首页|New Findings on Machine Learning from SRM Institute of Science and Technology Su mmarized (Optimized Tiny Machine Learning and Explainable Ai for Trustable and E nergy-efficient Fog-enabled Healthcare Decision Support System)
New Findings on Machine Learning from SRM Institute of Science and Technology Su mmarized (Optimized Tiny Machine Learning and Explainable Ai for Trustable and E nergy-efficient Fog-enabled Healthcare Decision Support System)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news originating from Tamil Nadu, India, by NewsR x correspondents, research stated, "The Internet of things (IoT)-based healthcar e decision support system plays a crucial role in modern medicine, especiAlly wi th the rise in chronic illnesses and an aging population necessitating continuou s remote health monitoring. Current healthcare decision support systems struggle to deliver timely and accurate decisions with minimal latency due to limited re al-time healthcare data and inefficient computational resources." Our news journalists obtained a quote from the research from the SRM Institute o f Science and Technology, "There is a critical need for an optimized, energy-eff icient machine learning model that reliably supports remote health monitoring wi thin IoT and fog computing environments. Our study proposes an Optimized Tiny Ma chine Learning (TinyML) and Explainable AI (XAI) binary classification model for a trustable and energy-efficient healthcare decision support system, leveraging fog computing to optimize performance. The fog-based approach improves response times and enhances bandwidth usage, addressing critical needs such as reduced l atency, higher bandwidth utilization, and decreased packet loss. To further impr ove efficiency, we incorporate the innovative mLZW data compression technique, s ignificantly enhancing data communication efficiency and reducing response time to critical health alerts. However, limited real-time healthcare data records ch Allenge machine learning classification performance. By implementing a TinyML al gorithm, our system demonstrates superior performance to other machine learning models. The proposed optimized TinyML model achieves an impressive F1 score of 0 .93 for health abnormalities detection, emphasizing its robustness and effective ness."
Tamil NaduIndiaAsiaCyborgsEmergi ng TechnologiesMachine LearningSRM Institute of Science and Technology
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