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 >

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 >

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 >

Thi-Xuyen Bui^1,2, Yu-Sheng Lu¹, Yu-Sheng Liao¹, Te-Hua Fang^1,3,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074862 - 10 February 2026

Abstract The mechanical performance of exceedingly soft materials such as Ag is significantly influenced by various working conditions. Therefore, this study systematically investigates the effects of crack geometry, substrate crystal orientation, and indenter shape on crack propagation. The mechanical response of Ag is analyzed using the quasi-continuum (QC) method. A pre-crack with a predefined depth and angle was introduced to initiate fracture behavior. The results show that when the pre-crack height is 50 Å, the crack propagates rapidly as the imprint depth increases from 0 to 7 Å, grows steadily up to 15 Å, and then… More >

Lishu Qin^*, Yu Gao, Xinyuan Lu

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074739 - 10 February 2026

Abstract With the rapid development of intelligent navigation technology, efficient and safe path planning for mobile robots has become a core requirement. To address the challenges of complex dynamic environments, this paper proposes an intelligent path planning framework based on grid map modeling. First, an improved Safe and Smooth A* (SSA*) algorithm is employed for global path planning. By incorporating obstacle expansion and corner-point optimization, the proposed SSA* enhances the safety and smoothness of the planned path. Then, a Partitioned Dynamic Window Approach (PDWA) is integrated for local planning, which is triggered when dynamic or sudden… 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 >

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 >

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 >

Hyunki Lim^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074138 - 10 February 2026

Abstract High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements. In particular, in a multi-label environment, higher complexity is required as much as the number of labels. Moreover, an optimization problem that fully considers all dependencies between features and labels is difficult to solve. In this study, we propose a novel regression-based multi-label feature selection method that integrates mutual information to better exploit the underlying data structure. By incorporating mutual information into the regression formulation, the model captures not only linear relationships but also complex non-linear dependencies. The proposed… More >