Human activity recognition plays a competent part in human being to evaluate the activities of elder people. However, recently proposed human activity recognition techniques perform poorly whenever object characteristics are similar to background features (i.e., features are merely differing from each other). Therefore, an efficient meta-heuristic technique-based activity recognition technique is required to improve the accuracy of human activity recognition systems. To achieve the objectives of this research work, we have designed a novel hybrid differential evolution based J48 model to efficiently recognize the activities of human beings. Extensive experiments have been carried out to evaluate the effectiveness of the proposed technique. Experimental results reveal that the proposed technique outperforms existing techniques.

Machine learning, Activity recognition, Differential evolution, Neural networks

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