Sultan Kahla¹, Zuping Zhang^1,*, Majed Alsafyani², Ahmed Emara^3,*, Mohammod Abdullah Bin Hossain⁴, Abdulwahab Osman Sheikhdon¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076402 - 09 April 2026

Abstract The effective diagnosis and treatment planning require the correct classification of the cerebral neoplasia, such as glioma, meningioma, and pituitary tumors. The recent developments in the deep learning field have made a significant contribution to the field of image analysis in medicine; however, Vision Transformers (ViTs) have achieved good results but are computationally complex. This paper presents NeuroTriad-ViT, a proprietary large-scale Vision Transformer of 235 million parameters, which is represented as a high-performance teacher model to classify brain tumors. Knowledge distillation is applied in an attempt to transfer the representations that the teacher learned to… More >

Jie Wang, Deming Lei^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076202 - 09 April 2026

Abstract Both flexible jobshop scheduling and parallel batch processing machine scheduling have been extensively considered; however, the flexible jobshop and parallel batch processing machine scheduling problem (FJPBPMSP) is prevalent in real-life manufacturing processes and is seldom investigated. In this study, FJPBPMSP is examined, where flexible processing and batch processing are performed sequentially. An adaptive imperialist competitive algorithm with cooperation (CAICA) is proposed to minimize makespan and total energy consumption simultaneously. In CAICA, a four-string representation is adopted, and initial empires with novel structures are formed by uniformly dividing the population. An adaptive assimilation and revolution are More >

Zahra Farhadpour^1,*, Tan Fong Ang^1,*, Chee Sun Liew²

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076163 - 09 April 2026

Abstract The heterogeneity and dynamic behavior of fog computing environments introduce major challenges to achieving optimal application placement. Limited fog resources and varying workloads often necessitate offloading applications beyond their local clusters, making it difficult to maintain the required level of service quality under varying conditions. In this context, placement methods must ensure a balanced trade-off between multiple objectives, such as time and cost, while maintaining reliable adherence to constraints like application deadlines and limited fog-node memory. Existing solutions, including heuristic, metaheuristic, learning-based, and hybrid optimization approaches, have been proposed to address these challenges. However, many… More >

Cheng Yang, Xianghong Tang^*, Jianguang Lu, Chaobin Wang

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076126 - 09 April 2026

Abstract Graph neural networks (GNNs) have demonstrated impressive capabilities in processing graph-structured data, yet their vulnerability to adversarial perturbations poses serious challenges to real-world applications. Existing defense methods often fail to handle diverse types of attacks and adapt to dynamic adversarial strategies because they typically rely on static defense mechanisms or focus narrowly on a single robustness dimension. To address these limitations, we propose an adversarial attention-based robustness strategy (AARS), which is a unified framework designed to enhance the robustness of GNNs against structural and feature perturbations. AARS operates in two stages: the first stage employs More >

Awais Ahmad¹, Fakhri Alam Khan^2,3, Awais Ahmad⁴, Gautam Srivastava^5,6,7, Syed Atif Moqurrab^8,*, Abdul Razaque⁹, Dina S. M. Hassan^10,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076104 - 09 April 2026

Abstract Collision avoidance is recognized as a critical challenge in Vehicular Ad-Hoc Networks (VANETs), which demand real-time decision-making. It plays a vital role in ensuring road safety and traffic efficiency. Traditional approaches like rule-based systems and heuristic methods fail to provide optimal solutions in dynamic and unpredictable traffic scenarios. They cannot balance multiple objectives like minimizing collision risk, ensuring passenger comfort, and optimizing fuel efficiency, leading to suboptimal performance in real-world conditions. To tackle collision avoidance, this paper introduces a novel approach by defining the issue as an optimal control problem and solving it using the… More >

Mohammed A. Ahmed¹, Jian Dong^2,*, Ronghua Shi², Ammar Nassr³, Hani Almaqtari³, Ala A. Alsanabani³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075616 - 09 April 2026

Abstract Facial Expression Recognition (FER) is an essential endeavor in computer vision, applicable in human-computer interaction, emotion assessment, and mental health surveillance. Although Convolutional Neural Networks (CNNs) have proven effective in Facial Emotion Recognition, they encounter difficulties in capturing long-range connections and global context. To address these constraints, we propose Spatial Quad-Similarity Network (SQSNet), an innovative hybrid framework that integrates the local feature extraction capabilities of CNNs with the global contextual modeling efficacy of Swin Transformers via a cohesive fusion technique. SQSNet introduces the Spatial Quad-Similarity (SQS) module, a feature refinement approach that amplifies discriminative characteristics… More >

He Tian^1,2, Ziliang Zhu^1,2, Jiangping Li^1,2, Ziyun Li^1,2, Baofeng Tang^1,2, Pengfei Ju^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075309 - 09 April 2026

Abstract As the number of electric vehicles continues to rise, pressure on charging infrastructure grows increasingly intense. Mobile charging technology, with its flexibility and deployability, has emerged as an effective solution. Within this technology, charging robots or vehicles must autonomously locate and dock with charging ports. Consequently, precise and stable charging port recognition constitutes both a prerequisite and the core bottleneck for achieving automated operations in mobile charging systems. However, in practical scenarios, charging ports often prove difficult to detect reliably due to factors such as physical obstructions, variations in lighting, and long shooting distances. To… More >

Chinmai Bhat^1,2, Mayur Jiyalal Prajapati², Yulius Shan Romario³, Wojciech Macek⁴, Maziar Ramezani⁵, Cho-Pei Jiang^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075197 - 09 April 2026

Abstract This study aims to develop a digital twin framework for fabricating automotive components through additive manufacturing (AM) technology. The framework comprises topology optimization (TO), finite element analysis (FEA), and fabrication analysis using Simufact Additive, which ensures the first-time-right fabrication of the component. Using TO-FEA, the component is designed with reduced overall weight without compromising the structural and functional performance. After the successful design of the component, it is analyzed for fabrication feasibility before undergoing the actual fabrication process. In the present study, an automotive flange fork is designed and fabricated through AM laser powder-bed fusion… More >

Sarmad Dheyaa Azeez¹, Muhammad Ilyas^2,*, Saadaldeen Rashid Ahmed^3,4

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074930 - 09 April 2026

Abstract The rapid evolution of 5G-enabled Software Defined Networks (SDNs) has transformed modern communication systems by enabling ultra-low latency, massive connectivity, and high throughput. However, the increased complexity of traffic flows and the rise of sophisticated cyber-attacks such as Distributed Denial of Service (DDoS), Botnets, Fake Base Stations, and Zero-Day exploits have made intrusion detection a critical challenge. Traditional Intrusion Detection System (IDS) approaches often suffer from poor gen-eralization, high false positives, and lack of interpretability, making them unsuitable for dynamic 5G environments. This paper presents a novel Graph Neural Network (GNN) with Multi-Head Attention (MHA)… More >

Sudhir Kumar Pandey¹, Jian-Xun Mi^1,*, Zahid Ullah², Mona Jamjoom³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074811 - 09 April 2026

Abstract Adversarial examples in object detection frequently fail to transfer between different models because attacks overfit to the source model’s architecture and feature space. We propose AugTrans, a framework that addresses this limitation through input-space regularization. Our key innovation is a multi-stage augmentation pipeline that incorporates object-level semantic awareness into transformation design. The pipeline comprises three novel components: dynamic object-centric rotation with adaptive scheduling, multi-box aware resizing based on ground-truth annotations, and composite noise injection. These transformations are integrated within the Expectation over Transformation (EOT) framework. By optimizing perturbations to remain effective across semantically meaningful transformations, our… More >