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  • Open Access

    ARTICLE

    An Enhanced VIKOR and Its Revisit for the Manufacturing Process Application

    Ting-Yu Lin1, Kuo-Chen Hung2,*, Josef Jablonsky3, Kuo-Ping Lin1

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1901-1927, 2025, DOI:10.32604/cmc.2025.063543 - 16 April 2025

    Abstract VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) has been developed and applied for over twenty-five years, gaining recognition as a prominent multi-criteria decision-making (MCDM) method. Over this period, numerous studies have explored its applications, conducted comparative analyses, integrated it with other methods, and proposed various modifications to enhance its performance. This paper aims to delve into the fundamental principles and objectives of VIKOR, which aim to maximize group utility and minimize individual regret simultaneously. However, this study identifies a significant limitation in the VIKOR methodology: its process amplifies the weight of individual regret, and the calculated… More >

  • Open Access

    ARTICLE

    Optimizing Forecast Accuracy in Cryptocurrency Markets: Evaluating Feature Selection Techniques for Technical Indicators

    Ahmed El Youssefi1, Abdelaaziz Hessane1,2, Imad Zeroual1, Yousef Farhaoui1,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3411-3433, 2025, DOI:10.32604/cmc.2025.063218 - 16 April 2025

    Abstract This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators. In this work, over 130 technical indicators—covering momentum, volatility, volume, and trend-related technical indicators—are subjected to three distinct feature selection approaches. Specifically, mutual information (MI), recursive feature elimination (RFE), and random forest importance (RFI). By extracting an optimal set of 20 predictors, the proposed framework aims to mitigate redundancy and overfitting while enhancing interpretability. These feature subsets are integrated into support vector regression (SVR), Huber regressors, and k-nearest neighbors (KNN) models to forecast the… More >

  • Open Access

    ARTICLE

    Application of Multi-Criteria Decision and Simulation Approaches to Selection of Additive Manufacturing Technology for Aerospace Application

    Ilesanmi Afolabi Daniyan1,*, Rumbidzai Muvunzi2, Festus Fameso3, Julius Ndambuki3, Williams Kupolati3, Jacques Snyman3

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1623-1648, 2025, DOI:10.32604/cmc.2025.062092 - 16 April 2025

    Abstract This study evaluates the Fuzzy Analytical Hierarchy Process (FAHP) as a multi-criteria decision (MCD) support tool for selecting appropriate additive manufacturing (AM) techniques that align with cleaner production and environmental sustainability. The FAHP model was validated using an example of the production of aircraft components (specifically fuselage) employing AM technologies such as Wire Arc Additive Manufacturing (WAAM), laser powder bed fusion (L-PBF), Binder Jetting (BJ), Selective Laser Sintering (SLS), and Laser Metal Deposition (LMD). The selection criteria prioritized eco-friendly manufacturing considerations, including the quality and properties of the final product (e.g., surface finish, high strength,… More >

  • Open Access

    ARTICLE

    Ordered Clustering-Based Semantic Music Recommender System Using Deep Learning Selection

    Weitao Ha1, Sheng Gang2, Yahya D. Navaei3, Abubakar S. Gezawa4, Yaser A. Nanehkaran2,5,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3025-3057, 2025, DOI:10.32604/cmc.2025.061343 - 16 April 2025

    Abstract Music recommendation systems are essential due to the vast amount of music available on streaming platforms, which can overwhelm users trying to find new tracks that match their preferences. These systems analyze users’ emotional responses, listening habits, and personal preferences to provide personalized suggestions. A significant challenge they face is the “cold start” problem, where new users have no past interactions to guide recommendations. To improve user experience, these systems aim to effectively recommend music even to such users by considering their listening behavior and music popularity. This paper introduces a novel music recommendation system… More >

  • Open Access

    ARTICLE

    Collaborative Decomposition Multi-Objective Improved Elephant Clan Optimization Based on Penalty-Based and Normal Boundary Intersection

    Mengjiao Wei1,*, Wenyu Liu2

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2505-2523, 2025, DOI:10.32604/cmc.2025.060887 - 16 April 2025

    Abstract In recent years, decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios. In these algorithms, the reference vectors of the Penalty-Based boundary intersection (PBI) are distributed parallelly while those based on the normal boundary intersection (NBI) are distributed radially in a conical shape in the objective space. To improve the problem-solving effectiveness of multi-objective optimization algorithms in engineering applications, this paper addresses the improvement of the Collaborative Decomposition (CoD) method, a multi-objective decomposition technique that integrates PBI and NBI, and combines it with the Elephant Clan Optimization Algorithm, introducing the… More >

  • Open Access

    ARTICLE

    Heart Disease Prediction Model Using Feature Selection and Ensemble Deep Learning with Optimized Weight

    Iman S. Al-Mahdi1, Saad M. Darwish1,*, Magda M. Madbouly2

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 875-909, 2025, DOI:10.32604/cmes.2025.061623 - 11 April 2025

    Abstract Heart disease prediction is a critical issue in healthcare, where accurate early diagnosis can save lives and reduce healthcare costs. The problem is inherently complex due to the high dimensionality of medical data, irrelevant or redundant features, and the variability in risk factors such as age, lifestyle, and medical history. These challenges often lead to inefficient and less accurate models. Traditional prediction methodologies face limitations in effectively handling large feature sets and optimizing classification performance, which can result in overfitting poor generalization, and high computational cost. This work proposes a novel classification model for heart… More >

  • Open Access

    ARTICLE

    Selection and Parameter Optimization of Constraint Systems for Girder-End Longitudinal Displacement Control in Three-Tower Suspension Bridges

    Zihang Wang1, Ying Peng1, Xiong Lan2, Xiaoyu Bai3, Chao Deng1, Yuan Ren1,*

    Structural Durability & Health Monitoring, Vol.19, No.3, pp. 643-664, 2025, DOI:10.32604/sdhm.2025.060302 - 03 April 2025

    Abstract To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge, this study takes the Cangrong Xunjiang Bridge as an engineering case for finite element analysis. This bridge employs an unprecedented tower-girder constraint method, with all vertical supports placed at the transition piers at both ends. This paper aims to study the characteristics of longitudinal displacement control at the girder ends under this novel structure, relying on finite element (FE) analysis. Initially, based on the Weigh In Motion (WIM) data, a random vehicle load… More >

  • Open Access

    ARTICLE

    A Method for Fast Feature Selection Utilizing Cross-Similarity within the Context of Fuzzy Relations

    Wenchang Yu1, Xiaoqin Ma1,2, Zheqing Zhang1, Qinli Zhang1,2,*

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1195-1218, 2025, DOI:10.32604/cmc.2025.060833 - 26 March 2025

    Abstract Feature selection methods rooted in rough sets confront two notable limitations: their high computational complexity and sensitivity to noise, rendering them impractical for managing large-scale and noisy datasets. The primary issue stems from these methods’ undue reliance on all samples. To overcome these challenges, we introduce the concept of cross-similarity grounded in a robust fuzzy relation and design a rapid and robust feature selection algorithm. Firstly, we construct a robust fuzzy relation by introducing a truncation parameter. Then, based on this fuzzy relation, we propose the concept of cross-similarity, which emphasizes the sample-to-sample similarity relations… More >

  • Open Access

    ARTICLE

    Machine Learning Stroke Prediction in Smart Healthcare: Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

    Abdul Ahad1,2, Ira Puspitasari1,3,*, Jiangbin Zheng2, Shamsher Ullah4, Farhan Ullah5, Sheikh Tahir Bakhsh6, Ivan Miguel Pires7,*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5115-5134, 2025, DOI:10.32604/cmc.2025.062605 - 06 March 2025

    Abstract This research explores the use of Fuzzy K-Nearest Neighbor (F-KNN) and Artificial Neural Networks (ANN) for predicting heart stroke incidents, focusing on the impact of feature selection methods, specifically Chi-Square and Best First Search (BFS). The study demonstrates that BFS significantly enhances the performance of both classifiers. With BFS preprocessing, the ANN model achieved an impressive accuracy of 97.5%, precision and recall of 97.5%, and an Receiver Operating Characteristics (ROC) area of 97.9%, outperforming the Chi-Square-based ANN, which recorded an accuracy of 91.4%. Similarly, the F-KNN model with BFS achieved an accuracy of 96.3%, precision More >

  • Open Access

    REVIEW

    Optimal Location of Renewable Energy Generators in Transmission and Distribution System of Deregulated Power Sector: A Review

    Digambar Singh1, Najat Elgeberi2, Mohammad Aljaidi3,*, Ramesh Kumar4,5, Rabia Emhamed Al Mamlook6, Manish Kumar Singla4,7,8,*

    Energy Engineering, Vol.122, No.3, pp. 823-859, 2025, DOI:10.32604/ee.2025.059309 - 07 March 2025

    Abstract The literature on multi-attribute optimization for renewable energy source (RES) placement in deregulated power markets is extensive and diverse in methodology. This study focuses on the most relevant publications directly addressing the research problem at hand. Similarly, while the body of work on optimal location and sizing of renewable energy generators (REGs) in balanced distribution systems is substantial, only the most pertinent sources are cited, aligning closely with the study’s objective function. A comprehensive literature review reveals several key research areas: RES integration, RES-related optimization techniques, strategic placement of wind and solar generation, and RES… More >

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