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Titlebook: Internet of Things of Big Data for Healthcare; 5th International Wo Jun Qi,Po Yang Conference proceedings 2024 The Editor(s) (if applicable

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978-3-031-52215-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Internet of Things of Big Data for Healthcare978-3-031-52216-1Series ISSN 1865-0929 Series E-ISSN 1865-0937
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Communications in Computer and Information Sciencehttp://image.papertrans.cn/i/image/472506.jpg
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Enhancing Search Engine Optimization in Healthcare and Clinical Domains with Natural Language Proceorough graph analysis, employing methods like node2vec, to identify pages with closely related content within our domain, addressing the issue of keyword cannibalization and content competition for ranking. In this paper, we present our innovative solutions in a visually intuitive manner, showcasing
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Evaluation of Integrated XAI Frameworks for Explaining Disease Prediction Models in Healthcare,lgorithms. Findings show that OmniXAI supports more methods and data formats. It can implement and explain tabular, image and NLP predictions while InterpretML supports only tabular data. InterpretML had a better plot of the explanation for the tabular prediction. InterpretML is more user-friendly a
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,Adaptive Prior Correction in Alzheimer’s Disease Spatio-Temporal Modeling via Multi-task Learning,tiveness of the proposed progression model is demonstrated by its superior prediction performance over multiple competing methods and accurate identification of compact sets of cognition-relevant biomarkers.
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An Electromyographic Signal Acquisition System for Sarcopenia,evice exhibits the merit in portable use and highly operational efficiency. In future, it holds a high potential to replace the commercially cumbersome or non-portable testing devices, thereby facilitating widespread sarcopenia screening across diverse populations.
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