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Titlebook: Cognitive Informatics and Soft Computing; Proceeding of CISC 2 Pradeep Kumar Mallick,Akash Kumar Bhoi,Victor Hugo Conference proceedings 20

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發(fā)表于 2025-3-25 04:25:16 | 只看該作者
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發(fā)表于 2025-3-25 08:19:33 | 只看該作者
Cognitive Informatics and Soft Computing978-981-16-1056-1Series ISSN 2194-5357 Series E-ISSN 2194-5365
23#
發(fā)表于 2025-3-25 12:07:18 | 只看該作者
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發(fā)表于 2025-3-26 03:05:47 | 只看該作者
Classification of Arrhythmia Through Heart Rate Variability Using Logistic Regression,tion heart rhythm of the subjects suffering from arrhythmia. Accurate classification of the abnormal ECG signal from normal signals helps the clinical practitioners to diagnose arrhythmia patients. This article proposed an arrhythmia detection scheme using feature selection and logistic regression.
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發(fā)表于 2025-3-26 07:18:51 | 只看該作者
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發(fā)表于 2025-3-26 12:09:20 | 只看該作者
2194-5357 s a reference resource for researchers and practitioners in .This book presents best selected research papers presented at the 3rd?International Conference on Cognitive Informatics and Soft Computing (CISC 2020), held at Balasore College of Engineering & Technology, Balasore, Odisha, India, from 12
29#
發(fā)表于 2025-3-26 13:43:16 | 只看該作者
Intelligent Systems Reference Librarys reviewed with their comparative analysis as per healthcare data analytics. To design and implement a biosensor, suitable machine learning algorithm should be selected in order to detect anxiety and stress levels. Using machine learning algorithms, the analysis can be fault-tolerant and stress detection could be effective in terms of convenience.
30#
發(fā)表于 2025-3-26 20:47:21 | 只看該作者
Biotechnology: Pharmaceutical Aspectstion heart rhythm of the subjects suffering from arrhythmia. Accurate classification of the abnormal ECG signal from normal signals helps the clinical practitioners to diagnose arrhythmia patients. This article proposed an arrhythmia detection scheme using feature selection and logistic regression.
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