Muhammad Ishtiaq¹, Yeon-Ju Lee², Annabathini Geetha Bhavani³, Sung-Gyu Kang^1,*, Nagireddy Gari Subba Reddy^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.075957 - 10 February 2026

Abstract Lithium manganese silicate (Li-Mn-Si-O) cathodes are key components of lithium-ion batteries, and their physical and mechanical properties are strongly influenced by their underlying crystal structures. In this study, a range of machine learning (ML) algorithms were developed and compared to predict the crystal systems of Li-Mn-Si-O cathode materials using density functional theory (DFT) data obtained from the Materials Project database. The dataset comprised 211 compositions characterized by key descriptors, including formation energy, energy above the hull, bandgap, atomic site number, density, and unit cell volume. These features were utilized to classify the materials into monoclinic… More >

Zhanglei Wang^1,2, Shuo Yan^1,2, Jingyu Gao^1,2, Haoyu Wu^1,2, Baili Wang^1,2, Xiuying Zhao^1,2,*, Shikai Hu^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075667 - 10 February 2026

Abstract The glass transition temperature (T_g) of styrene-butadiene rubber (SBR) is a key parameter determining its low-temperature flexibility and processing performance. Accurate prediction of T_g is crucial for material design and application optimisation. Addressing the limitations of traditional experimental measurements and theoretical models in terms of efficiency, cost, and accuracy, this study proposes a machine learning prediction framework that integrates multi-model ensemble and Bayesian optimization by constructing a multi-component feature dataset and algorithm optimization strategy. Based on the constructed high-quality dataset containing 96 SBR samples, nine machine learning models were employed to predict the T_g of SBR and… More >

Viorel Mînzu^1,*, Iulian Arama²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.075619 - 10 February 2026

Abstract This work addresses optimality aspects related to composite laminates having layers with different orientations. Regression Neural Networks can model the mechanical behavior of these laminates, specifically the stress-strain relationship. If this model has strong generalization ability, it can be coupled with a metaheuristic algorithm–the PSO algorithm used in this article–to address an optimization problem (OP) related to the orientations of composite laminates. To solve OPs, this paper proposes an optimization framework (OFW) that connects the two components, the optimal solution search mechanism and the RNN model. The OFW has two modules: the search mechanism (Adaptive… More >

Zhen Wu^1,2, Hao Liu³, Linlin Zhang⁴, Zehui Zhang^5,*, Jie Wu¹, Haibin He¹, Bin Zhou⁶

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075156 - 10 February 2026

Abstract Recently, Internet of Things (IoT) has been increasingly integrated into the automotive sector, enabling the development of diverse applications such as the Internet of Vehicles (IoV) and intelligent connected vehicles. Leveraging IoV technologies, operational data from core vehicle components can be collected and analyzed to construct fault diagnosis models, thereby enhancing vehicle safety. However, automakers often struggle to acquire sufficient fault data to support effective model training. To address this challenge, a robust and efficient federated learning method (REFL) is constructed for machinery fault diagnosis in collaborative IoV, which can organize multiple companies to collaboratively More >

Alaa Tolah^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074997 - 10 February 2026

Abstract The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors. This creates a significant gap between theoretical knowledge and the practical defensive capabilities needed in the field. To address this, we propose TeachSecure-CTI, a novel framework for adaptive cybersecurity curriculum generation that integrates real-time Cyber Threat Intelligence (CTI) with AI-driven personalization. Our framework employs a layered architecture featuring a CTI ingestion and clustering module, natural language processing for semantic concept extraction, and a reinforcement learning agent for adaptive content sequencing. By… More >

Nien-Tsu Hu^1,*, Hsiang-Tung Kao¹, Chin-Sheng Chen¹, Shih-Hao Chang²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074993 - 10 February 2026

Abstract Trajectory tracking for nonlinear robotic systems remains a fundamental yet challenging problem in control engineering, particularly when both precision and efficiency must be ensured. Conventional control methods are often effective for stabilization but may not directly optimize long-term performance. To address this limitation, this study develops an integrated framework that combines optimal control principles with reinforcement learning for a single-link robotic manipulator. The proposed scheme adopts an actor–critic structure, where the critic network approximates the value function associated with the Hamilton–Jacobi–Bellman equation, and the actor network generates near-optimal control signals in real time. This dual… More >

Enzo Hoummady^*, Fehmi Jaafar

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074897 - 10 February 2026

Abstract With the proliferation of Internet of Things (IoT) devices, securing these interconnected systems against cyberattacks has become a critical challenge. Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic. This paper presents a comparative benchmark of classic machine learning (ML) and state-of-the-art deep learning (DL) algorithms for IoT intrusion detection. Our methodology employs a two-phased approach: a preliminary pilot study using a custom-generated dataset to establish baselines, followed by a comprehensive evaluation on the large-scale CICIoTDataset2023. We benchmarked algorithms including Random Forest, XGBoost, CNN, and Stacked LSTM. The… More >

Abdallah Ghourabi^1,*, Hassen Chouaib²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.074505 - 10 February 2026

Abstract In recent years, ransomware attacks have become one of the most common and destructive types of cyberattacks. Their impact is significant on the operations, finances and reputation of affected companies. Despite the efforts of researchers and security experts to protect information systems from these attacks, the threat persists and the proposed solutions are not able to significantly stop the spread of ransomware attacks. The latest remarkable achievements of large language models (LLMs) in NLP tasks have caught the attention of cybersecurity researchers to integrate these models into security threat detection. These models offer high embedding… More >

Junnian Wang¹, Xiaoxia Wang¹, Zexin Luo¹, Qixiang Ouyang¹, Chao Zhou¹, Huanyu Wang^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074473 - 10 February 2026

Abstract Internet of Things (IoTs) devices are bringing about a revolutionary change our society by enabling connectivity regardless of time and location. However, The extensive deployment of these devices also makes them attractive victims for the malicious actions of adversaries. Within the spectrum of existing threats, Side-Channel Attacks (SCAs) have established themselves as an effective way to compromise cryptographic implementations. These attacks exploit unintended, unintended physical leakage that occurs during the cryptographic execution of devices, bypassing the theoretical strength of the crypto design. In recent times, the advancement of deep learning has provided SCAs with a… More >

Sara Tehsin¹, Inzamam Mashood Nasir^1,*, Wiem Abdelbaki², Fadwa Alrowais³, Khalid A. Alattas⁴, Sultan Almutairi⁵, Radwa Marzouk⁶

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074416 - 10 February 2026

Abstract Objective: Deep learning is employed increasingly in Gastroenterology (GI) endoscopy computer-aided diagnostics for polyp segmentation and multi-class disease detection. In the real world, implementation requires high accuracy, therapeutically relevant explanations, strong calibration, domain generalization, and efficiency. Current Convolutional Neural Network (CNN) and transformer models compromise border precision and global context, generate attention maps that fail to align with expert reasoning, deteriorate during cross-center changes, and exhibit inadequate calibration, hence diminishing clinical trust. Methods: HMA-DER is a hierarchical multi-attention architecture that uses dilation-enhanced residual blocks and an explainability-aware Cognitive Alignment Score (CAS) regularizer to directly align… More >