Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (961)
  • Open Access

    ARTICLE

    Harnessing Machine Learning for Superior Prediction of Uniaxial Compressive Strength in Reinforced Soilcrete

    Ala’a R. Al-Shamasneh1, Faten Khalid Karim2, Arsalan Mahmoodzadeh3,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 281-303, 2025, DOI:10.32604/cmc.2025.065748 - 09 June 2025

    Abstract Soilcrete is a composite material of soil and cement that is highly valued in the construction industry. Accurate measurement of its mechanical properties is essential, but laboratory testing methods are expensive, time-consuming, and include inaccuracies. Machine learning (ML) algorithms provide a more efficient alternative for this purpose, so after assessment with a statistical extraction method, ML algorithms including back-propagation neural network (BPNN), K-nearest neighbor (KNN), radial basis function (RBF), feed-forward neural networks (FFNN), and support vector regression (SVR) for predicting the uniaxial compressive strength (UCS) of soilcrete, were proposed in this study. The developed models… More >

  • Open Access

    ARTICLE

    A Machine Learning-Based Framework for Heart Disease Diagnosis Using a Comprehensive Patient Cohort

    Saadia Tabassum1,2, Fazal Muhammad2, Muhammad Ayaz Khan3, Muhammad Uzair Khan2,4, Dawar Awan4, Neelam Gohar5, Shahid Khan6, Amal Al-Rasheed7,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1253-1278, 2025, DOI:10.32604/cmc.2025.065423 - 09 June 2025

    Abstract Early and accurate detection of Heart Disease (HD) is critical for improving patient outcomes, as HD remains a leading cause of mortality worldwide. Timely and precise prediction can aid in preventive interventions, reducing fatal risks associated with misdiagnosis. Machine learning (ML) models have gained significant attention in healthcare for their ability to assist professionals in diagnosing diseases with high accuracy. This study utilizes 918 instances from publicly available UCI and Kaggle datasets to develop and compare the performance of various ML models, including Adaptive Boosting (AB), Naïve Bayes (NB), Extreme Gradient Boosting (XGB), Bagging, and… More >

  • Open Access

    ARTICLE

    Interpolation-Based Reversible Data Hiding in Encrypted Audio with Scalable Embedding Capacity

    Yuan-Yu Tsai1,*, Alfrindo Lin1, Wen-Ting Jao1, Yi-Hui Chen2,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 681-697, 2025, DOI:10.32604/cmc.2025.064370 - 09 June 2025

    Abstract With the rapid expansion of multimedia data, protecting digital information has become increasingly critical. Reversible data hiding offers an effective solution by allowing sensitive information to be embedded in multimedia files while enabling full recovery of the original data after extraction. Audio, as a vital medium in communication, entertainment, and information sharing, demands the same level of security as images. However, embedding data in encrypted audio poses unique challenges due to the trade-offs between security, data integrity, and embedding capacity. This paper presents a novel interpolation-based reversible data hiding algorithm for encrypted audio that achieves… More >

  • Open Access

    ARTICLE

    BDS-3 Satellite Orbit Prediction Method Based on Ensemble Learning and Neural Networks

    Ruibo Wei1,2, Yao Kong3, Mengzhao Li1,2, Feng Liu1,2,*, Fang Cheng4,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1507-1528, 2025, DOI:10.32604/cmc.2025.063722 - 09 June 2025

    Abstract To address uncertainties in satellite orbit error prediction, this study proposes a novel ensemble learning-based orbit prediction method specifically designed for the BeiDou navigation satellite system (BDS). Building on ephemeris data and perturbation corrections, two new models are proposed: attention-enhanced BPNN (AEBP) and Transformer-ResNet-BiLSTM (TR-BiLSTM). These models effectively capture both local and global dependencies in satellite orbit data. To further enhance prediction accuracy and stability, the outputs of these two models were integrated using the gradient boosting decision tree (GBDT) ensemble learning method, which was optimized through a grid search. The main contribution of this More >

  • Open Access

    ARTICLE

    Diabetes Prediction Using ADASYN-Based Data Augmentation and CNN-BiGRU Deep Learning Model

    Tehreem Fatima1, Kewen Xia1,*, Wenbiao Yang2, Qurat Ul Ain1, Poornima Lankani Perera1

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 811-826, 2025, DOI:10.32604/cmc.2025.063686 - 09 June 2025

    Abstract The rising prevalence of diabetes in modern society underscores the urgent need for precise and efficient diagnostic tools to support early intervention and treatment. However, the inherent limitations of existing datasets, including significant class imbalances and inadequate sample diversity, pose challenges to the accurate prediction and classification of diabetes. Addressing these issues, this study proposes an innovative diabetes prediction framework that integrates a hybrid Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model for classification with Adaptive Synthetic Sampling (ADASYN) for data augmentation. ADASYN was employed to generate synthetic yet representative data samples, effectively mitigating class… More >

  • Open Access

    ARTICLE

    Large Language Model in Healthcare for the Prediction of Genetic Variants from Unstructured Text Medicine Data Using Natural Language Processing

    Noor Ayesha1, Muhammad Mujahid2, Abeer Rashad Mirdad2, Faten S. Alamri3,*, Amjad R. Khan2

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1883-1899, 2025, DOI:10.32604/cmc.2025.063560 - 09 June 2025

    Abstract Large language models (LLMs) and natural language processing (NLP) have significant promise to improve efficiency and refine healthcare decision-making and clinical results. Numerous domains, including healthcare, are rapidly adopting LLMs for the classification of biomedical textual data in medical research. The LLM can derive insights from intricate, extensive, unstructured training data. Variants need to be accurately identified and classified to advance genetic research, provide individualized treatment, and assist physicians in making better choices. However, the sophisticated and perplexing language of medical reports is often beyond the capabilities of the devices we now utilize. Such an… More >

  • Open Access

    ARTICLE

    Predicting Short-Term Wind Power Generation at Musalpetti Wind Farm: Model Development and Analysis

    Namal Rathnayake1, Jeevani Jayasinghe2,3, Rashmi Semasinghe2, Upaka Rathnayake4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2287-2305, 2025, DOI:10.32604/cmes.2025.064464 - 30 May 2025

    Abstract In this study, a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions. Using data on wind speed, air temperature, nacelle position, and actual power, lagged features were generated to capture temporal dependencies. Among 24 evaluated models, the ensemble bagging approach achieved the best performance, with R2 values of 0.89 at 0 min and 0.75 at 60 min. Shapley Additive exPlanations (SHAP) analysis revealed that while wind speed is the primary driver for short-term predictions, air temperature and nacelle position become more More >

  • Open Access

    ARTICLE

    SSA-LSTM-Multi-Head Attention Modelling Approach for Prediction of Coal Dust Maximum Explosion Pressure Based on the Synergistic Effect of Particle Size and Concentration

    Yongli Liu1,2, Weihao Li1,2,*, Haitao Wang1,2,3, Taoren Du4

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2261-2286, 2025, DOI:10.32604/cmes.2025.064179 - 30 May 2025

    Abstract Coal dust explosions are severe safety accidents in coal mine production, posing significant threats to life and property. Predicting the maximum explosion pressure () of coal dust using deep learning models can effectively assess potential risks and provide a scientific basis for preventing coal dust explosions. In this study, a 20-L explosion sphere apparatus was used to test the maximum explosion pressure of coal dust under seven different particle sizes and ten mass concentrations (), resulting in a dataset of 70 experimental groups. Through Spearman correlation analysis and random forest feature selection methods, particle size… More >

  • Open Access

    ARTICLE

    Deep Learning and Heuristic Optimization for Secure and Efficient Energy Management in Smart Communities

    Murad Khan1,*, Mohammed Faisal1, Fahad R. Albogamy2, Muhammad Diyan3

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2027-2052, 2025, DOI:10.32604/cmes.2025.063764 - 30 May 2025

    Abstract The rapid advancements in distributed generation technologies, the widespread adoption of distributed energy resources, and the integration of 5G technology have spurred sharing economy businesses within the electricity sector. Revolutionary technologies such as blockchain, 5G connectivity, and Internet of Things (IoT) devices have facilitated peer-to-peer distribution and real-time response to fluctuations in supply and demand. Nevertheless, sharing electricity within a smart community presents numerous challenges, including intricate design considerations, equitable allocation, and accurate forecasting due to the lack of well-organized temporal parameters. To address these challenges, this proposed system is focused on sharing extra electricity… More >

  • Open Access

    ARTICLE

    Ultrashort-Term Power Prediction of Distributed Photovoltaic Based on Variational Mode Decomposition and Channel Attention Mechanism

    Zhebin Sun1, Wei Wang1, Mingxuan Du2, Tao Liang1, Yang Liu1, Hailong Fan3, Cuiping Li2, Xingxu Zhu2, Junhui Li2,*

    Energy Engineering, Vol.122, No.6, pp. 2155-2175, 2025, DOI:10.32604/ee.2025.062218 - 29 May 2025

    Abstract Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation. This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition (VMD) and Channel Attention Mechanism. First, Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power. Second, the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition (VMD). Finally, the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM… More >

Displaying 1-10 on page 1 of 961. Per Page