Browsing by Author "Abasi, Ammar Kamal"

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  • Publication
    Gene selection for microarray data classification based on Gray Wolf Optimizer enhanced with TRIZ-inspired operators
    (2021)
    Abu Zitar, Raed 
    ;
    Alomari, Osama Ahmad
    ;
    Makhadmeh, Sharif Naser
    ;
    Al-Betar, Mohammed Azmi
    ;
    Alyasseri, Zaid Abdi Alkareem
    ;
    Doush, Iyad Abu
    ;
    Abasi, Ammar Kamal
    ;
    Awadallah, Mohammed A.
    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.
    Scopus© Citations 88  112  31
  • Publication
    Review on COVID-19 diagnosis models based on machine learning and deep learning approaches
    (2022)
    Abu Zitar, Raed 
    ;
    Alyasseri, Zaid Abdi Alkareem
    ;
    Al-Betar, Mohammed Azmi
    ;
    Doush, Iyad Abu
    ;
    Awadallah, Mohammed A.
    ;
    Abasi, Ammar Kamal
    ;
    Makhadmeh, Sharif Naser
    ;
    Alomari, Osama Ahmad
    ;
    Abdulkareem, Karrar Hameed
    ;
    Adam, Afzan
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    Damasevicius, Robertas
    ;
    Mohammed, Mazin Abed
    COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.
    Scopus© Citations 150  142  45