Noura Mohammed Alaskar¹, Muzammil Hussain², Saif Jasim Almheiri¹, Atta-ur-Rahman³, Adnan Khan^4,5,6, Khan M. Adnan^7,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074041 - 10 February 2026

Abstract The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats. The early detection of threats is both necessary and complex, yet these interconnected healthcare settings generate enormous amounts of heterogeneous data. Traditional Intrusion Detection Systems (IDS), which are generally centralized and machine learning-based, often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy. Moreover, traditional AI-driven IDS usually face challenges in handling large-scale, heterogeneous healthcare data while ensuring data… More >

Boram Kim, Guebin Choi^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073500 - 10 February 2026

Abstract Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships, as in Euclidean data such as time series or image grids. However, real-world data frequently involve irregular, interconnected structures, requiring a shift toward non-Euclidean approaches. This study introduces a novel anomaly detection framework designed to handle non-Euclidean data by modeling transactions as graph signals. By leveraging graph convolution filters, we extract meaningful connection strengths that capture relational dependencies often overlooked in traditional methods. Utilizing the Graph Convolutional Networks (GCN) framework, we integrate graph-based embeddings with conventional anomaly detection models, More >

Abdu Salam¹, Fahd M. Aldosari², Donia Y. Badawood³, Farhan Amin^4,*, Isabel de la Torre^5,*, Gerardo Mendez Mezquita⁶, Henry Fabian Gongora⁶

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073342 - 10 February 2026

Abstract Lung cancer remains a major global health challenge, with early diagnosis crucial for improved patient survival. Traditional diagnostic techniques, including manual histopathology and radiological assessments, are prone to errors and variability. Deep learning methods, particularly Vision Transformers (ViT), have shown promise for improving diagnostic accuracy by effectively extracting global features. However, ViT-based approaches face challenges related to computational complexity and limited generalizability. This research proposes the DualSet ViT-PSO-SVM framework, integrating a ViT with dual attention mechanisms, Particle Swarm Optimization (PSO), and Support Vector Machines (SVM), aiming for efficient and robust lung cancer classification across multiple… More >

Aman Aman Ullah^1,2,#, Yanfeng Wu^1,#, Shaheryar Najam³, Nouf Abdullah Almujally⁴, Ahmad Jalal^5,6,*, Hui Liu^1,7,8,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072508 - 10 February 2026

Abstract Human object detection and recognition is essential for elderly monitoring and assisted living however, models relying solely on pose or scene context often struggle in cluttered or visually ambiguous settings. To address this, we present SCENET-3D, a transformer-driven multimodal framework that unifies human-centric skeleton features with scene-object semantics for intelligent robotic vision through a three-stage pipeline. In the first stage, scene analysis, rich geometric and texture descriptors are extracted from RGB frames, including surface-normal histograms, angles between neighboring normals, Zernike moments, directional standard deviation, and Gabor-filter responses. In the second stage, scene-object analysis, non-human objects… More >

Dilfuza Jabborova^1,2,3,*, Khurshid Sulaymanov¹, Muzafar Jabborov⁴, Nayan Ahmed⁵, Tatiana Minkina⁶, Olga Biryukova⁶, Nasir Mehmood^6,*, Vishnu D. Rajput⁶

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.072577 - 30 January 2026

Abstract The combined effects of macronutrients (Nitrogen, Phosphorus, and Potassium-N, P, K) and micronutrient fertilization on turmeric yield, soil enzymatic activity, microbial biomass, and nutrient dynamics remains poorly understood, despite their significance for sustainable soil fertility management and optimizing crop productivity across diverse agroecosystems. To investigate, a net house experiment on sandy loam Haplic Chernozem was conducted to 03 fertilizer regimes, viz. N₇₅P₅₀K₅₀ kg ha⁻¹ (T-2), N₁₂₅P₁₀₀K₁₀₀ kg ha⁻¹ (T-3), and N₁₀₀P₇₅K₇₅ + B₃Zn₆Fe₆ kg ha⁻¹ (T-4). Furthermore, the influence of these treatments was systematically assessed on soil nutrient status (N, P, K), enzymatic activities (alkaline phosphomonoesterase, dehydrogenase, fluorescein diacetate… More >

Huaruo Chen^1,2, Gefan Wang¹, Hancai Qiu¹, Hui Ma¹, Zhentao Peng¹, Ruihan Liu³, Feng Xu^4,*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.073911 - 28 January 2026

Abstract Background: As an important indicator of subjective well-being (SWB), decent work is a key guarantee for the sustainable development of teachers and their psychological health and work quality. Faced with the rapid development of artificial intelligence and the global labor market, vocational college teachers are facing challenges such as workload pressure and limited career development, which may harm their well-being. This study aims to localize the measurement method of decent work in Chinese vocational education based on the theory of the Psychology of Working Theory, and explore the relationship mechanism between organizational support, career adaptability, decent… More >

Jinpan Wang^1,#, Yue Yuan^2,#,*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.071212 - 28 January 2026

Abstract Background: Childhood leukemia, a malignant proliferative disorder of the hematopoietic system and the most common childhood cancer, poses a significant threat to the lives and health of affected children. For parents, a leukemia diagnosis in their child is a profoundly traumatic event. As primary caregivers, they endure immense psychological distress and caregiving stress throughout the prolonged and demanding treatment process, which can adversely affect their own well-being and caregiving capacity. However, the psychological mechanisms, such as the role of mindfulness, linking caregiver stress to parental coping strategies remain underexplored, and evidence-based interventions to support these parents… More >

Mong-Fong Horng^1,#, Thanh-Lam Nguyen^1,#, Thanh-Tuan Nguyen^2,*, Chin-Shiuh Shieh^1,*, Lan-Yuen Guo³, Chen-Fu Hung⁴, Chun-Chih Lo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075064 - 29 January 2026

Abstract The global population is rapidly expanding, driving an increasing demand for intelligent healthcare systems. Artificial intelligence (AI) applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend. Among these applications, mouth motion tracking and mouth-state detection represent an important direction, providing valuable support for diagnosing neuromuscular disorders such as dysphagia, Bell’s palsy, and Parkinson’s disease. In this study, we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices. The proposed system integrates the Facial… More >

Fatima Al-Quayed^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074799 - 29 January 2026

Abstract The rapid proliferation of Internet of Things (IoT) devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative, distributed architectural solutions. This paper proposes FE-ACS (Fog-Edge Adaptive Cybersecurity System), a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge, fog, and cloud layers to optimize security efficacy, latency, and privacy. Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923, while maintaining significantly lower end-to-end latency (18.7 ms) compared to cloud-centric (152.3 ms) and fog-only (34.5… More >

Kang-Jia Wang¹, Hongzhi Zhang², Runsheng Lin^3,*, Jiabin Li⁴, Xiao-Yong Wang^1,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074787 - 29 January 2026

Abstract Conventional low-carbon concrete design approaches have often overlooked carbonation durability and the progressive loss of cover caused by surface scaling, both of which can increase the long-term risk of reinforcement corrosion. To address these limitations, this study proposes an improved design framework for low-carbon slag concrete that simultaneously incorporates carbonation durability and cover scaling effects into the mix proportioning process. Based on experimental data, a linear predictive model was developed to estimate the 28-day compressive strength of slag concrete, achieving a correlation coefficient of R = 0.87711 and a root mean square error (RMSE) of… More >