Kuen-Suan Chen^1,2,3, Tsai-Sung Lin⁴, Ruey-Chyn Tsaur^4,*, Minh T. N. Nguyen⁵

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.079476 - 09 April 2026

Abstract As artificial intelligence, the Internet of Things, edge computing, and blockchain are increasingly integrated into long-term care (LTC) services, policymakers face complex and often non-compensatory trade-offs among affordability, workforce sustainability, service reliability, and data governance. Conventional compensatory evaluation models tend to mask critical structural weaknesses and limiting their usefulness for Smart LTC policy assessment. This study proposes and applies a Fuzzy Trade-Off-Aware Scoring with Conflicts (Fuzzy TASC) framework to evaluate Smart LTC system performance. Four digital-integration configurations—conventional cloud-based LTC, AI+IoT, AI+Edge, and AI+Blockchain—were compared across 12 OECD countries. A Monte Carlo perturbation procedure was incorporated… More >

Ans D. Alghamdi^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075985 - 09 April 2026

Abstract How can AI assist doctors in generating clinical reports without compromising patient privacy? This question motivates our development of PrivLLM-Guard, a novel framework for differentially private large language models (LLMs) tailored to real-time confidential medical text generation and summarization. While LLMs have shown promise in automating clinical documentation, the sensitivity of healthcare data demands rigorous privacy protections. PrivLLM-Guard addresses this need by combining advanced—differential privacy techniques with adaptive noise calibration, ensuring robust privacy guarantees without sacrificing utility. The framework integrates bidirectional transformer encoders with autoregressive decoders, further enhanced by privacy-aware attention and gradient perturbation mechanisms. Extensive More >

Muhammad Adnan Tariq¹, Sunawar Khan², Tehseen Mazhar^2,3, Tariq Shahzad⁴, Sahar Arooj⁵, Khmaies Ouahada⁶, Muhammad Adnan Khan^7,*, Habib Hamam^8,9,10,11

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078672 - 30 March 2026

Abstract The contemporary smart cities, smart homes, smart buildings, and smart health care systems are the results of the explosive growth of Internet of Things (IoT) devices and deep learning. Yet the centralized training paradigms have fundamental issues in data privacy, regulatory compliance, and ownership silo alongside the scaled limitations of the real-life application. The concept of Federated Deep Learning (FDL) is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings. It is an overview of the privacy-preserving… More >

Mohammed Abdullah Alsuwaiket^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078169 - 30 March 2026

Abstract The healthcare field is fraught with challenges associated with severe class imbalance, wherein such critical conditions like sepsis, cardiac arrest, and drug adverse reactions are rare but have dire clinical consequences. This paper presents a new framework, Deep Reinforcement Adaptive Gradient Optimization Network to Mining Rare Events (DRAGON-MINE), to demonstrate how deep reinforcement learning can be used synergistically with adaptive gradient optimization and address the inherent weaknesses of current methods in the prediction of rare health events. The suggested architecture uses a dual-pathway consisting of a reinforcement learning agent to dynamically reweigh samples and an… More >

Laura Lacomba-Trejo^1,*, Francisco González-Sala¹, Sandra Simó², Florencia Talmón-Knuser³

International Journal of Mental Health Promotion, Vol.28, No.3, 2026, DOI:10.32604/ijmhp.2026.077053 - 31 March 2026

Abstract Backgrounds: Somatization and eating-related problems in adolescents living in residential care may be shaped by the interplay of risk and protective factors, including gender, relational trauma, attachment patterns, emotional intelligence, and perceived social support. This study examined how gender, relational trauma, attachment dimensions, resilience, and emotional intelligence contribute to the presence of somatic and eating difficulties in this population. Methods: The sample included 46 adolescents (63% female; ages 12–17, Mean = 14.85, Standard Deviation (SD) = 1.49) residing in child protection institutions in Uruguay. Participants completed self-report measures assessing childhood relational trauma (CaMir), attachment dimensions (anxiety… More > Graphic Abstract

Xiaojiao Sun^1,*, Shuge Zhang^2,3,*

International Journal of Mental Health Promotion, Vol.28, No.3, 2026, DOI:10.32604/ijmhp.2026.073845 - 31 March 2026

Abstract Background: Depression is a growing public health concern among Chinese children and adolescents, with substantial healthcare costs. Physical activity (PA) and organized sport are modifiable behaviours protective against depression. This study, therefore, estimated the healthcare costs of depression attributable to insufficient PA and organized sport participation. Methods: A cost-of-illness approach with population attributable fraction (PAF) was applied. Relative risks were derived from longitudinal evidence, prevalence estimates from national Chinese surveys, and depression case numbers from the Global Burden of Disease 2021. Direct healthcare costs were extrapolated from European Union estimates, adjusted to 2024 US dollars (USD),… More >

Sara Tehsin^1,*, Muhammad John Abbas², Inzamam Mashood Nasir¹, Fadwa Alrowais³, Reham Abualhamayel⁴, Abdulsamad Ebrahim Yahya⁵, Radwa Marzouk⁶

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076515 - 12 March 2026

Abstract Globally, diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness. To treat these vision disorders effectively, proper diagnosis must occur in a timely manner, and with conventional methods such as fundus photography, optical coherence tomography (OCT), and slit-lamp imaging, much depends on an expert’s interpretation of the images, making the systems very labor-intensive to operate. Moreover, clinical settings face difficulties with inter-observer variability and limited scalability with these diagnostic devices. To solve these problems, we have developed the Efficient Channel-Spatial Attention Network (ECSA-Net), a new deep learning-based… More >

Pablo Guillén¹, Enrique Frias-Martinez^2,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076400 - 12 March 2026

Abstract Alzheimer’s disease (AD) diagnosis and prognosis increasingly rely on machine learning (ML) models. Although these models provide good results, clinical adoption is limited by the need for technical expertise and the lack of trustworthy and consistent model explanations. SHAP (SHapley Additive exPlanations) is commonly used to interpret AD models, but existing studies tend to focus on explanations for isolated tasks, providing little evidence about their robustness across disease stages, model architectures, or prediction objectives. This paper proposes a multi-level explainability framework that measures the coherence, stability and consistency of explanations by integrating: (1) within-model coherence… More >

Chia-Hui Liu¹, Zhen-Yu Wu^2,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075495 - 12 March 2026

Abstract Federated Learning (FL) enables collaborative medical model training without sharing sensitive patient data. However, existing FL systems face increasing security risks from post quantum adversaries and often incur non-negligible computational and communication overhead when encryption is applied. At the same time, training high performance AI models requires large volumes of high quality data, while medical data such as patient information, clinical records, and diagnostic reports are highly sensitive and subject to strict privacy regulations, including HIPAA and GDPR. Traditional centralized machine learning approaches therefore pose significant challenges for cross institutional collaboration in healthcare. To address… More >

Natalie Y. Luo¹, Edie L. Sperling^2,*, Juliette Lum²

International Journal of Mental Health Promotion, Vol.28, No.2, 2026, DOI:10.32604/ijmhp.2026.074948 - 27 February 2026

Abstract Objectives: Healthcare students experience significant stress due to their rigorous graduate school curricula. These levels of stress are associated with higher risks of depression, self-harm, and exhaustion. Coping interventions have been shown to help students develop healthy stress coping strategies. The purpose of this systematic review and meta-analysis was to examine the diverse array of coping interventions and what characteristics of coping interventions were most effective at decreasing stress among healthcare students. Methods: Any intervention designed to address coping for academic stress was included among medical, dental, nursing, physician assistant, allied health, veterinary, psychology, etc. students.… More >