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- W4385514210 abstract "ABSTRACTThe rapid advancement of technology such as stream processing technologies, deep-learning approaches, and artificial intelligence plays a prominent and vital role, to detect heart rate using a prediction model. However, the existing methods could not handle high -dimensional datasets, and deep feature learning to improvise the performance. Therefore, this work proposed a real-time heart rate prediction model, using K-nearest neighbour (KNN) adhered to the principle component analysis algorithm (PCA) and weighted random forest algorithm for feature fusion (KPCA-WRF) approach and deep CNN feature learning framework. The feature selection, from the fused features, was optimized by ant colony optimization (ACO) and particle swarm optimization (PSO) algorithm to enhance the selected fused features from deep CNN. The optimized features were reduced to low dimensions using the PCA algorithm. The significant straight heart rate features are plotted by capturing out nearest similar data point values using the algorithm. The fused features were then classified for aiding the training process. The weighted values are assigned to those tuned hyper parameters (feature matrix forms). The optimal path and continuity of the weighted feature representations are moved using the random forest algorithm, in K-fold validation iterations.KEYWORDS: ACO-ant colony optimizationPSO-Particle swarm optimizationPCA-Principal component analysisK-Fold cross validationheart rateCNN-Convolutional neural networkKNN-K-nearest neighbour Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article." @default.
- W4385514210 created "2023-08-04" @default.
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- W4385514210 date "2023-08-03" @default.
- W4385514210 modified "2023-09-27" @default.
- W4385514210 title "KPCA-WRF-prediction of heart rate using deep feature fusion and machine learning classification with tuned weighted hyper-parameter" @default.
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- W4385514210 doi "https://doi.org/10.1080/0954898x.2023.2238070" @default.
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