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 the situation, all to spot unusual behaviour in real-time video feeds. The proposed system utilizes YOLO-based deep learning models to detect weapons and classify human behaviour. On the other hand, a scoring engine determines the level of risks from the objects observed, the behaviour pattern, the location, and past occurrences. High-risk occurrences will automatically be accompanied by raising an alarm, collecting the evidence, reporting the occurrence, and sending emails. In order to foster openness in the process and increase the level of confidence, Explainable Artificial Intelligence (XAI) methods including SHAP (SHapley Additive exPlanations) and Grad-CAM are applied to enhance transparency. Also, a real-time dashboard for incidents is put up for monitoring and analyzing incidents.
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…
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…
Modern microblogging and social networking ecosystems serve as massive, continuous streams of public expression, generating expansive volumes of multilingual data that reflect global consumer sentiments and emotional trends. Parsing actionable insights from this cross-lingual tex…
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…
Microplastic contamination in coastal ecosystems has emerged as a critical environmental issue with significant ecological, economic, and public health consequences. Conventional monitoring approaches rely heavily on field sampling and laboratory-based analysis, which are time-co…
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 c…