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  • Open Access

    EDITORIAL

    Introduction to the Special Issue on Mathematical Aspects of Computational Biology and Bioinformatics-II

    Dumitru Baleanu1,2, Carla M. A. Pinto3, Sunil Kumar4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1297-1299, 2025, DOI:10.32604/cmes.2025.067010 - 30 May 2025

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Enhanced Multimodal Physiological Signal Analysis for Pain Assessment Using Optimized Ensemble Deep Learning

    Karim Gasmi1, Olfa Hrizi1,*, Najib Ben Aoun2,3, Ibrahim Alrashdi1, Ali Alqazzaz4, Omer Hamid5, Mohamed O. Altaieb1, Alameen E. M. Abdalrahman1, Lassaad Ben Ammar6, Manel Mrabet6, Omrane Necibi1

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2459-2489, 2025, DOI:10.32604/cmes.2025.065817 - 30 May 2025

    Abstract The potential applications of multimodal physiological signals in healthcare, pain monitoring, and clinical decision support systems have garnered significant attention in biomedical research. Subjective self-reporting is the foundation of conventional pain assessment methods, which may be unreliable. Deep learning is a promising alternative to resolve this limitation through automated pain classification. This paper proposes an ensemble deep-learning framework for pain assessment. The framework makes use of features collected from electromyography (EMG), skin conductance level (SCL), and electrocardiography (ECG) signals. We integrate Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Bidirectional Gated Recurrent Units (BiGRU),… More >

  • Open Access

    ARTICLE

    Efficient Resource Management in IoT Network through ACOGA Algorithm

    Pravinkumar Bhujangrao Landge1, Yashpal Singh1, Hitesh Mohapatra2, Seyyed Ahmad Edalatpanah3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1661-1688, 2025, DOI:10.32604/cmes.2025.065599 - 30 May 2025

    Abstract Internet of things networks often suffer from early node failures and short lifespan due to energy limits. Traditional routing methods are not enough. This work proposes a new hybrid algorithm called ACOGA. It combines Ant Colony Optimization (ACO) and the Greedy Algorithm (GA). ACO finds smart paths while Greedy makes quick decisions. This improves energy use and performance. ACOGA outperforms Hybrid Energy-Efficient (HEE) and Adaptive Lossless Data Compression (ALDC) algorithms. After 500 rounds, only 5% of ACOGA’s nodes are dead, compared to 15% for HEE and 20% for ALDC. The network using ACOGA runs for More >

  • Open Access

    ARTICLE

    Design of Chaos Induced Aquila Optimizer for Parameter Estimation of Electro-Hydraulic Control System

    Khizer Mehmood1, Naveed Ishtiaq Chaudhary2,*, Zeshan Aslam Khan3, Khalid Mehmood Cheema4, Muhammad Asif Zahoor Raja2, Sultan S. Alshamrani5, Kaled M. Alshmrany6

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1809-1841, 2025, DOI:10.32604/cmes.2025.064900 - 30 May 2025

    Abstract Aquila Optimizer (AO) is a recently proposed population-based optimization technique inspired by Aquila’s behavior in catching prey. AO is applied in various applications and its numerous variants were proposed in the literature. However, chaos theory has not been extensively investigated in AO. Moreover, it is still not applied in the parameter estimation of electro-hydraulic systems. In this work, ten well-defined chaotic maps were integrated into a narrowed exploitation of AO for the development of a robust chaotic optimization technique. An extensive investigation of twenty-three mathematical benchmarks and ten IEEE Congress on Evolutionary Computation (CEC) functions… More >

  • Open Access

    ARTICLE

    A Shuffled Frog-Leaping Algorithm with Competition for Parallel Batch Processing Machines Scheduling in Fabric Dyeing Process

    Mingbo Li, Deming Lei*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1789-1808, 2025, DOI:10.32604/cmes.2025.064886 - 30 May 2025

    Abstract As a complicated optimization problem, parallel batch processing machines scheduling problem (PBPMSP) exists in many real-life manufacturing industries such as textiles and semiconductors. Machine eligibility means that at least one machine is not eligible for at least one job. PBPMSP and scheduling problems with machine eligibility are frequently considered; however, PBPMSP with machine eligibility is seldom explored. This study investigates PBPMSP with machine eligibility in fabric dyeing and presents a novel shuffled frog-leaping algorithm with competition (CSFLA) to minimize makespan. In CSFLA, the initial population is produced in a heuristic and random way, and the More >

  • Open Access

    ARTICLE

    Dynamic Response of Bridge Pile Foundations under Pile-Soil-Fault Interaction in Seismic Areas

    Yujie Li1, Zhongju Feng1,*, Fuchun Wang1, Jiang Guan2, Xiaoqian Ma3

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1549-1573, 2025, DOI:10.32604/cmes.2025.064785 - 30 May 2025

    Abstract To study the dynamic response rules of pile foundations of mega-bridges over faults in strong seismic areas, a finite element model of the pile foundation-soil-fault interaction of the Haiwen Bridge is established. The 0.2–0.6 g peak acceleration of the 5010 seismic waves is input to study the effect of the seismic wave of different intensities and the distance changes between the fault and the pile foundation on the dynamic response of the pile body. The results show that the soil layer covering the bedrock amplifies the peak pile acceleration, and the amplifying effect decreases with… More >

  • Open Access

    ARTICLE

    Optimal Fuzzy Tracking Synthesis for Nonlinear Discrete-Time Descriptor Systems with T-S Fuzzy Modeling Approach

    Yi-Chen Lee1, Yann-Horng Lin2, Wen-Jer Chang2,*, Muhammad Shamrooz Aslam3,*, Zi-Yao Lin2

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1433-1461, 2025, DOI:10.32604/cmes.2025.064717 - 30 May 2025

    Abstract An optimal fuzzy tracking synthesis for nonlinear discrete-time descriptor systems is discussed through the Parallel Distributed Compensation (PDC) approach and the Proportional-Difference (P-D) feedback framework. Based on the Takagi-Sugeno Fuzzy Descriptor Model (T-SFDM), a nonlinear discrete-time descriptor system is represented as several linear fuzzy subsystems, which facilitates the linear P-D feedback technique and streamlines the fuzzy controller design process. Leveraging the P-D feedback fuzzy controller, the closed-loop T-SFDM can be transformed into a standard system that guarantees non-impulsiveness and causality for the nonlinear discrete-time descriptor system. In view of the disturbance problems, a passive performance… More >

  • Open Access

    ARTICLE

    Promoting Tailored Hotel Recommendations Based on Traveller Preferences: A Circular Intuitionistic Fuzzy Decision Support Model

    Sana Shahab1, Ibtehal Alazman2, Ashit Kumar Dutta3, Mohd Anjum4, Vladimir Simic5,6,7,*, Željko Stević8, Nouf Abdulrahman Alqahtani2

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2155-2183, 2025, DOI:10.32604/cmes.2025.064553 - 30 May 2025

    Abstract With the increasing complexity of hotel selection, traditional decision-making models often struggle to account for uncertainty and interrelated criteria. Multi-criteria decision-making (MCDM) techniques, particularly those based on fuzzy logic, provide a robust framework for handling such challenges. This paper presents a novel approach to MCDM within the framework of Circular Intuitionistic Fuzzy Sets (C-IFS) by combining three distinct methodologies: Weighted Aggregated Sum Product Assessment (WASPAS), an Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN), and the CRITIC method (Criteria Importance Through Intercriteria Correlation). To address the dynamic nature of traveler preferences in hotel selection,… More >

  • Open Access

    ARTICLE

    Demand Forecasting of a Microgrid-Powered Electric Vehicle Charging Station Enabled by Emerging Technologies and Deep Recurrent Neural Networks

    Sahbi Boubaker1,*, Adel Mellit2,3,*, Nejib Ghazouani4, Walid Meskine5, Mohamed Benghanem6, Habib Kraiem7,8

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2237-2259, 2025, DOI:10.32604/cmes.2025.064530 - 30 May 2025

    Abstract Electric vehicles (EVs) are gradually being deployed in the transportation sector. Although they have a high impact on reducing greenhouse gas emissions, their penetration is challenged by their random energy demand and difficult scheduling of their optimal charging. To cope with these problems, this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand forecasting. The present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’ charging scheduling task. By using predictive algorithms for solar generation and load demand… More >

  • Open Access

    ARTICLE

    Methodology for Detecting Non-Technical Energy Losses Using an Ensemble of Machine Learning Algorithms

    Irbek Morgoev1, Roman Klyuev2,*, Angelika Morgoeva1

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1381-1399, 2025, DOI:10.32604/cmes.2025.064502 - 30 May 2025

    Abstract Non-technical losses (NTL) of electric power are a serious problem for electric distribution companies. The solution determines the cost, stability, reliability, and quality of the supplied electricity. The widespread use of advanced metering infrastructure (AMI) and Smart Grid allows all participants in the distribution grid to store and track electricity consumption. During the research, a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings. This model is an ensemble meta-algorithm (stacking) that generalizes the algorithms of random… More >

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