Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (1,804)
  • Open Access

    ARTICLE

    Layered Feature Engineering for E-Commerce Purchase Prediction: A Hierarchical Evaluation on Taobao User Behavior Datasets

    Liqiu Suo1, Lin Xia1, Yoona Chung1, Eunchan Kim1,2,*

    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 >

  • Open Access

    ARTICLE

    Multi-Algorithm Machine Learning Framework for Predicting Crystal Structures of Lithium Manganese Silicate Cathodes Using DFT Data

    Muhammad Ishtiaq1, Yeon-Ju Lee2, Annabathini Geetha Bhavani3, Sung-Gyu Kang1,*, Nagireddy Gari Subba Reddy2,*

    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 >

  • Open Access

    ARTICLE

    Machine Learning-Driven Prediction of the Glass Transition Temperature of Styrene-Butadiene Rubber

    Zhanglei Wang1,2, Shuo Yan1,2, Jingyu Gao1,2, Haoyu Wu1,2, Baili Wang1,2, Xiuying Zhao1,2,*, Shikai Hu1,2,*

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

    Abstract The glass transition temperature (Tg) of styrene-butadiene rubber (SBR) is a key parameter determining its low-temperature flexibility and processing performance. Accurate prediction of Tg 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 Tg of SBR and… More >

  • Open Access

    ARTICLE

    An Integrated Attention-BiLSTM Approach for Probabilistic Remaining Useful Life Prediction

    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 >

  • Open Access

    ARTICLE

    A Chinese Abbreviation Prediction Framework Based on Chain-of-Thought Prompting and Semantic Preservation Dynamic Adjustment

    Jingru Lv1, Jianpeng Hu1,*, Jin Zhao2, Yonghao Luo1

    CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073212 - 10 February 2026

    Abstract Chinese abbreviations improve communicative efficiency by extracting key components from longer expressions. They are widely used in both daily communication and professional domains. However, existing abbreviation generation methods still face two major challenges. First, sequence-labeling-based approaches often neglect contextual meaning by making binary decisions at the character level, leading to abbreviations that fail to capture semantic completeness. Second, generation-based methods rely heavily on a single decoding process, which frequently produces correct abbreviations but ranks them lower due to inadequate semantic evaluation. To address these limitations, we propose a novel two-stage framework with Generation–Iterative Optimization for More >

  • Open Access

    ARTICLE

    Engine Failure Prediction on Large-Scale CMAPSS Data Using Hybrid Feature Selection and Imbalance-Aware Learning

    Ahmad Junaid1, Abid Iqbal2,*, Abuzar Khan1, Ghassan Husnain1,*, Abdul-Rahim Ahmad3, Mohammed Al-Naeem4

    CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073189 - 10 February 2026

    Abstract Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness. This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail. It uses the NASA CMAPSS dataset, which has over 200,000 engine cycles from 260 engines. The process begins with systematic preprocessing, which includes imputation, outlier removal, scaling, and labelling of the remaining useful life. Dimensionality is reduced using a hybrid selection method that combines variance filtering, recursive elimination, and gradient-boosted importance scores, yielding a stable set of… More >

  • Open Access

    ARTICLE

    Spatio-Temporal Monitoring and Assessment of Groundwater Quality for Domestic and Agricultural Use in Kurukshetra District, Haryana, India

    Aakash Deep*, Sushil Kumar, Bhagwan Singh Chaudhary

    Revue Internationale de Géomatique, Vol.35, pp. 79-100, 2026, DOI:10.32604/rig.2026.074969 - 05 February 2026

    Abstract The assessment of groundwater quality is crucial for ensuring its safe and sustainable use for domestic and agricultural purposes. The Kurukshetra district in the Indian state of Haryana relies heavily on groundwater to meet household and agricultural needs. Sustainable groundwater management must be assessed in terms of suitability for domestic and agricultural needs in a region. The current study analyzed pre-monsoon geochemical data from groundwater samples in the study area for 1991, 2000, 2010, and 2020. A Geographic Information System (GIS) was used to create spatial distribution maps for hydrogen ion concentration, total hardness, total… More >

  • Open Access

    ARTICLE

    Prediction of Root Zone Temperature Dynamics at Effective Depth on Lettuce Production in Greenhouse Using Sensitivity and Feature Importance Analysis with XGBoost

    Hasan Kaan Kucukerdem*

    Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.074188 - 30 January 2026

    Abstract Root-zone temperature (RZT) strongly affects plant growth, nutrient uptake and tolerance to environmental stress, making its regulation a key challenge in greenhouse cultivation in cold climates. This study aimed to assess the potential of passive techniques, namely black polyethylene mulch and row covers, for modifying RZT dynamics in lettuce (Lactuca sativa L.) production and to evaluate the predictive performance of the eXtreme Gradient Boosting (XGBoost) algorithm. Experiments were conducted in Iğdır, Türkiye, over a 61-day period, with soil temperature continuously monitored at depths of 1–30 cm under mulched and non-mulched conditions, alongside measurements of greenhouse air… More >

  • Open Access

    ARTICLE

    The Relationship between Parental Marital Conflict and Adolescent Short Video Dependence: A Chain Mediation Model

    Lei Yang, Yang Liu*

    International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.073529 - 28 January 2026

    Abstract Background: This study aims to investigate the underlying mechanisms between parental marital conflict and adolescent short video dependence by constructing a chain mediation model, focusing on the mediating roles of experiential avoidance and emotional disturbance (anxiety, depression, and stress). Methods: Conducted in January 2025, the research recruited 4125 adolescents from multiple Chinese provinces through convenience sampling; after data cleaning, 3957 valid participants (1959 males, 1998 females) were included. Using a cross-sectional design, measures included parental marital conflict, experiential avoidance, anxiety, depression, stress, and short video dependence. Results: Pearson correlation analysis revealed significant positive correlations among all variables.… More >

  • Open Access

    ARTICLE

    The Connection Paradox: How Social Support Facilitates Short Video Addiction and Solitary Well-Being among Older Adults in China

    Yue Cui1, Ziqing Yang2, Hao Gao1,*

    International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.072986 - 28 January 2026

    Abstract Background: In the Chinese context, the impact of short video applications on the psychological well-being of older adults is contested. While often examined through a pathological lens of addiction, this perspective may overlook paradoxical, context-dependent positive outcomes. Therefore, the main objective of this study is to challenge the traditional Compensatory Internet Use Theory by proposing and testing a chained mediation model that explores a paradoxical pathway from social support to life satisfaction via problematic social media use. Methods: Data were collected between July and August 2025 via the Credamo online survey platform, yielding 384 valid responses… More >

Displaying 1-10 on page 1 of 1804. Per Page