This paper presents an AI-driven self-adaptive irrigation framework integrating IoT sensing, computer vision, adaptive learning, and automated irrigation control. The proposed system continuously monitors soil moisture, temperature, humidity, water flow, and visual plant health conditions using ESP32-based sensing devices and ESP32-CAM image acquisition. Centralised FastAPI server performs feature extraction, environmental analysis and AI based irrigation decision making at the backend. The proposed system, compared with existing threshold-based conventional irrigation systems, dynamically adjusts irrigation behaviour based on feedback received from the environment and plant stress conditions.
Machine learning (ML) and artificial intelligence (AI) will be substituting financial modeling and corporate strategy and will offer an opportunity to make data-driven, adaptive, and predictive decisions. This review examines the application of machine learning processes to finan…
Rising levels of carbon emissions have emerged as a key factor to climate change requiring smart mechanisms of monitoring and mitigation. In this paper, CarbonIQ, a machine learning-based, generative AI-based, and IoT-based data collection integrated carbon footprint prediction a…
Predictive analytics has emerged as a vital component of contemporary educational data analysis, enabling higher education institutions to move from reactive evaluation to proactive academic planning. The increasing availability of digital academic records—such as attendance, int…
Modern surveillance systems often struggle to detect and respond to threats in real time. This paper introduces a system that uses context-aware threat intelligence to detect weapons, recognize violent actions, analyze time-based patterns, and assess potential threats based on th…
In the digital age, the need for quick and reliable access to healthcare information is increasingly essential. This project introduces an AI-powered Symptom Checker Application that leverages Natural Language Processing (NLP) and Machine Learning (ML) to interpret user-described…
In the era of modern technologies, introduced the widespread use of cloud computing and other solutions that revolutionized the storage and management of information. Cloud-based network threat identification and risk management using applying Log Analysis and Machine learning is…