Liqiu Suo¹, Lin Xia¹, Yoona Chung¹, Eunchan Kim^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.076329 - 10 February 2026

Abstract Accurate purchase prediction in e-commerce critically depends on the quality of behavioral features. This paper proposes a layered and interpretable feature engineering framework that organizes user signals into three layers: Basic, Conversion & Stability (efficiency and volatility across actions), and Advanced Interactions & Activity (cross-behavior synergies and intensity). Using real Taobao (Alibaba’s primary e-commerce platform) logs (57,976 records for 10,203 users; 25 November–03 December 2017), we conducted a hierarchical, layer-wise evaluation that holds data splits and hyperparameters fixed while varying only the feature set to quantify each layer’s marginal contribution. Across logistic regression (LR), decision… More >

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 >

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 >

Shereen S. Jumaa¹, Mohsin H. Challoob², Amjad J. Humaidi^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074752 - 10 February 2026

Abstract Military image encryption plays a vital role in ensuring the secure transmission of sensitive visual information from unauthorized access. This paper proposes a new Tri-independent keying method for encrypting military images. The proposed encryption method is based on multilevel security stages of pixel-level scrambling, bit-level manipulation, and block-level shuffling operations. For having a vast key space, the input password is hashed by the Secure Hash Algorithm 256-bit (SHA-256) for generating independently deterministic keys used in the multilevel stages. A piecewise pixel-level scrambling function is introduced to perform a dual flipping process controlled with an adaptive… More >

Bassam Al-Shargabi^1,*, Mohammed Abbas Fadhil Al-Husainy², Abdelrahman Abuarqoub¹, Omar Albahbouh Aldabbas³

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074550 - 10 February 2026

Abstract The advent of 5G technology has significantly enhanced the transmission of images over networks, expanding data accessibility and exposure across various applications in digital technology and social media. Consequently, the protection of sensitive data has become increasingly critical. Regardless of the complexity of the encryption algorithm used, a robust and highly secure encryption key is essential, with randomness and key space being crucial factors. This paper proposes a new Robust Deoxyribonucleic Acid (RDNA) nucleotide-based encryption method. The RDNA encryption method leverages the unique properties of DNA nucleotides, including their inherent randomness and extensive key space,… 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 >

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 >

Hui Yu¹, Jun Liu^1,*, Mingwei Lin^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074191 - 10 February 2026

Abstract Small object detection has been a focus of attention since the emergence of deep learning-based object detection. Although classical object detection frameworks have made significant contributions to the development of object detection, there are still many issues to be resolved in detecting small objects due to the inherent complexity and diversity of real-world visual scenes. In particular, the YOLO (You Only Look Once) series of detection models, renowned for their real-time performance, have undergone numerous adaptations aimed at improving the detection of small targets. In this survey, we summarize the state-of-the-art YOLO-based small object detection 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 >

Bo Zhu^#, Enzhi Dong^#, Zhonghua Cheng^*, Kexin Jiang, Chiming Guo, Shuai Yue

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074009 - 10 February 2026

Abstract Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies, effectively reducing both the frequency of failures and associated costs. As a core component of PHM, RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making. However, deep learning models often falter when processing raw, noisy temporal signals, fail to quantify prediction uncertainty, and face challenges in effectively capturing the nonlinear dynamics of equipment degradation. To address these issues, this study proposes a novel deep learning framework. First, a new bidirectional long short-term memory network integrated with More >