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 >

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 >

Naeem Ali Askar^1,*, Adib Habbal^1,*, Hassen Hamouda², Abdullah Mohammad Alnajim³, Sheroz Khan⁴

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4625-4658, 2024, DOI:10.32604/cmc.2024.058607 - 19 December 2024

Abstract Named Data Networking (NDN) has emerged as a promising communication paradigm, emphasizing content-centric access rather than location-based access. This model offers several advantages for Internet of Healthcare Things (IoHT) environments, including efficient content distribution, built-in security, and natural support for mobility and scalability. However, existing NDN-based IoHT systems face inefficiencies in their forwarding strategy, where identical Interest packets are forwarded across multiple nodes, causing broadcast storms, increased collisions, higher energy consumption, and delays. These issues negatively impact healthcare system performance, particularly for individuals with disabilities and chronic diseases requiring continuous monitoring. To address these challenges,… More >

Saad Awadh Alanazi^*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4049-4080, 2024, DOI:10.32604/cmc.2024.057211 - 19 December 2024

Abstract Spear Phishing Attacks (SPAs) pose a significant threat to the healthcare sector, resulting in data breaches, financial losses, and compromised patient confidentiality. Traditional defenses, such as firewalls and antivirus software, often fail to counter these sophisticated attacks, which target human vulnerabilities. To strengthen defenses, healthcare organizations are increasingly adopting Machine Learning (ML) techniques. ML-based SPA defenses use advanced algorithms to analyze various features, including email content, sender behavior, and attachments, to detect potential threats. This capability enables proactive security measures that address risks in real-time. The interpretability of ML models fosters trust and allows security… More >

Soha Rawas¹, Cerine Tafran¹, Duaa AlSaeed², Nadia Al-Ghreimil^2,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4575-4601, 2024, DOI:10.32604/cmc.2024.055307 - 19 December 2024

Abstract In the rapidly evolving landscape of healthcare, the integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) holds immense promise for revolutionizing data analytics and decision-making processes. Current techniques for personalized medicine, disease diagnosis, treatment recommendations, and resource optimization in the Internet of Medical Things (IoMT) vary widely, including methods such as rule-based systems, machine learning algorithms, and data-driven approaches. However, many of these techniques face limitations in accuracy, scalability, and adaptability to complex clinical scenarios. This study investigates the synergistic potential of AI-driven optimization techniques and NLP applications in the context of the… More >

Israa Ibraheem Al Barazanchi^1,2,*, Wahidah Hashim¹, Reema Thabit¹, Mashary Nawwaf Alrasheedy^3,4, Abeer Aljohan⁵, Jongwoon Park⁶, Byoungchol Chang⁶

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4787-4832, 2024, DOI:10.32604/cmc.2024.055079 - 19 December 2024

Abstract This research aims to enhance Clinical Decision Support Systems (CDSS) within Wireless Body Area Networks (WBANs) by leveraging advanced machine learning techniques. Specifically, we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) layers and echo state cells. These models are tailored to improve diagnostic precision, particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases. Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex, sequential medical data, struggling with long-term dependencies and data… More >

Wei Wang^1,2, Lan Li^3,*

Psycho-Oncologie, Vol.18, No.4, pp. 257-269, 2024, DOI:10.32604/po.2024.057004 - 04 December 2024

Abstract This study explores the perspectives and challenges faced by family members providing mental support to cancer patients in Beijing, China. The primary objective is to understand the emotional and practical roles family members undertake and the difficulties they encounter. Utilizing a qualitative research design, data were collected through semi-structured interviews with family caregivers of cancer patients. Thematic analysis revealed several key themes: the dual burden of emotional support and caregiving responsibilities, the impact on daily life and personal well-being, the role and effectiveness of external support systems, perceptions of medical staff support, and the common More >

Zulian Zhang¹, Meiyu Yan², Jiaqin Qi^3,*

International Journal of Mental Health Promotion, Vol.26, No.11, pp. 957-966, 2024, DOI:10.32604/ijmhp.2024.055468 - 28 November 2024

Abstract Background: The demanding nature of nursing, characterized by long hours, high-stress environments, and substantial workloads, can significantly impact nurses’ mental health. However, there are limited studies that assessed the influence of workplace environment on nursing mental health based on both quantitative and qualitative approaches. Methods: This study aims to comprehensively investigate the multidimensional relationship between the workplace environment and nurses’ well-being. This cross-sectional study was based on a sample of 3256 nurses from various healthcare settings in Shandong province, China (2022), who participated in the quantitative phase. For the qualitative phase, a subsample of participants… More >