Hitesh Mohapatra^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076726

Abstract This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge. Existing literature shows that edge-based distributed intelligence reduces cloud dependency. It addresses transmission latency, device energy use, and bandwidth limits. Recent optimization strategies employ dynamic task offloading mechanisms to determine optimal workload placement across local devices and edge servers without centralized coordination. Empirical findings from the literature indicate performance improvements with latency reductions of approximately 32.8% and energy efficiency gains of 27.4% compared to conventional cloud-centric models.… More >

Ying Yao¹, Le Tian^1,2,3, Yuxiang Hu^1,2,3,*, Pengshuai Cui^1,2,3

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075747

Abstract In the process of programmable networks simplifying network management and increasing network flexibility through custom packet behavior, security incidents caused by human logic errors are seriously threatening their safe operation, robust verification methods are required to ensure their correctness. As one of the formal methods, symbolic execution offers a viable approach for verifying programmable networks by systematically exploring all possible paths within a program. However, its application in this field encounters scalability issues due to path explosion and complex constraint-solving. Therefore, in this paper, we propose NetVerifier, a scalable verification system for programmable networks. To… More >

Wenchao Shi¹, Tao Wei², Chuan Yang³, Qichao Fan³, Hongxi Liu⁴, Bin Shao^5,*, Peng Jing^4,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075293

Abstract Enhancing the strength of nanotwinned aluminum (Al) is essential for the development of next-generation high-end chip technology. To better understand the detwinning behavior of nanotwinned Al under conditions with no resolved shear stress acting on the twin boundaries, we conducted molecular dynamics simulations of uniaxial tensile deformation in nanotwinned single-crystal Al at room temperature. Detwinning is observed only when the twin boundary spacing is 7.01 Å. At larger spacings, twin boundaries remain parallel to the loading direction, with no rotation or bending, indicating negligible migration. Detwinning is triggered by localized stress from dislocation interactions, with More >

Qi Wang, Kelvin Amos Nicodemas^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.074305

Abstract Multivariate time series forecasting plays a crucial role in decision-making for systems like energy grids and transportation networks, where temporal patterns emerge across diverse scales from short-term fluctuations to long-term trends. However, existing Transformer-based methods often process data at a single resolution or handle multiple scales independently, overlooking critical cross-scale interactions that influence prediction accuracy. To address this gap, we introduce the Hierarchical Attention Transformer (HAT), which enables direct information exchange between temporal hierarchies through a novel cross-scale attention mechanism. HAT extracts multi-scale features using hierarchical convolutional-recurrent blocks, fuses them via temperature-controlled mechanisms, and optimizes More >

Qian Hu, Lei Tan^*, Qingjun Yuan, Zong Zuo, Yan Li

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076137

Abstract Multi-behavior recommendation methods leverage various types of user interaction behaviors to make personalized recommendations. Behavior paths formed by diverse user interactions reveal distinctive patterns between users and items. Modeling these behavioral paths captures multidimensional behavioral features, which enables accurate learning of user preferences and improves recommendation accuracy. However, existing methods share two critical limitations: (1) Lack of modeling for the diversity of behavior paths; (2) Ignoring the impact of item attribute information on user behavior paths. To address these issues, we propose a Directed Behavior path graph-based Multi-behavior Recommendation method (DBMR). Specifically, we first construct… More >

Dmytro Tymoshchuk^1,*, Oleh Yasniy¹, Iryna Didych², Pavlo Maruschak^3,*, Yuri Lapusta⁴

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077062

Abstract This article presents an approach to predicting the hysteresis behavior of shape memory alloys (SMAs) using a Temporal Convolutional Network (TCN) deep learning model, followed by the interpretation of the results using Explainable Artificial Intelligence (XAI) methods. The experimental dataset was prepared based on cyclic loading tests of nickel-titanium wire at loading frequencies of 0.3, 0.5, 1, 3, and 5 Hz. For training, validation, and testing, 100–250 loading-unloading cycles were used. The input features of the models were stress σ (MPa), cycle number (Cycle parameter), and loading-unloading stage indicator, while the output variable was strain… More >

Yan-Hao Huang^*, Yu-Tse Huang

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076800

Abstract Diabetic Retinopathy (DR) is a common microvascular complication of diabetes that progressively damages the retinal blood vessels and, without timely treatment, can lead to irreversible vision loss. In clinical practice, DR is typically diagnosed by ophthalmologists through visual inspection of fundus images, a process that is time-consuming and prone to inter- and intra-observer variability. Recent advances in artificial intelligence, particularly Convolutional Neural Networks (CNNs) and Transformer-based models, have shown strong potential for automated medical image classification and decision support. In this study, we propose a Position-Wise Attention-Enhanced Vision Transformer (PWAE-ViT), which integrates a positional attention… More >

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

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076515

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 >

Rama Gautam^1,*, Nikhil Marriwala¹, Reeta Devi¹, Dhariya Singh Arya²

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076136

Abstract In this study, the multi-scale (meso and macro) modelling was used to predict the electric response of the material. Porosity was introduced through a sugar-templating process to enhance compressibility and sensitivity. Mean-field homogenization was employed to predict the electrical conductivity of the nanocomposites, which was validated experimentally through I–V characterisation, confirming stable Ohmic behavior. The homogenised material parameters were incorporated into COMSOL Multiphysics to simulate diaphragm deflection and capacitance variation under applied pressure. Experimental results showed a linear and stable capacitance response at the force magnitude of 0–7 N. The Graphene nanoplatelets (GnP)–Polydimethylsiloxane (PDMS) sensor demonstrated More >

Hongyu Xu^1,*, Xiaofeng Liu¹, Zhao Li¹, Shuai Zhang², Jintao Cui¹, Zongshuai Zhou¹, Longlong Chen¹, Mengen Zhang¹

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075413

Abstract In two-scale topology optimization, enhancing the connectivity between adjacent microstructures is crucial for achieving the collaborative optimization of micro-scale performance and macro-scale manufacturability. This paper proposes a two-scale concurrent topology optimization strategy aimed at improving the interface connection strength. This method employs a parametric approach to explicitly divide the micro-design domain into a “boundary connection region” and a “free design domain” at the initial stage of optimization. The boundary connection region is used to generate a connection layer that enhances the interface strength, while the free design domain is not constrained by this layer, thus… More >