Rekha Phadke¹, Abdul Lateef Haroon Phulara Shaik², Dayanidhi Mohapatra³, Doaa Sami Khafaga^4,*, Eman Abdullah Aldakheel⁴, N. Sathyanarayana⁵

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-26, 2026, DOI:10.32604/cmc.2025.067564 - 09 December 2025

Abstract Recently, the Internet of Things (IoT) technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices. Furthermore, the IoT plays a key role in multiple domains, including industrial automation, smart homes, and intelligent transportation systems. However, an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness. To address these issue, this research proposes a Modified Walrus Optimization Algorithm (MWaOA) for effective resource management in smart IoT systems. In the proposed MWaOA, a crowding process… More >

Wanwei Huang^1,*, Huicong Yu¹, Jiawei Ren², Kun Wang³, Yanbu Guo¹, Lifeng Jin⁴

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.068493 - 10 November 2025

Abstract Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity. These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy. This paper proposes an industrial Internet of Things intrusion detection feature selection algorithm based on an improved whale optimization algorithm (GSLDWOA). The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to, such as local optimality, long detection time, and reduced accuracy. First, the initial population’s diversity is increased using the Gaussian Mutation More >

Songsong Zhang¹, Huazhong Jin^1,2,*, Zhiwei Ye^1,2, Jia Yang^1,2, Jixin Zhang^1,2, Dongfang Wu^1,2, Xiao Zheng^1,2, Dingfeng Song¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068044 - 10 November 2025

Abstract Multi-label feature selection (MFS) is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels. However, traditional centralized methods face significant challenges in privacy-sensitive and distributed settings, often neglecting label dependencies and suffering from low computational efficiency. To address these issues, we introduce a novel framework, Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization (DHBCPSO-MSR). Leveraging the federated learning paradigm, Fed-MFSDHBCPSO allows clients to perform local feature selection (FS) using DHBCPSO-MSR. Locally selected feature subsets are encrypted with differential privacy (DP) and transmitted… More >

Sidra Naseem¹, Rashid Jahangir^1,*, Nazik Alturki², Faheem Shehzad³, Muhammad Sami Ullah⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2481-2501, 2025, DOI:10.32604/cmes.2025.072575 - 26 November 2025

Abstract Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies. While deep learning models have significantly advanced medical image analysis, challenges such as imbalanced datasets and redundant features persist. This study proposes a novel framework that customizes two deep learning models, NasNetMobile and ResNet50, by incorporating bottleneck architectures, named as NasNeck and ResNeck, to enhance feature extraction. The feature vectors are fused into a combined vector, which is further optimized using an improved Whale Optimization Algorithm to minimize redundancy and improve discriminative power. The optimized feature vector is then classified using artificial… More >

Minghai Zheng¹, Zhaoxia Cui^1,*, Jiang Liu¹, Jianjun Li²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 431-455, 2025, DOI:10.32604/cmes.2025.071913 - 30 October 2025

Abstract To accurately depict the strong nonlinear relationship between the viscosity of propellant slurry and shear rate, premix time, and temperature, and to improve the prediction accuracy, based on the sample preparation and experimental measurement of a certain type of propellant, viscosity data under multiple working conditions were obtained as the basic data for the research. By comparing typical models such as support vector regression and random forest, it was found that although the traditional BP neural network was superior to the both, its accuracy was still insufficient. Based on this, a BP model co-optimized by… More >

Xinjian Li¹, Yu Zhang^1,2,*, Zewen Wang¹, Zhenyun Song¹

Energy Engineering, Vol.122, No.11, pp. 4603-4619, 2025, DOI:10.32604/ee.2025.065799 - 27 October 2025

Abstract Power prediction has been critical in large-scale wind power grid connections. However, traditional wind power prediction methods have long suffered from problems, for instance low prediction accuracy and poor reliability. For this purpose, a hybrid prediction model (VMD-LSTM-Attention) has been proposed, which integrates the variational modal decomposition (VMD), the long short-term memory (LSTM), and the attention mechanism (Attention), and has been optimized by improved dung beetle optimization algorithm (IDBO). Firstly, the algorithm’s performance has been significantly enhanced through the implementation of three key strategies, namely the elite group strategy of the Logistic-Tent map, the nonlinear… More >

Shekaina Justin^1,*, Wafaa Saleh², Hind Mohammed Albalawi³, J. Shermina⁴

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5469-5487, 2025, DOI:10.32604/cmc.2025.066888 - 23 October 2025

Abstract Solar radiation is the main source of energy on Earth and plays a major role in the hydrological cycles, surface radiation balance, weather and climate changes, and vegetation photosynthesis. Accurate solar radiation prediction is of paramount importance for both climate research and the solar industry. This prediction includes forecasting techniques and advanced modeling to evaluate the amount of solar energy available at a specific location during a given period. Solar energy is the cheapest form of clean energy, and due to the intermittent nature of the energy, accurate forecasting across multiple timeframes is necessary for… More >

Weicong Tan^1,#, Qiwu Wu^2,3,#,*, Lingzhi Jiang¹, Tao Tong², Yunchen Su²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3621-3652, 2025, DOI:10.32604/cmc.2025.068781 - 23 September 2025

Abstract This study introduces a novel algorithm known as the dung beetle optimization algorithm based on bounded reflection optimization and multi-strategy fusion (BFDBO), which is designed to tackle the complexities associated with multi-UAV collaborative trajectory planning in intricate battlefield environments. Initially, a collaborative planning cost function for the multi-UAV system is formulated, thereby converting the trajectory planning challenge into an optimization problem. Building on the foundational dung beetle optimization (DBO) algorithm, BFDBO incorporates three significant innovations: a boundary reflection mechanism, an adaptive mixed exploration strategy, and a dynamic multi-scale mutation strategy. These enhancements are intended to… More >

Tayfun Abut^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4023-4042, 2025, DOI:10.32604/cmc.2025.066920 - 23 September 2025

Abstract In this study, a dynamic model for an inverted pendulum system (IPS) attached to a car is created, and two different control methods are applied to control the system. The designed control algorithms aim to stabilize the pendulum arms in the upright position and the car to reach the equilibrium position. Grey Wolf Optimization-based Linear Quadratic Regulator (GWO-LQR) and GWO-based Fuzzy LQR (FLQR) control algorithms are used in the control process. To improve the performance of the LQR and FLQR methods, the optimum values of the coefficients corresponding to the foot points of the membership… More >

Raja Masadeh¹, Omar Almomani^2,*, Abdullah Zaqebah¹, Shayma Masadeh³, Kholoud Alshqurat³, Ahmad Sharieh⁴, Nesreen Alsharman⁵

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3709-3737, 2025, DOI:10.32604/cmc.2025.066797 - 23 September 2025

Abstract This research presents a novel nature-inspired metaheuristic optimization algorithm, called the Narwhale Optimization Algorithm (NWOA). The algorithm draws inspiration from the foraging and prey-hunting strategies of narwhals, “unicorns of the sea”, particularly the use of their distinctive spiral tusks, which play significant roles in hunting, searching prey, navigation, echolocation, and complex social interaction. Particularly, the NWOA imitates the foraging strategies and techniques of narwhals when hunting for prey but focuses mainly on the cooperative and exploratory behavior shown during group hunting and in the use of their tusks in sensing and locating prey under the… More >