Parul Dubey^1,*, Pushkar Dubey², Mohammed Zakariah^3,4,*, Abdulaziz S. Almazyad⁴, Deema Mohammed Alsekait⁵

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3897-3919, 2025, DOI:10.32604/cmc.2025.066882 - 23 September 2025

Abstract Psychological distress detection plays a critical role in modern healthcare, especially in ambient environments where continuous monitoring is essential for timely intervention. Advances in sensor technology and artificial intelligence (AI) have enabled the development of systems capable of mental health monitoring using multi-modal data. However, existing models often struggle with contextual adaptation and real-time decision-making in dynamic settings. This paper addresses these challenges by proposing TRANS-HEALTH, a hybrid framework that integrates transformer-based inference with Belief-Desire-Intention (BDI) reasoning for real-time psychological distress detection. The framework utilizes a multimodal dataset containing EEG, GSR, heart rate, and activity… More >

You-Jyun Yeh¹, Edward T.-H. Chu^1,*, Chia-Rong Lee², Jiun Hsu³, Hui-Mei Wu³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3369-3391, 2025, DOI:10.32604/cmc.2025.065336 - 23 September 2025

Abstract Porters play a crucial role in hospitals because they ensure the efficient transportation of patients, medical equipment, and vital documents. Despite its importance, there is a lack of research addressing the prediction of completion times for porter tasks. To address this gap, we utilized real-world porter delivery data from National Taiwan University Hospital, Yunlin Branch, Taiwan. We first identified key features that can influence the duration of porter tasks. We then employed three widely-used machine learning algorithms: decision tree, random forest, and gradient boosting. To leverage the strengths of each algorithm, we finally adopted an… More >

Batyrkhan Omarov^1,2,3,4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2195-2253, 2025, DOI:10.32604/cmc.2025.064799 - 23 September 2025

Abstract Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing, enabling automated lesion detection, physiological monitoring, and therapy planning with accuracy that rivals expert performance. This survey reviews the principal model families as convolutional, recurrent, generative, reinforcement, autoencoder, and transfer-learning approaches as emphasising how their architectural choices map to tasks such as segmentation, classification, reconstruction, and anomaly detection. A dedicated treatment of multimodal fusion networks shows how imaging features can be integrated with genomic profiles and clinical records to yield more robust, context-aware predictions. To support clinical adoption, we outline post-hoc explainability More >

Jae Sung Son¹, Soo-Jin Kim^2,*

Congenital Heart Disease, Vol.20, No.4, pp. 421-440, 2025, DOI:10.32604/chd.2025.070250 - 18 September 2025

Abstract Background: Advancements in diagnostic tools, surgical techniques, and long-term management have significantly improved survival among individuals with congenital heart disease (CHD), leading to an evolving epidemiologic profile characterized by increasing procedural complexity and a growing adult CHD population. This study aimed to examine nationwide trends in CHD surgeries over a 17-year period, with a focus on temporal shifts in surgical volume, procedural complexity, and age-specific incidence. Methods: A total of 41,608 CHD surgeries and 85,417 surgical procedures performed between 2002 and 2018 were identified from a nationwide health insurance database. Temporal trends were evaluated using segmented… More >

Marie Chaumel¹, Nicolas Brichart², Franck Bruyère¹, Ali Bourgi^1,*

Canadian Journal of Urology, Vol.32, No.4, pp. 359-366, 2025, DOI:10.32604/cju.2025.066470 - 29 August 2025

Abstract Background: The COVID-19 pandemic disrupted healthcare systems globally, raising concerns about delayed cancer diagnosis and treatment. In France, transurethral resection of bladder tumors (TURBT) was prioritized in national urology guidelines to ensure the timely management of urothelial carcinoma. This study aimed to assess the impact of care reorganization on tumor staging, recurrence, palliative care, and mortality in bladder cancer patients from the pre-pandemic through late-pandemic periods. Methods: We conducted a retrospective multicenter study including all patients who underwent TURBT with histologically confirmed urothelial carcinoma between April and December of 2019 (pre-pandemic), 2020 (early pandemic), 2021… More >

Alican Kaya¹, Emre Seyrek², Abdulselami Sarıgül³, Mehmet Şata⁴, Juan Gómez-Salgado^5,6,*, Murat Yıldırım^7,8,*

International Journal of Mental Health Promotion, Vol.27, No.8, pp. 1149-1163, 2025, DOI:10.32604/ijmhp.2025.067388 - 29 August 2025

Abstract Background: Social media addiction, one of the behavioural addictions, is a significant predictor of job performance. It has also been posited that individuals whose fundamental requirements (e.g., sleep) are not sufficiently met and who lack adequate support (e.g., perceived social support) are incapable of effectively harnessing their potential. The primary objective of this study is to examine the mediating effects of sleep disorder and perceived social support on the relationship between social media addiction and job performance. Furthermore, it seeks to explore the moderating effects of perceived social support on sleep disorders and job performance.… More >

Kamepalli S. L. Prasanna¹, Vijaya J², Parvathaneni Naga Srinivasu¹, Babar Shah³, Farman Ali^4,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1603-1630, 2025, DOI:10.32604/cmc.2025.068707 - 29 August 2025

Abstract Cardiovascular disease prediction is a significant area of research in healthcare management systems (HMS). We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance. The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data. We present Clustered Butterfly Optimization Techniques (RoughK-means+BOA) as a new hybrid method for predicting heart disease. This method comprises two phases: clustering data using Roughk-means (RKM) and data analysis using the butterfly optimization algorithm (BOA). The benchmark dataset from the UCI More >

Jihoon Moon¹, Jiyoung Woo^2,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1383-1425, 2025, DOI:10.32604/cmc.2025.067784 - 29 August 2025

Abstract Improving early diagnosis of autism spectrum disorder (ASD) in children increasingly relies on predictive models that are reliable and accessible to non-experts. This study aims to develop such models using Python-based tools to improve ASD diagnosis in clinical settings. We performed exploratory data analysis to ensure data quality and identify key patterns in pediatric ASD data. We selected the categorical boosting (CatBoost) algorithm to effectively handle the large number of categorical variables. We used the PyCaret automated machine learning (AutoML) tool to make the models user-friendly for clinicians without extensive machine learning expertise. In addition,… More >

Maazen Alsabaan¹, Jasmin Praful Bharadiya², Vishwanath Eswarakrishnan³, Adnan Mustafa Cheema⁴, Zaid Bin Faheem⁵, Jehad Ali^6,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1135-1160, 2025, DOI:10.32604/cmc.2025.066969 - 29 August 2025

Abstract The healthcare sector involves many steps to ensure efficient care for patients, such as appointment scheduling, consultation plans, online follow-up, and more. However, existing healthcare mechanisms are unable to facilitate a large number of patients, as these systems are centralized and hence vulnerable to various issues, including single points of failure, performance bottlenecks, and substantial monetary costs. Furthermore, these mechanisms are unable to provide an efficient mechanism for saving data against unauthorized access. To address these issues, this study proposes a blockchain-based authentication mechanism that authenticates all healthcare stakeholders based on their credentials. Furthermore, also… More >

Saad Awadh Alanazi¹, Fahad Ahmad^2,3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 769-800, 2025, DOI:10.32604/cmc.2025.066753 - 29 August 2025

Abstract This study presents a comprehensive and secure architectural framework for the Internet of Medical Things (IoMT), integrating the foundational principles of the Confidentiality, Integrity, and Availability (CIA) triad along with authentication mechanisms. Leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, the proposed system is designed to safeguard Patient-Generated Health Data (PGHD) across interconnected medical devices. Given the increasing complexity and scale of cyber threats in IoMT environments, the integration of Intrusion Detection and Prevention Systems (IDPS) with intelligent analytics is critical. Our methodology employs both standalone and hybrid ML & DL models to… More >