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Activity Analysis Using Biosensor In Machine Learning
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| Author:
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, DR.P.KANAGARAJU, DR.P.SENTHILRAJA, S.RAJKUMAR, D.SEENIVASAN, R.BASKAR
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| Abstract:
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Recent advances and breakthroughs in machine learning (ML) technology have enabled us to interpret unprecedented amounts of collection, analysis, and sensory information over the last decade. Biosensor-based data collection applications in the field of modern medicine will be the future of heart health. With previous systems, it is difficult to find an analysis of biosensor cardiac data levels. Machine learning is a monitoring heart point for nursing diagnosis, and biosensor coupling is highlighted here. Biosensors are used to collect cardiac data. Support Reinforcement Vector Machine Learning algorithm (SRVMLA) used to classify the Biosensor based collected data. The proposed system can contain three processes: Data collection, preprocessing, feature selection and classification. Initialized the first step preprocessing is used to remove cleaning data from unwanted noise and data collected from biosensors. Feature selection is complete before grading and reveals essential features from a dataset of each variable. Classification algorithms based on SRVMLA have been proposed to improve functional classification performance by selecting useful classifications that contribute to effective classification methods based on biosensors' data. The proposed system increases study accuracy and reduces time complexity.
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| Keyword:
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Biosensor, support Reinforcement Vector Machine Learning algorithm, cardiac data, preprocessing, Data collection, feature selection, classification
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| EOI:
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-
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| DOI:
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https://doi.org/10.31838/ijpr/2020.12.04.588
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| Download:
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