Abdul Ahad^1,2, Ira Puspitasari^1,3,*, Jiangbin Zheng², Shamsher Ullah⁴, Farhan Ullah⁵, Sheikh Tahir Bakhsh⁶, Ivan Miguel Pires^7,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5115-5134, 2025, DOI:10.32604/cmc.2025.062605 - 06 March 2025

Abstract This research explores the use of Fuzzy K-Nearest Neighbor (F-KNN) and Artificial Neural Networks (ANN) for predicting heart stroke incidents, focusing on the impact of feature selection methods, specifically Chi-Square and Best First Search (BFS). The study demonstrates that BFS significantly enhances the performance of both classifiers. With BFS preprocessing, the ANN model achieved an impressive accuracy of 97.5%, precision and recall of 97.5%, and an Receiver Operating Characteristics (ROC) area of 97.9%, outperforming the Chi-Square-based ANN, which recorded an accuracy of 91.4%. Similarly, the F-KNN model with BFS achieved an accuracy of 96.3%, precision More >

Ali Hasan Dakheel^1,*, Mohammed Raheem Mohammed¹, Zainab Ali Abd Alhuseen¹, Wassan Adnan Hashim^2,3

Intelligent Automation & Soft Computing, Vol.40, pp. 195-220, 2025, DOI:10.32604/iasc.2025.061622 - 28 February 2025

Abstract The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health. This study aims to enhance risk prediction in pregnancy with a novel deep learning model based on a Long Short-Term Memory (LSTM) generator, designed to capture temporal relationships in cardiotocography (CTG) data. This methodology integrates CTG signals with demographic characteristics and utilizes preprocessing techniques such as noise reduction, normalization, and segmentation to create high-quality input for the model. It uses convolutional layers to extract spatial information, followed by LSTM layers to model sequences for superior predictive performance. The overall More >

Laith Abualigah^*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1539-1592, 2025, DOI:10.32604/cmc.2025.060765 - 17 February 2025

Abstract Particle Swarm Optimization (PSO) has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields. This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications, but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms. Covering six strategic areas, which include Data Mining, Machine Learning, Engineering Design, Energy Systems, Healthcare, and Robotics, the study demonstrates the versatility and effectiveness of the PSO. Experimental results are, however, used to show the strong and More >

Tanzila Saba¹, Muhammad Mujahid¹, Shaha Al-Otaibi², Noor Ayesha³, Amjad Rehman Khan^1,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3337-3353, 2025, DOI:10.32604/cmc.2025.060163 - 17 February 2025

Abstract Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone; however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with… More >

Mohammed Salih Ahmed¹, Atta Rahman^2,*, Yahya Alhabboub¹, Khalid Alzahrani¹, Hassan Baragbah¹, Basel Altaha¹, Hussein Alkatout¹, Sardar Asad Ali Biabani^3,4, Rashad Ahmed⁵, Aghiad Bakry²

Intelligent Automation & Soft Computing, Vol.40, pp. 149-175, 2025, DOI:10.32604/iasc.2025.059080 - 12 February 2025

Abstract This study rigorously evaluates the potential of transfer learning in diagnosing retinal eye diseases using advanced models such as YOLOv8, Xception, ConvNeXtTiny, and VGG16. All models were trained on the esteemed RFMiD dataset, which includes images classified into six critical categories: Diabetic Retinopathy (DR), Macular Hole (MH), Diabetic Neuropathy (DN), Optic Disc Changes (ODC), Tesselated Fundus (TSLN), and normal cases. The research emphasizes enhancing model performance by prioritizing recall metrics, a crucial strategy aimed at minimizing false negatives in medical diagnostics. To address the challenge of imbalanced data, we implemented effective preprocessing techniques, including cropping,… More >

Rakesh Kumar^1,*, Ajay Singh², Ahmed Subahi Ahmed Kassar³, Mohammed Ismail Humaida³, Sudhanshu Joshi⁴, Manu Sharma⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1289-1349, 2025, DOI:10.32604/cmes.2025.059152 - 27 January 2025

Abstract This review aims to analyze the development and impact of Artificial Intelligence (AI) in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives. It is extensively devoted to AI technology deployment relevant to disease management, healthcare delivery, epidemiology, and policy-making. However, its AI is culturally sensitive and ethically grounded in Islam. Based on the PRISMA framework, an SLR evaluated primary academic literature, cases, and practices of Saudi Arabia’s AI implementation in the public healthcare sector. Instead, it categorizes prior research based on how AI can work, the issues it poses, and… More >

Hariharasakthisudhan Ponnarengan^1,*, Sivakumar Rajendran², Vikas Khalkar³, Gunapriya Devarajan⁴, Logesh Kamaraj⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 1-48, 2025, DOI:10.32604/cmes.2024.056605 - 17 December 2024

Abstract The purpose of this review is to explore the intersection of computational engineering and biomedical science, highlighting the transformative potential this convergence holds for innovation in healthcare and medical research. The review covers key topics such as computational modelling, bioinformatics, machine learning in medical diagnostics, and the integration of wearable technology for real-time health monitoring. Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems, while machine learning algorithms have improved the accuracy of disease prediction and diagnosis. The synergy between bioinformatics and computational techniques has led to breakthroughs in More >

Vajratiya Vajrobol¹, Geetika Jain Saxena², Amit Pundir², Sanjeev Singh¹, Akshat Gaurav³, Savi Bansal^4,5, Razaz Waheeb Attar⁶, Mosiur Rahman⁷, Brij B. Gupta^7,8,9,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 49-90, 2025, DOI:10.32604/cmes.2024.056500 - 17 December 2024

Abstract Mental health is a significant issue worldwide, and the utilization of technology to assist mental health has seen a growing trend. This aims to alleviate the workload on healthcare professionals and aid individuals. Numerous applications have been developed to support the challenges in intelligent healthcare systems. However, because mental health data is sensitive, privacy concerns have emerged. Federated learning has gotten some attention. This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems. It explores various dimensions of federated learning in mental health, such as More >

Bassem Toeama¹, Nathan Perlis², Paul Grootendorst¹, William Orovan³, Emmanuel Papadimitropoulos^1,4

Canadian Journal of Urology, Vol.31, No.4, pp. 11963-11970, 2024

Abstract Introduction: Prostate cancer is the third leading cause of death from cancer among Canadian men. High intensity focused ultrasound (HIFU) is a novel approach for primary treatment of localized prostate cancer. Little is known, however, about its costs. We aimed to collect the direct costs and health-related quality of life (HRQoL) data of HIFU in primary treatment of localized low and intermediate risk prostate cancer in Ontario.
Materials and methods: We collected direct costs and HRQoL data of 20 patients with localized low or intermediate risk prostate cancer who received whole gland HIFU at a privately owned… More >

Omer Nabeel Dara^1,*, Tareq Abed Mohammed², Abdullahi Abdu Ibrahim¹

Intelligent Automation & Soft Computing, Vol.39, No.6, pp. 1007-1033, 2024, DOI:10.32604/iasc.2024.058736 - 30 December 2024

Abstract Healthcare polypharmacy is routinely used to treat numerous conditions; however, it often leads to unanticipated bad consequences owing to complicated medication interactions. This paper provides a graph convolutional network (GCN)-based model for identifying adverse effects in polypharmacy by integrating pharmaceutical data from electronic health records (EHR). The GCN framework analyzes the complicated links between drugs to forecast the possibility of harmful drug interactions. Experimental assessments reveal that the proposed GCN model surpasses existing machine learning approaches, reaching an accuracy (ACC) of 91%, an area under the receiver operating characteristic curve (AUC) of 0.88, and an More >