Browsing by Author "Slim, S.O"

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    IoT System Based on parameter optimization of Deep Learning using Genetic Algorithm
    (Intelligent Network and Systems Society, 2021-03) Slim, S.O; Elfattah, M.M.A; Atia, A; Mostafa, M.-S.M
    View references (73) Nowadays, more and more human activity recognition (HAR) tasks are being solved with deep learning techniques because it’s high recognition rate. The architectural design of deep learning is a challenge because it has multiple parameters which effect on the result. In this work, we propose a novel method to enhance deep learning architecture by using genetic algorithm and adding new statistical features. Genetic algorithm is utilized as an enhancing method to get the optimal value parameters of deep learning. Also new statistical features are appended to the features that are extracted automatically from CNN technique. Because the spread of the internet and its significance in our life, we developed Internet of Things (IoT) system. Therefore, we evaluated the performance of the proposed method in its system and found satisfactory results. Moreover, the proposed method was trained on two benchmark datasets (WISDM and UCI) and tested on the dataset, which was collected from IoT system. The results showed that the proposed model improved the accuracy up to 93.8% and 86.1% for user-dependent and independent. © 2020