Daily Archives: April 23, 2025

Greenhouse Monitoring and Controlling Using Iot and Machine Learning
Authors:-Shreya Patwadkar, Harshada Shete, Nirmala Shelke, Snehal Sabale

Abstract-This study presents an ESP8266-based greenhouse monitoring and controlling system that effectively regulates environmental parameters essential for plant growth. The system utilizes sensors such as the DHT11 for temperature and humidity, YL69 for soil moisture, and an LDR for light intensity. These sensors provide real-time data to the ESP8266 microcontroller, which not only processes the inputs but also enables wireless connectivity for remote monitoring and control. Through relay modules, the system controls devices such as water pumps, artificial lighting, and ventilation fans to maintain optimal growing conditions. A display panel can be included for on-site visualization, while data transmission via Wi-Fi facilitates integration with cloud platforms for data logging and analytics. This IoT-enabled approach supports automation, enhances sustainability, and minimizes manual intervention, making it a robust solution for modern greenhouse management.

DOI: 10.61137/ijsret.vol.11.issue2.369

Published by: Kajal Tripathi

Bail Reckoner
Authors:-Kutala Pravalika, Korimi Praveen

Abstract-The subjectivity and complexity of bail determinations in criminal justice systems have for a long time presented difficulties to fairness and consistency. In this paper, we suggest a software solution named Bail Reckoner that helps assess bail eligibility with the use of machine learning methodologies. Through processing of historical court data and determining useful features such as offense category, previous offenses, and socio-economic factors, the system produces a probabilistic bail outcome suggestion. Our method is designed to assist judges and legal practitioners by increasing transparency and minimizing bias. The system is tested using simulated legal datasets and shows encouraging accuracy in matching with real judicial decisions.

DOI: 10.61137/ijsret.vol.11.issue2.368

Published by: Kajal Tripathi

Copyright in the Digital Age: Addressing Issues on Online Piracy and Streaming Services
Authors:-Soumya Mishra, Dr. Taru Mishra

Abstract-Copyright laws are being developed along with technology. Today, digital advances such as the Internet and PC offer both opportunities and challenges for creative work stakeholders. The balance of these interests is complicated but reflects the continued adaptation of copyright to new developments. The advent of the Internet and the widespread adoption of HR computers have begun an era of unprecedented connectivity and accessibility for creative work. However, in addition to these transformative developments, there are various challenges faced by stakeholders in the production, distribution, and consumption of copyrighted content. This analysis examines various digital copyrights in the Internet age and uses literature review methods that address the complex interactions of technological advancements and legal frameworks. The rise of digital copyright infringement, the spread of user-generated content platforms, and the development of new distribution models provide traditional ideas for copyright tracking and intellectual property protection. Furthermore, the global features of the digital economy complicate regulatory efforts as the terms of the legal framework make it difficult to meet the infinite characteristics of online transactions and consumption of digital content. This review examines development strategies used by political decision-makers, industrial interest groups, and law to control these challenges, from legislative reforms to innovation in content management and digital rights management (DRM) systems. Through a comprehensive analysis of existing literature, this overview uncovers ongoing dialogue related to digital copyrights on the Internet, providing insight into the complexity of copyright in the digital age, debate, and future trajectories.

DOI: 10.61137/ijsret.vol.11.issue2.367

Published by: Kajal Tripathi

AI-Driven Real-Time Threat Detection System for Women’s Safety Using Deep Learning and Gesture Analytics
Authors:-Associate Professor Dr.S. Mohana, Preethi S, Sujay Charan P, Parthiban S, Sanjai Krishnan A, Pradiksha R J

Abstract-This paper presents a comprehensive AI-driven real-time threat detection framework designed to enhance women’s safety in urban environments. The system integrates advanced computer vision techniques including YOLOv8- based person detection, ResNet-50 gender classification, and LSTM-based gesture recognition to analyze live surveillance feeds. Through the execution of a multi-modal threat analysis algorithm, the system can recognize vital scenarios including SOS gestures (with 92.3% accuracy), isolated female detection in night hours (with 89.7% accuracy), and probable mob scenario situations. The system is integrated with a distributed architecture supporting low- connectivity location-based edge computing, real-time alert generation based on Twilio/Vonage APIs, and compatibility with police systems through an exclusive web dashboard. Experimental outcomes prove 86.4% threat detection accuracy at an average latency of 1.2 seconds on NVIDIA Jetson devices.

DOI: 10.61137/ijsret.vol.11.issue2.366

Published by: Kajal Tripathi