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- W3208593771 abstract "Recently, computer and smartphone devices have been increasingly employed in various life aspects. Human Activity Recognition (HAR) is one of the most emerging fields that can be clearly noticed in terms of computer related researches. This study proposes a classification model to recognize human six basic activities based on smartphone embedded sensors. For feature selection, we utilized three optimization search algorithms which are: Particle Swarm Optimization (PSO), Greedy, and Genetic algorithm. K-nearest neighbors (KNN) classifier, with Manhattan distance function, is adopted to measure the models performance on two publicly available well-known datasets (i.e., WISDM and UCI-HAR). All experiments are achieved using 10-folds cross validation technique, and the results are evaluated and compared using different metrics, for instance, accuracy, root mean square error, f-measure, and number of features. The experimental results proved that PSO based KNN classifier presents the best performance compared to the other two algorithms, in addition to the baseline KNN classifier. Moreover, the proposed model outperforms those state-of-the-art models presented in related literature regarding the UCI-HAR dataset with performance rate of 98.89%, and close to the best performance achieved on WISDM dataset with performance rate of 91.07%." @default.
- W3208593771 created "2021-11-08" @default.
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- W3208593771 date "2021-01-01" @default.
- W3208593771 modified "2023-09-23" @default.
- W3208593771 title "Optimizing the Performance of KNN Classifier for Human Activity Recognition" @default.
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- W3208593771 doi "https://doi.org/10.1007/978-3-030-81462-5_34" @default.
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