Ghada Atteia^1,*, Mohammed Dabboor², Konstantinos Karantzalos³, Maali Alabdulhafith¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1747-1767, 2025, DOI:10.32604/cmc.2025.063363 - 09 June 2025

Abstract This study explores the integration of Synthetic Aperture Radar (SAR) imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection. This study proposes a novel hybrid approach for oil spill detection. The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization (BHHO) and Principal Component Analysis (PCA) for improved feature extraction and selection from input SAR imagery. Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images. The BHHO and PCA algorithms were implemented to identify subsets of… More >

Reza Goodarzi¹, Ali Jalali^1,*, Omid Hashemi Pour Tafreshi¹, Jalil Mazloum¹, Peyman Beygi²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 653-679, 2025, DOI:10.32604/cmc.2025.062803 - 09 June 2025

Abstract Acute lymphoblastic leukemia (ALL) is characterized by overgrowth of immature lymphoid cells in the bone marrow at the expense of normal hematopoiesis. One of the most prioritized tasks is the early and correct diagnosis of this malignancy; however, manual observation of the blood smear is very time-consuming and requires labor and expertise. Transfer learning in deep neural networks is of growing importance to intricate medical tasks such as medical imaging. Our work proposes an application of a novel ensemble architecture that puts together Vision Transformer and EfficientNetV2. This approach fuses deep and spatial features to… More >

Saleh Ateeq Almutairi^*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2599-2624, 2023, DOI:10.32604/csse.2023.041609 - 28 July 2023

Abstract Heart disease is a primary cause of death worldwide and is notoriously difficult to cure without a proper diagnosis. Hence, machine learning (ML) can reduce and better understand symptoms associated with heart disease. This study aims to develop a framework for the automatic and accurate classification of heart disease utilizing machine learning algorithms, grid search (GS), and the Aquila optimization algorithm. In the proposed approach, feature selection is used to identify characteristics of heart disease by using a method for dimensionality reduction. First, feature selection is accomplished with the help of the Aquila algorithm. Then,… More >

Amjed Al Fahoum^1,*, Tahani A. Ghobon²

Journal of New Media, Vol.5, No.1, pp. 1-22, 2023, DOI:10.32604/jnm.2023.037583 - 14 June 2023

Abstract A groundbreaking method is introduced to leverage machine learning algorithms to revolutionize the prediction of success rates for science fiction films. In the captivating world of the film industry, extensive research and accurate forecasting are vital to anticipating a movie’s triumph prior to its debut. Our study aims to harness the power of available data to estimate a film’s early success rate. With the vast resources offered by the internet, we can access a plethora of movie-related information, including actors, directors, critic reviews, user reviews, ratings, writers, budgets, genres, Facebook likes, YouTube views for movie… More >

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy², Fadwa Alrowais^1,*, Sunil Kumar³, Abdelhameed Ibrahim⁴, Abdelaziz A. Abdelhamid^5,6

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4027-4041, 2023, DOI:10.32604/cmc.2023.033039 - 31 October 2022

Abstract In data mining and machine learning, feature selection is a critical part of the process of selecting the optimal subset of features based on the target data. There are 2ⁿ potential feature subsets for every n features in a dataset, making it difficult to pick the best set of features using standard approaches. Consequently, in this research, a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm (ASSOA) has been proposed. When using metaheuristics to pick features, it is common for the selection of features to vary across runs, which can lead… More >

S. J. K. Jagadeesh Kumar¹, P. Parthasarathi², Mehedi Masud³, Jehad F. Al-Amri⁴, Mohamed Abouhawwash^5,6,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2909-2924, 2023, DOI:10.32604/iasc.2023.030335 - 17 August 2022

Abstract The main task of thyroid hormones is controlling the metabolism rate of humans, the development of neurons, and the significant growth of reproductive activities. In medical science, thyroid disorder will lead to creating thyroiditis and thyroid cancer. The two main thyroid disorders are hyperthyroidism and hypothyroidism. Many research works focus on the prediction of thyroid disorder. To improve the accuracy in the classification of thyroid disorder this paper proposes optimization-based feature selection by using differential evolution with the Butterfly optimization algorithm (DE-BOA). For the classifier fuzzy C-means algorithm (FCM) is used. The proposed DEBOA-FCM is More >