Dumitru Baleanu^1,2, Carla M. A. Pinto³, Sunil Kumar^4,*

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 >

Karim Gasmi¹, Olfa Hrizi^1,*, Najib Ben Aoun^2,3, 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 >

Pravinkumar Bhujangrao Landge¹, Yashpal Singh¹, Hitesh Mohapatra², Seyyed Ahmad Edalatpanah^3,*

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 >

Khizer Mehmood¹, Naveed Ishtiaq Chaudhary^2,*, 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 >

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 >

Yujie Li¹, Zhongju Feng^1,*, 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 >

Yi-Chen Lee¹, Yann-Horng Lin², Wen-Jer Chang^2,*, Muhammad Shamrooz Aslam^3,*, 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 >

Sana Shahab¹, Ibtehal Alazman², Ashit Kumar Dutta³, Mohd Anjum⁴, Vladimir Simic^5,6,7,*, 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 >

Sahbi Boubaker^1,*, Adel Mellit^2,3,*, Nejib Ghazouani⁴, Walid Meskine⁵, Mohamed Benghanem⁶, Habib Kraiem^7,8

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 >

Irbek Morgoev¹, Roman Klyuev^2,*, 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 >