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  • Open Access


    Improved Whale Optimization with Local-Search Method for Feature Selection

    Malek Alzaqebah1,2,*, Mutasem K. Alsmadi3, Sana Jawarneh4, Jehad Saad Alqurni5, Mohammed Tayfour3, Ibrahim Almarashdeh3, Rami Mustafa A. Mohammad6, Fahad A. Alghamdi3, Nahier Aldhafferi6, Abdullah Alqahtani6, Khalid A. Alissa7, Bashar A. Aldeeb8, Usama A. Badawi3, Maram Alwohaibi1,2, Hayat Alfagham3

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1371-1389, 2023, DOI:10.32604/cmc.2023.033509

    Abstract Various feature selection algorithms are usually employed to improve classification models’ overall performance. Optimization algorithms typically accompany such algorithms to select the optimal set of features. Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics. The present paper presents two Stages of Local Search models for feature selection based on WOA (Whale Optimization Algorithm) and Great Deluge (GD). GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search. Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm.… More >

  • Open Access


    A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification

    Noureen Talpur1,*, Said Jadid Abdulkadir1, Mohd Hilmi Hasan1, Hitham Alhussian1, Ayed Alwadain2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5799-5820, 2023, DOI:10.32604/cmc.2023.034025

    Abstract Machine learning (ML) practices such as classification have played a very important role in classifying diseases in medical science. Since medical science is a sensitive field, the pre-processing of medical data requires careful handling to make quality clinical decisions. Generally, medical data is considered high-dimensional and complex data that contains many irrelevant and redundant features. These factors indirectly upset the disease prediction and classification accuracy of any ML model. To address this issue, various data pre-processing methods called Feature Selection (FS) techniques have been presented in the literature. However, the majority of such techniques frequently suffer from local minima issues… More >

  • Open Access


    Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

    Doaa Sami Khafaga1, Amel Ali Alhussan1,*, Abdelaziz A. Abdelhamid2,3, Abdelhameed Ibrahim4, Mohamed Saber5, El-Sayed M. El-kenawy6,7

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1469-1482, 2023, DOI:10.32604/csse.2023.031943

    Abstract Arrhythmia has been classified using a variety of methods. Because of the dynamic nature of electrocardiogram (ECG) data, traditional handcrafted approaches are difficult to execute, making the machine learning (ML) solutions more appealing. Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives. Cardiac arrhythmia classification and prediction have greatly improved in recent years. Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish. Every year, it is one of the main reasons of mortality for both men and women, worldwide. For the classification of arrhythmias, this work proposes a… More >

  • Open Access


    Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

    Doaa Sami Khafaga1, Amel Ali Alhussan1,*, El-Sayed M. El-kenawy2,3, Ali E. Takieldeen3, Tarek M. Hassan4, Ehab A. Hegazy5, Elsayed Abdel Fattah Eid6, Abdelhameed Ibrahim7, Abdelaziz A. Abdelhamid8,9

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 749-765, 2022, DOI:10.32604/cmc.2022.029605

    Abstract One of the most common kinds of cancer is breast cancer. The early detection of it may help lower its overall rates of mortality. In this paper, we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal images. The proposed approach starts with data preprocessing the input images and segmenting the significant regions of interest. In addition, to properly train the machine learning models, data augmentation is applied to increase the number of segmented regions using various scaling ratios. On the other hand, to extract the relevant features from the breast cancer cases, a set… More >

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