artificial intelligence

Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. This research focuses on the use of AI for anomaly detection in energy systems, specifically targeting Central Processing Unit (CPU) and Graphic Processing Unit (GPU) overheating in energy systems. With the increasing complexity and reliance on energy-consuming devices, overheating can significantly affect system performance and energy efficiency. This research proposes an artificial intelligence driven model integrated into the task scheduler of a system to monitor CPU and GPU temperature levels. When abnormal temperature thresholds are detected, the system promptly alerts the user, preventing potential damage and ensuring optimal performance. The methodology follows a structured approach which is the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, using Python programming and leveraging task scheduling for real-time monitoring. The results highlight the model’s accuracy in detecting anomalies, providing timely alerts, and preventing overheating events. The anomaly detection system improves energy management by identifying potential risks before they escalate, demonstrating its ability to optimize system efficiency, reduce energy waste, and improve decision-making regarding system and sustainability. Received: June 25, 2025 Accepted: July 23, 2025  DOI: https://doi.org/10.17654/0973700625001

The global demographic transition toward an aging population presents unprecedented challenges for long-term care systems, with critical workforce shortages affecting $92 \%$ of nursing homes and $70 \%$ of assisted living facilities. This mixed-methods study investigates the effectiveness of AI-enhanced human-machine collaboration in improving long-term care service efficiency and quality. Following PRISMA and STROBE guidelines, we conducted a systematic review of 105 studies and controlled trials across 218 facilities ( 94 intervention, 124 control) over 18 months. The AI-enhanced system analyzed 150 daily clinical data points per patient, providing real-time alerts for condition changes, fall risk assessment, and medication monitoring. Results demonstrated significant improvements in $89 \%$ of quality measures, including a $9 \%$ reduction in major falls ( $p=0.034$ ), 22\% decrease in ADL dependency ( $p DOI: https://dx.doi.org/10.17654/0973700626007 Received: October 18, 2025 Accepted: November 3, 2025;