beyond this, it also discusses challenges and potential solutions for a more green IT landscape in the data center, cloud computing, virtualization, energy-efficient hardware, and new sustainable development practices in software. To sum up, this paper focuses attention on some of the critical factors for driving the adoption of sustainable IT solutions: policy, education, and cross-sector collaboration.
Authors - Dheekshitha Bazar, Gajelli Sai Susmitha, Shreshta Myana, Ramu Kuchipudi, Ramakrishna Kolikipogu, P. Ramesh Babu, K. Gangadhara Rao Abstract - Strategic planning, grid management, and lessening the financial burden on Telangana’s power sector all depend on accurate demand forecasts for electricity. Currently, forecasting methods rely primarily on traditional approaches, but these models often fall short in capturing complex demand patterns at multiple time intervals, especially in dynamic sectors like agriculture. Existing forecasting methods, focused mainly on traditional approaches, often fall short in capturing complex demand patterns across multiple time scales, particularly in sectors like agriculture. This study introduces a comprehensive multi-scale forecasting model for Telangana’s electricity consumption over the next five years, targeting yearly, monthly, weekly, and daily intervals, with a focus on peak load forecasting. Time series techniques such as ARIMA, Prophet, Weighted Moving Average (WMA), and Error Trend Seasonality (ETS) are leveraged to capture seasonality, trends, and short-term fluctuations in demand, providing actionable insights for the Telangana SLDC. Methods for machine learning such as Long Short-Term Memory (LSTM) networks and Gradient Boosting Machines (GBM), are integrated to capture complex temporal patterns and improve predictive accuracy. This study offers a scalable framework for electricity demand forecasting, adaptable to other regions and utilities, advancing methodologies in the power sector. The suggested approach uses metrics to assess the model’s performance such as Root Mean Square Error, Mean Absolute Error (MAE), Both Mean Absolute Percentage Error (MAPE) and RMSE are used to choose the most precise model for every period.
Authors - Hemal S, Sohana R, M Shahina Parveen, Tarun Pradeep Kumar Abstract - Childhood fever poses a significant health concern in India, necessitating timely intervention and effective healthcare strategies. However, predicting fever prevalence accurately remains a challenge due to the diverse healthcare landscape and maternal-child health indicators. This research aims to develop a systematic methodology for predicting childhood fever prevalence based on maternal and child healthcare indicators in India. Leveraging machine learning algorithms, particularly Support Vector Regression (SVR), the study seeks to provide an effective tool for early detection and intervention in infant fever cases. Using data from the "India - Annual Health Survey (AHS) 2012-13" dataset, specific maternal and child healthcare indicators relevant to childhood fever prevalence are identified. These indicators encompass ante-natal care, delivery care, immunization, breastfeeding, and supplementation practices. Various regression algorithms, including SVR, are trained and evaluated to accurately predict childhood fever prevalence. Experimental results demonstrate that SVR outperforms other regression algorithms, showcasing its effectiveness in capturing non-linear relationships and handling outliers. This study offers a structured framework for early detection and intervention in childhood fever cases, leveraging machine learning algorithms and maternal-child health indicators. By accurately predicting fever prevalence, healthcare practitioners can implement timely interventions, ultimately improving healthcare outcomes for infants in India.
Authors - Shivam Kumar Singh, Sindhu Chandra Sekharan, Aishwarya Mondal, Nitin Nagar, Shruti Shreya, Yuting Zhu Abstract - This work presents a comprehensive IoT-based smart assistant device aimed at providing essential navigation and safety support for physically challenged individuals, especially those with visual impairments. The device is equipped with advanced functionalities, including GPS tracking for real-time location monitoring, MobileNet-based object and face recognition, OCR capabilities for reading printed text, and ultrasonic sensors for detecting obstacles, which trigger an alarm to alert the user. Its design prioritizes energy efficiency, allowing it to run effectively on low power while offering reliable real-time processing. By combining multiple assistive features into a single, cost-effective, and portable device, this solution sets itself apart from traditional options that often focus on one functionality or rely on expensive hardware. The modular and scalable architecture not only makes it an affordable and practical solution but also allows for easy customization and potential wireless enhancements. This flexibility opens up possibilities for broader applications in fields like assistive healthcare, autonomous navigation, and consumer electronics, making it a pioneering tool in inclusive technology that enhances mobility, security, and overall independence for its users.
