Shanshan Wang^1,2,3, Quan Yuan¹, Weiwei Tan¹, Tengfei Yang¹, Liang Zeng^1,2,3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3057-3075, 2023, DOI:10.32604/cmc.2023.044807

Abstract Feature Selection (FS) is an important problem that involves selecting the most informative subset of features from a dataset to improve classification accuracy. However, due to the high dimensionality and complexity of the dataset, most optimization algorithms for feature selection suffer from a balance issue during the search process. Therefore, the present paper proposes a hybrid Sine-Cosine Chimp Optimization Algorithm (SCChOA) to address the feature selection problem. In this approach, firstly, a multi-cycle iterative strategy is designed to better combine the Sine-Cosine Algorithm (SCA) and the Chimp Optimization Algorithm (ChOA), enabling a more effective search in the objective space. Secondly,… More >

Malek Alzaqebah^1,2,*, 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 Alwohaibi^1,2, 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 >

Noureen Talpur^1,*, 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 >

Doaa Sami Khafaga¹, Amel Ali Alhussan^1,*, Abdelaziz A. Abdelhamid^2,3, Abdelhameed Ibrahim⁴, Mohamed Saber⁵, El-Sayed M. El-kenawy^6,7

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 >

Doaa Sami Khafaga¹, Amel Ali Alhussan^1,*, El-Sayed M. El-kenawy^2,3, Ali E. Takieldeen³, Tarek M. Hassan⁴, Ehab A. Hegazy⁵, Elsayed Abdel Fattah Eid⁶, Abdelhameed Ibrahim⁷, Abdelaziz A. Abdelhamid^8,9

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 >