Animal Health Monitoring System Using IoT And Wireless Sensor Network
Abstract: The proposed gadget addresses sustainable development goals (SDGs) along with no poverty, 0 hunger, and sustainable cities by means of enforcing a shrewd farm animal monitoring machine to enhance dairy production. Traditional farm animal’s management in developing international locations faces inefficiencies due to limited technological advancements, which negatively affect productiveness and useful resource utilization. This research introduces a cost effective, clever dairy tracking gadget integrating Wi-Fi sensor nodes, the Internet of Things (IoT) and Node MCU generation. The gadget encompasses with three modules inclusive of a wise environmental tracking system, a cow collar prepared with sensors for tracking health and region, and water level indicator. Real-time information is processed and saved in a comprehensive database, enabling instantaneous signals for anomalies. The gadget enhances farm animal health and productiveness with the aid of minimizing human intervention, reducing labour fees, and automating vital functions. Its modular, plug-and-play layout offers scalability for programs in zoos and fowl monitoring, making it a sizable development in contemporary agricultural practices.

Authors: Thanushree P S, Thrupthi N, Meghana B S, Savita D Torvi

File Name: Thanushree_13010-25-103.pdf
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Wall Hawk: An AI-Based Threat Detector for Intelligent Surveillance Camera
Abstract: In today’s world of evolving security threats and modern warfare, there is a growing need for advanced systems that enhance situational awareness and threat detection. The "Wall Hawk" project is an AI-powered surveillance solution designed to aid counter-terrorism and military missions. It integrates microwave radar for behind-wall human detection and autonomous robots equipped for bomb and gas sensing, ensuring 360° environmental monitoring. The system combines NodeMCU and Raspberry Pi for efficient control and processing, using radar sensors, gas/metal detectors, and AI-enabled cameras for real-time weapon and explosive identification. With Python and OpenCV, it employs deep learning for accurate image analysis and threat classification. Wall Hawk reduces human risk, minimizes false alarms, and delivers rapid, actionable intelligence—making it ideal for military zones, border control, rescue operations, and high-security areas.

Authors: Savitha J, Suman D S, Rakshitha R S, Jeevith S H, Yogeesh M, M R Maanasa

File Name: Savitha_13010-25-102.pdf
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Enhancing Power System Stability Integrating Neural Network Control & PSO Optimization for Single Machine Infinite Bus
Abstract: This paper presents a hybrid control strategy combining Particle Swarm Optimization (PSO) with Neural Networks (NN) to enhance the stability of the Single Machine Infinite Bus (SMIB) system. Conventional Power System Stabilizers (PSS) are effective in suppressing electromechanical oscillations but struggle with the dynamic and non-linear complexities of modern power systems. The proposed PSO-NN controller automatically tunes the neural network parameters, leveraging the global search capabilities of PSO to optimize system stability under varying conditions. Simulation results demonstrate significant improvements in transient stability, reduced oscillations, and faster settling times, particularly in minimizing rotor angle error and speed overshoot. This approach offers a robust solution for modern interconnected grids, addressing increasing system complexities and disturbances. The study also suggests potential extensions, such as incorporating renewable energy sources and exploring additional optimization algorithms to further enhance grid resilience and stability.

Authors: Girase Sagar Mahendrasing, Prof T. Y. Kharche

File Name: Sagar_13010-25-104.pdf
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A Comprehensive Review of Deep Learning Applications in Soybean Disease Detection
Abstract: Soybean (Glycine max) is very important on a global scale because it gives both people and animals needed protein and oil. Still, its production is halted by the presence of numerous diseases that are brought about by fungi, bacteria, viruses and nematodes. Detection of diseases at the right time and with accuracy saves crops from being damaged and ensures enough food supplies. Checking things manually in the usual way is time-consuming, adds inconsistency and requires experts, so it becomes harder to use on a large scale. Over the past few years, deep learning (DL) has proved to be very effective in automating the process of detecting diseases. This work provides a broad review of new studies using deep learning for identifying soybean diseases. We examine multiple DL models, for example, Convolutional Neural Networks, hybrid models made of CNNs and GNNs, Vision Transformers and the latest YOLOv8-DML object detection tool. Many things separate these models in terms of discovering important features for signs, immediate diagnosis and applying their skills in various complex environments. The role of tools such as Grad-CAM is highlighted, since they bring more clarity to the model and build user trust. Various research papers are reviewed in a systematic way and their methods, results and problems are explained in detail. Besides, it is shown in the table that the accuracy, datasets, how easy to interpret they are and their ability to be used in real situations are not the same for all models. Even though many positive things have been achieved, several challenges still exist. It is still very difficult to convert research findings into actual applications because of the shortage of different data, variations in the environment, dependence on specific hardware and poor understanding of the math used. Therefore, the paper also discusses areas of research that remain unsolved and suggests creating light, understandable and adaptable AI systems that support different types of information and fit with IoT systems. The main goal of this review is to serve as a basic reference for people working in agriculture, data science and crop health analysis. The article makes progress for precision agriculture by uniting research and pointing out areas where great advances can be made.

Authors: Naresh Kumar, Ashish Verma, Dr. Saurabh Gaur

File Name: Naresh_13010-25-105.pdf
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