Yazar "Awadallah, Mohammed A." seçeneğine göre listele

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    Binary JAYA Algorithm with Adaptive Mutation for Feature Selection
    (SPRINGER HEIDELBERG, TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY, 2020) Awadallah, Mohammed A.; Al-Betar, Mohammed Azmi; Hammouri, Abdelaziz I.; Alomari, Osama Ahmad
    In this paper, a new metaheuristic algorithm called JAYA algorithm has been adapted for feature selection. Feature selection is a typical problem in machine learning and data mining domain concerned with determining the subset of high discriminative features from the irrelevant, noisy, redundant, and high-dimensional features. JAYA algorithm is initially proposed for continuous optimization. Due to the binary nature of the feature selection problem, the JAYA algorithm is adjusted using sinusoidal (i.e., S-shape) transfer function. Furthermore, the mutation operator controlled by adaptive mutation rate (Rm) parameter is also utilized to control the diversity during the search. The proposed binary JAYA algorithm with adaptive mutation is called BJAM algorithm. The performance of BJAM algorithm is tested using 22 real-world benchmark datasets, which vary in terms of the number of features and the number of instances. Four measures are used for performance analysis: classification accuracy, number of features, fitness values, and computational times. Initially, a comparison between binary JAYA (BJA) algorithm and the proposed BJAM algorithm is conducted to show the effect of the mutation operator in the convergence behavior. After that, the results produced by the BJAM algorithm are compared against those yielded by ten state-of-the-art methods. Surprisingly, the proposed BJAM algorithm is able to excel other comparative methods in 7 out of 22 datasets in terms of classification accuracy. This can lead to the conclusion that the proposed BJAM algorithm is an efficient algorithm for the problems belonging to the feature selection domain and is pregnant with fruitful results.
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    Gene selection for microarray data classification based on Gray Wolf Optimizer enhanced with TRIZ-inspired operators
    (ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2021) Alomari, Osama Ahmad; Makhadmeh, Sharif Naser; Al-Betar, Mohammed Azmi; Alyasseri, Zaid Abdi Alkareem; Abu Doush, Iyad; Abasi, Ammar Kamal; Awadallah, Mohammed A.; Abu Zitar, Raed
    DNA microarray technology is the fabrication of a single chip to contain a thousand genetic codes. Each microarray experiment can analyze many thousands of genes in parallel. The outcomes of the DNA microarray is a table/matrix, called gene expression data. Pattern recognition algorithms are widely applied to gene expression data to differentiate between health and cancerous patient samples. However, gene expression data is characterized as a high dimensional data that typically encompassed of redundant, noisy, and irrelevant genes. Datasets with such characteristics pose a challenge to machine learning algorithms. This is because they impede the training and testing process and entail high resource computations that deteriorate the classification performance. In order to avoid these pitfalls, gene selection is needed. This paper proposes a new hybrid filter-wrapper approach using robust Minimum Redundancy Maximum Relevancy (rMRMR) as a filter approach to choose the topranked genes. Modified Gray Wolf Optimizer (MGWO) is used as a wrapper approach to seek further small sets of genes. In MGWO, new optimization operators inspired by the TRIZ-inventive solution are coupled with the original GWO to increase the diversity of the population. To evaluate the performance of the proposed method, nine well-known microarray datasets are tested. The support vector machine (SVM) is employed for the classification task to estimate the goodness of the selected subset of genes. The effectiveness of TRIZ optimization operators in MGWO is evaluated by investigating the convergence behavior of GWO with and without TRIZ optimization operators. Moreover, the results of MGWO are compared with seven state-of-art gene selection methods using the same datasets based on classification accuracy and the number of selected genes. The results show that the proposed method achieves the best results in four out of nine datasets and it obtains remarkable results on the remaining datasets. The experimental results demonstrated the effectiveness of the proposed method in searching the gene search space and it was able to find the best gene combinations.
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    Text documents clustering using modified multi-verse optimizer
    (Institute of Advanced Engineering and Science, 2020) Abasi, Ammar Kamal; Khader, Ahamad Tajudin; Al-Betar, Mohammed Azmi; Naim, Syibrah; Awadallah, Mohammed A.; Alomari, Osama Ahmad
    In this study, a multi-verse optimizer (MVO) is utilised for the text document clustering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems. For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the final results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce significant results in comparison with three well-established methods. Copyright c 2020 Insitute of Advanced Engineeering and Science. All rights reserved.
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    Wind Driven Optimization With Smart Home Battery for Power Scheduling Problem in Smart Home
    (IEEE, 2021) Makhadmeh, Sharif Naser; Al-Betar, Mohammed Azmi; Abasi, Ammar Kamal; Awadallah, Mohammed A.; Alyasseri, Zaid Abdi Alkareem; Alomari, Osama Ahmad; Abu Doush, Iyad
    The power scheduling problem in smart home (PSPSH) refers to schedule smart appliances at suitable times in accordance with pricing system(s). Smart appliances can be rearranged and scheduled by moving their operation times from one period to another. Such a process aims to decrease the electricity bill and the power demand at peak periods and improve user satisfaction. Different optimization approaches were proposed to address PSPSH, where metaheuristics are the most common methods. In this paper, wind-driven optimization (WDO) is adapted to handle PSPSH and optimize its objectives. Smart home battery (SHB) is modelled and used to improve the schedules by storing power at off-peak periods and using the stored power at peak periods. In the simulation results, the proposed approach proves its efficiency in reducing electricity bills and improving user satisfaction. In addition, WDO is compared with bacterial foraging optimization algorithm (BFOA) to evaluate and investigate its performance. WDO outperforms BFOA in optimizing PSPSH objectives.