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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