Authors - Nisarg Dobariya, Rutik Dobariya, Rikita Chokshi, Sarita Thummar Abstract - The transition from traditional to smart grids has been driven by the pursuit of greater efficiency, reliability, and consumer engagement. While smart grids offer numerous benefits, they are vulnerable to cybersecurity threats. Intrusion detection systems (IDS) are indispensable tools for safeguarding smart grid operations by identifying and preventing malicious attacks. This research investigates the application of various IDS models, classifiers, datasets, and algorithms in smart grid environments. The study underscores the importance of using datasets specifically designed for smart grid networks to ensure accurate and reliable IDS performance. Moreover, the research demonstrates the potential of distributed approaches and advanced algorithms in enhancing IDS capabilities, thereby bolstering the security and resilience of smart grid infrastructure.
Authors - Revathy P, Rakshana A, Tinu A V, Vijayakumar R Abstract - The increasing demand for efficient package delivery has led to a challenge in detecting food spills during transit. Traditional methods rely on manual inspection, which is time-consuming and prone to human error. This study proposes an AI-based approach using Convolutional Neural Networks (CNNs) implemented with TensorFlow to detect both damaged packages and spilled food packets. The model is trained on a large dataset of package and food packet images, learning key features indicative of physical damage and identifying food spills. By fine-tuning pre-trained CNN architectures, the model achieves high accuracy in detecting both damage and spills. The interface is attached with an alert mechanism that notifies when damage or spill is detected. The TensorFlow framework is used for building, training, and deploying the model efficiently. The proposed system aims to automate package and food packet inspection, reduce human labor, and improve delivery service reliability by providing fast and accurate damage and spill detection.
Authors - Smita Mahajan, Archana Chaudhari, Ameysingh Bayas, Devika Shrouti Abstract - Drowsiness is a critical issue that contributes to a significant number of accidents in various scenarios, such as driving and hazardous work environments. Existing drowsiness detection projects often rely on subjective measures and single modality detection, leading to limited accuracy and applicability. This research proposes a drowsiness detection system that employs deep neural networks and machine learning-based object detection techniques to overcome these limitations. The ability of the recent drowsiness detection systems to reliably and impartially detect drowsiness is restricted. The proposed model uses computer vision and machine learning algorithms to identify drivers' drowsiness based on facial attributes like eye movement monitoring. The model aims to improve the accuracy and reliability of drowsiness detection by combining multiple modalities. The implementation includes using the Keras library, which is required for a Convolutional Neural Network (CNN) architecture. The model is trained on a customized dataset of facial images with open or closed eyes labels. The CNN discovers the complex relationships and features from the data, classifying drowsiness critically. The proposed drowsiness detection system's results demonstrate an optimistic accuracy of 98.88%. The system signals real-time alerts when the drowsiness in the behavior of the driver is caught, potentially averting accidents and enhancing safety. This technique suggests an accurate and trustworthy approach for detecting drowsiness in different domains, including driving and unsafe work environments, with 98.88% accuracy. This system can be a valuable means for improving safety and controlling the accidents caused by driver drowsiness.
Authors - Aswathkrishna S D, M. Sujithra Abstract - In today's rapidly evolving world, recognizing student food choices is crucial. This study explores food choices and how they align with areas containing restaurants and grocery stores. Clustering techniques including K-Means, Hierarchical Clustering, and DBSCAN were employed with the silhouette score used to validate and determine the most effective method for analysis. Based on food choices data sourced from Kaggle and location data from the Foursquare API, the research provides location recommendations for students. Suggestions guide students to areas that align with their food choices aiming to enhance their overall experience.
Authors - Ofaletse Mphale, V. Lakshmi Narasimhan, S. Sasikumaran Abstract - The Internet of Medical Things (IoMT) presents transformative potential for healthcare by enabling real-time patient monitoring, advanced diagnostics, and personalized treatments. However, its adoption in developing countries is hindered by significant ethical, security, and privacy challenges. Studies focused on developing countries often identify these challenges but rarely propose rigorous frameworks for successful adoption. This study employs a desktop search methodology to comprehensively review the existing literature, identifying crucial ethical, security, and privacy concerns associated with the IoMT adoption. Through this analysis, the study proposes potential mitigation strategies and a framework to facilitate the effective adoption of IoMT in developing countries. Findings will support healthcare decision-makers and policymakers in developing countries, enabling them to devise strategies that ensure ethical practices, secure patient data, and safeguard privacy in healthcare IoT integration. This will lead to improved healthcare delivery and enhanced patient outcomes.
Authors - Priyal Donda, Vatsal Upadhyay, Janhavi Gulabani, Sharvari Patil, Vinaya Sawant Abstract - Phishing is increasingly being one of the frequent cyber-attacks. Since this trend has seen the incidence increased significantly in the last few years, people and organizations have been highly affected by data breaches and financial losses. Such growth only increases the demand for effective mechanisms of defense, as traditional approaches of machine learning like SVM, Random Forest, and Long Short-Term Memory networks often fail to detect phishing attempts with accuracy. SVMs can be computationally expensive, sensitive to noise, and require careful selection of kernel functions, while LSTMs are complex, prone to overfitting, and require substantial amounts of labeled data. In light of these limitations, the use of GANs has been recent in order to improve detection capabilities. GANs create realistic phishing URLs that advanced detection models struggle to distinguish, using semi-supervised training to differentiate between adversarial and legitimate URLs. Specifically, this holistic approach grapples with the sophistication of phishing attacks and places an emphasis on adaptive defense, since it has changed the basis for detection from content-based to URL-based techniques. Finally, these novel approaches introduce a promising pathway for the mitigation of phishing risks and sensitive information safeguarding, thus building security strength in the digital world.
Authors - Amol Mashankar, Smita Kalokar Abstract - The retail business marketplace is experiencing a significant shift, with a growing emphasis on the innovations brought by internet of things (IoT) technology. The retail aspect is rapidly evolving, driven by new improvements in internet technology, which play a important role in the transformation of the retail sector. The new updation involves continuously adapting to the fast-paced changes within the retail environment. New techniques and innovations are emerging daily to better address customer needs and satisfaction preferences. This paper focus on to explore the practices and performance of IoT technology in the retail sector. It also emphasis an analytical framework for evaluating the approaches to IoT technology practices and their effectiveness in retail stores.
Authors - Bakka Vamshi, Munnuru Umakanth, Kadwasra Swapna, Punuru Venkata Usha Sree, Mannepalli Rohini Sri, Sushama Rani Dutta Abstract - Predicting heavy rainfall remains a significant challenge for meteorological departments as it greatly impacts economies and human lives. Severe rainfall can result in natural disasters like floods and droughts, impacting millions of people globally every year. Precise rainfall prediction is especially important for nations like India, where agriculture serves as a key economic pillar. Due to the atmosphere’s dynamic nature, statistical methods often fall short in achieving high prediction accuracy. The complex, nonlinear characteristics of rainfall data make Artificial Neural Networks a more effective method. This paper reviews and compares various methods and algorithms employed by researchers for rainfall forecasting, presenting the findings in a tabular format to make these techniques accessible to non-specialists.
Authors - Rajitha Kotoju, Sugamya Katta, Abrar Khan Abstract - Real-time air quality monitoring and predictive pollution control are critical for addressing escalating environmental and public health challenges, particularly in low-income areas with limited infrastructure. This paper explores the integration of Big Data analytics and IoT to develop cost-effective, scalable solutions for real-time air quality assessment. The proposed framework aims to identify pollution patterns, predict air quality trends, and provide actionable insights for policymakers. A unique feature of this study is its emphasis on low-cost sensor deployment and edge-computing techniques to ensure accessibility in resource-constrained settings. The interdisciplinary approach combines environmental science, AI, and public health perspectives to establish a holistic framework for data collection, analysis, and decision-making. Additionally, this paper addresses the integration of findings into policy frameworks by proposing data-driven recommendations for urban planning, industrial regulation, and community health interventions. The results demonstrate significant advancements in predictive accuracy and actionable intelligence generation while minimizing implementation costs.
Authors - D.K. Chaturvedi, Nisha Verma Abstract - Artificial Intelligence and Machine Learning (AIML) are quickly proceeding in many areas. These technologies, including the smart footwear (SF) industry, have significantly impacted the consumer goods market. AIML are widely used in the design and production of SF. There are different applications of SF such as healthcare SF, assistive SF for old age persons or impairments, navigating footwear for unknown areas, mobility and gait analysis, safety footwear, anti-skid footwear, footwear for army personnel, and power generated footwear etc. The SF helps in acquisition of real-time data of patients to monitor and suggest suitable treatment. Besides these, SF can be classified based on the different architecture and processing techniques. This paper includes different research studies conducted in the past on various tools and techniques used to create smart footwear for different applications.
