Menwa Alshammeri^1,2,*, Noshina Tariq³, NZ Jhanji^4,5, Mamoona Humayun⁶, Muhammad Attique Khan⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074627 - 29 January 2026

Abstract Artificial Intelligence (AI) is changing healthcare by helping with diagnosis. However, for doctors to trust AI tools, they need to be both accurate and easy to understand. In this study, we created a new machine learning system for the early detection of Autism Spectrum Disorder (ASD) in children. Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning. For this, we combined several different models, including Random Forest, XGBoost, and Neural Networks, into a single, more powerful framework. We used two different types More >

Kinzah Noor¹, Agbotiname Lucky Imoize^2,*, Michael Adedosu Adelabu³, Cheng-Chi Lee^4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1575-1664, 2025, DOI:10.32604/cmes.2025.073200 - 26 November 2025

Abstract The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern… More > Graphic Abstract

Xianwei Li^1,2, Bo Wei^3,4, Xiaoying Yang^5,6,*, Amr Tolba⁷, Zijian Zeng⁸, Osama Alfarraj^7,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3437-3455, 2025, DOI:10.32604/cmc.2025.067200 - 23 September 2025

Abstract With the advancements of the next-generation communication networking and Internet of Things (IoT) technologies, a variety of computation-intensive applications (e.g., autonomous driving and face recognition) have emerged. The execution of these IoT applications demands a lot of computing resources. Nevertheless, terminal devices (TDs) usually do not have sufficient computing resources to process these applications. Offloading IoT applications to be processed by mobile edge computing (MEC) servers with more computing resources provides a promising way to address this issue. While a significant number of works have studied task offloading, only a few of them have considered More >

Wael A. Altabey^*

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1145-1165, 2025, DOI:10.32604/sdhm.2025.068922 - 05 September 2025

Abstract In the fabrication and monitoring of parts in composite structures, which are being used more and more in a variety of engineering applications, the prediction and fatigue failure detection in composite materials is a difficult problem. This difficulty arises from several factors, such as the lack of a comprehensive investigation of the fatigue failure phenomena, the lack of a well-defined fatigue damage theory used for fatigue damage prediction, and the inhomogeneity of composites because of their multiple internal borders. This study investigates the fatigue behavior of carbon fiber reinforced with epoxy (CFRE) laminated composite plates… More > Graphic Abstract

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 >

Sarmad K. Ibrahim^1,*, Saif A. Abdulhussien², Hazim M. ALkargole¹, Hassan H. Qasim¹

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5223-5238, 2025, DOI:10.32604/cmc.2025.066548 - 30 July 2025

Abstract The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), and Massive Machine Type Communication (mMTC)—present tremendous challenges to conventional methods of bandwidth allocation. A new deep reinforcement learning-based (DRL-based) bandwidth allocation system for real-time, dynamic management of 5G radio access networks is proposed in this paper. Unlike rule-based and static strategies, the proposed system dynamically updates itself according to shifting network conditions such as traffic load and channel conditions to maximize the achievable throughput, fairness, and compliance with QoS requirements. By using… More >

Yuliang He¹, Weihong Lou¹, Da Hang², Youhua Su^3,*

Structural Durability & Health Monitoring, Vol.19, No.4, pp. 887-902, 2025, DOI:10.32604/sdhm.2025.062070 - 30 June 2025

Abstract The computational accuracy and efficiency of modeling the stress spectrum derived from bridge monitoring data significantly influence the fatigue life assessment of steel bridges. Therefore, determining the optimal stress spectrum model is crucial for further fatigue reliability analysis. This study investigates the performance of the REBMIX algorithm in modeling both univariate (stress range) and multivariate (stress range and mean stress) distributions of the rain-flow matrix for a steel arch bridge, using Akaike’s Information Criterion (AIC) as a performance metric. Four types of finite mixture distributions—Normal, Lognormal, Weibull, and Gamma—are employed to model the stress range. More >

Dongwei Wang^1,*, Wensheng Ma², Weiguo Zhao¹, Rui Cao², Youchao Yang²

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.4, pp. 877-890, 2025, DOI:10.32604/fdmp.2024.055220 - 06 May 2025

Abstract Cavitation is an unavoidable phenomenon in the operation of centrifugal pumps. Prolonged cavitation can cause significant damage to the components of the flow channel, and in severe cases, it may even interfere with the normal energy exchange processes within the pump. Therefore, effective monitoring of cavitation in centrifugal pumps is crucial. This article presents a study that approaches the issue from an acoustic perspective, using experimental methods to gather and analyze acoustic data at the inlet and outlet of centrifugal pumps across various flow rates, with hydrophones as the primary measuring instruments. Results show that… More >

Jagadesh Balasubramani¹, Surendran Rajendran^1,*, Mohammad Zakariah², Abeer Alnuaim²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2793-2807, 2025, DOI:10.32604/cmc.2025.062266 - 16 April 2025

Abstract Autism spectrum disorder (ASD) is a multifaceted neurological developmental condition that manifests in several ways. Nearly all autistic children remain undiagnosed before the age of three. Developmental problems affecting face features are often associated with fundamental brain disorders. The facial evolution of newborns with ASD is quite different from that of typically developing children. Early recognition is very significant to aid families and parents in superstition and denial. Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD. Presently, artificial intelligence (AI) significantly contributes to the emerging computer-aided… More >

Jun Zhang^1,#,*, Li Wang^1,#, Shan Liu¹, Yurong Yang², Jingyi Fan³, Yijia Zhang¹

International Journal of Mental Health Promotion, Vol.27, No.3, pp. 287-302, 2025, DOI:10.32604/ijmhp.2025.060064 - 31 March 2025

Abstract Background: Parents of children with autism are susceptible to parenting burnout due to tremendous parenting burden and parenting challenges. Parenting burnout has a detrimental effect on both children with autism and their parents. However, the underlying mechanisms that lead to parenting burnout remain unclear. This study aimed to investigate the relationship between parenting stress and parenting burnout, along with the serial mediation effect of social support and coping strategies in the context of families with autistic children. Methods: We conducted a cross-sectional study in 231 parents of autistic children in four autism facilities located in… More >