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 >

Isam Bahaa Aldallal¹, Abdullahi Abdu Ibrahim^1,*, Saadaldeen Rashid Ahmed^2,3

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075212 - 10 February 2026

Abstract The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems (IDS) capable of addressing dynamic security threats under constrained resource environments. This paper proposes a hybrid IDS for IoT networks, integrating Support Vector Machine (SVM) and Genetic Algorithm (GA) for feature selection and parameter optimization. The GA reduces the feature set from 41 to 7, achieving a 30% reduction in overhead while maintaining an attack detection rate of 98.79%. Evaluated on the NSL-KDD dataset, the system demonstrates an accuracy of 97.36%, a recall of 98.42%, and an F1-score of 96.67%, with a low false More >

Le Thi Hong Van^1,*, Le Duc Thuan¹, Pham Van Huong¹, Nguyen Hieu Minh²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075027 - 10 February 2026

Abstract Optimizing convolutional neural networks (CNNs) for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy. This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection. Unlike conventional single-objective approaches, the proposed method formulates a global multi-objective fitness function that integrates accuracy, precision, recall, and model size (speed/model complexity penalty) with adjustable weights. This design enables both single-objective and weighted-sum multi-objective optimization, allowing adaptive selection of optimal CNN configurations for diverse deployment… More >

Chengqi Liu¹, Yongtao Li², Weiping Zou^3,*, Deyu Lin^4,5,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074975 - 10 February 2026

Abstract With the large-scale deployment of the Internet of Things (IoT) devices, their weak security mechanisms make them prime targets for malware attacks. Attackers often use Domain Generation Algorithm (DGA) to generate random domain names, hiding the real IP of Command and Control (C&C) servers to build botnets. Due to the randomness and dynamics of DGA, traditional methods struggle to detect them accurately, increasing the difficulty of network defense. This paper proposes a lightweight DGA detection model based on knowledge distillation for resource-constrained IoT environments. Specifically, a teacher model combining CharacterBERT, a bidirectional long short-term memory More >

Naresh Kumar¹, Anuj Kumar^1,*, Abhishek K. Mishra^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074430 - 10 February 2026

Abstract Density functional theory (DFT) calculations were employed to investigate the adsorption behavior of NH₃, AsH₃, PH₃, CO₂, and CH₄ molecules on both pristine and mono-vacancy phosphorene sheets. The pristine phosphorene surface shows weak physisorption with all the gas molecules, inducing only minor changes in its structural and electronic properties. However, the introduction of mono-vacancies significantly enhances the interaction strength with NH₃, PH₃, CO₂, and CH₄. These variations are attributed to substantial charge redistribution and orbital hybridization in the presence of defects. The defective phosphorene sheet also exhibits enhanced adsorption energies, along with favorable sensitivity and recovery characteristics, highlighting its potential 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 >

Yunhao Yu¹, Fuhua Luo¹, Zhenyong Zhang^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074277 - 10 February 2026

Abstract This paper investigates the detection and mitigation of coordinated cyberattacks on Load Frequency Control (LFC) systems integrated with Battery Energy Storage Systems (BESS). As renewable energy sources gain greater penetration, power grids are becoming increasingly vulnerable to cyber threats, potentially leading to frequency instability and widespread disruptions. We model two significant attack vectors: load-altering attacks (LAAs) and false data injection attacks (FDIAs) that corrupt frequency measurements. These are analyzed for their impact on grid frequency stability in both linear and nonlinear LFC models, incorporating generation rate constraints and nonlinear loads. A coordinated attack strategy is… More >

Ye-Chan Park¹, Mohd Asyraf Zulkifley², Bong-Soo Sohn³, Jaesung Lee^4,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074141 - 10 February 2026

Abstract Legal case classification involves the categorization of legal documents into predefined categories, which facilitates legal information retrieval and case management. However, real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains. This leads to biased model performance, in the form of high accuracy for overrepresented categories and underperformance for minority classes. To address this issue, in this study, we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms from the perspective of the legal domain. This approach enhances More >

Feihong Xu¹, Jianbo Wu^1,*, Qing An^1,*, Fei Zhu^1,2, Zhaoyang Han³, Saru Kumari⁴

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073441 - 10 February 2026

Abstract The large-scale deployment of Internet of Things (IoT) technology across various aspects of daily life has significantly propelled the intelligent development of society. Among them, the integration of IoT and named data networks (NDNs) reduces network complexity and provides practical directions for content-oriented network design. However, ensuring data integrity in NDN-IoT applications remains a challenging issue. Very recently, Wang et al. (Entropy, 27(5), 471(2025)) designed a certificateless aggregate signature (CLAS) scheme for NDN-IoT environments. Wang et al. stated that their construction was provably secure under various types of security attacks. Using theoretical analysis methods, in… More >

Nataliya Kazantseva^1,2,*, Nikolai Saharov¹, Denis Davydov^1,2, Nikolai Popov², Maxim Il’inikh¹

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.073078 - 10 February 2026

Abstract Because of the developed surface of the Triply Periodic Minimum Surface (TPMS) structures, polylactide (PLA) products with a TPMS structure are thought to be promising bio soluble implants with the potential for targeted drug delivery. For implants, mechanical properties are key performance characteristics, so understanding the deformation and failure mechanisms is essential for selecting the appropriate implant structure. The deformation and fracture processes in PLA samples with different interior architectures have been studied through computer simulation and experimental research. Two TPMS topologies, the Schwarz Diamond and Gyroid architectures, were used for the sample construction by… More >