Modern smart grids are becoming increasingly complex, creating a growing need for monitoring systems capable of detecting abnormal behavior before major power failures occur. This paper presents a real-time smart grid monitoring framework for early outage-risk detection using machine learning and streaming analytics. Smart meter data are processed using temporal feature extraction, lag observations, and rolling statistical measures to capture variations in electricity consumption patterns. Since labeled outage datasets are not readily available, an Isolation Forest-based anomaly detection model is used to identify unusual load behavior. To reduce false alarms, detected anomalies are further verified through statistical threshold analysis. The framework also supports continuous monitoring through a lightweight real-time dashboard with live visualization and streaming inference. Experimental results show that the system can effectively detect abnormal load fluctuations while maintaining low computational overhead suitable for real time deployment. The proposed approach provides a scalable and practical solution for proactive smart grid monitoring and anomaly-aware outage-risk assessment.
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…
Despite being one of the major modifiable risk factors for cardiovascular disease, stroke and premature death globally, a significant proportion of those affected are not detected or managed well. This paper presents the design and development of a real-time predictive system for…
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…
Rural communities in India face ongoing challenges in accessing primary healthcare. These challenges include a lack of doctors, geographical isolation, and high consultation costs. This paper discusses an AI-assisted healthcare kiosk, a web-based, fully offline system that provid…
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…