Helen Shin Huey Wee, Nur Syazreen Ahmad^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1301-1354, 2025, DOI:10.32604/cmes.2025.063611 - 30 May 2025

Abstract This paper reviews recent advancements in system identification methods for perturbed motorized systems, focusing on brushed DC motors, brushless DC motors, and permanent magnet synchronous motors. It examines data acquisition setups and evaluates conventional and metaheuristic optimization algorithms, highlighting their advantages, limitations, and applications. The paper explores emerging trends in model structures and parameter optimization techniques that address specific perturbations such as varying loads, noise, and friction. A comparative performance analysis is also included to assess several widely used optimization methods, including least squares (LS), particle swarm optimization (PSO), grey wolf optimizer (GWO), bat algorithm… More >

Xiangyang Cao^1,2, Yaojie Zheng^1,2, Hanbin Xiao^1,2,*, Min Xiao^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 289-334, 2025, DOI:10.32604/cmes.2025.063954 - 11 April 2025

Abstract This study investigates the Maximum Power Point Tracking (MPPT) control method of offshore wind-photovoltaic hybrid power generation system with offshore crane-assisted. A new algorithm of Global Fast Integral Sliding Mode Control (GFISMC) is proposed based on the tip speed ratio method and sliding mode control. The algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low jitter. An offshore wind power generation system model is presented to verify the algorithm effect. An offshore More >

Varada Rajkumar Kukkala¹, Surapaneni Phani Praveen², Naga Satya Koti Mani Kumar Tirumanadham³, Parvathaneni Naga Srinivasu^4,5,*

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1085-1112, 2024, DOI:10.32604/csse.2024.053603 - 13 September 2024

Abstract This paper investigates the application of machine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely; Z-Score incorporated with Grey Wolf Optimization (GWO) as well as Interquartile Range (IQR) coupled with Ant Colony Optimization (ACO). Using a performance index, it is shown that when compared with the Z-Score and GWO with AdaBoost, the IQR and ACO, with AdaBoost are not very accurate (89.0% vs. 86.0%) and less discriminative (Area Under the Curve (AUC) score of 93.0% vs. 91.0%). The Z-Score and GWO… More >

Mehrdad Shoeibi¹, Mohammad Mehdi Sharifi Nevisi², Reza Salehi³, Diego Martín^3,*, Zahra Halimi⁴, Sahba Baniasadi⁵

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3469-3493, 2024, DOI:10.32604/cmc.2024.049847 - 20 June 2024

Abstract Hyperspectral (HS) image classification plays a crucial role in numerous areas including remote sensing (RS), agriculture, and the monitoring of the environment. Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification. This process involves selecting the most informative spectral bands, which leads to a reduction in data volume. Focusing on these key bands also enhances the accuracy of classification algorithms, as redundant or irrelevant bands, which can introduce noise and lower model performance, are excluded. In this paper, we propose an approach for HS image classification using… More >

Abdul Qadir Khan¹, Guangmin Sun^1,*, Yu Li¹, Anas Bilal², Malik Abdul Manan¹

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2481-2504, 2023, DOI:10.32604/cmc.2023.043239 - 29 November 2023

Abstract In the emerging field of image segmentation, Fully Convolutional Networks (FCNs) have recently become prominent. However, their effectiveness is intimately linked with the correct selection and fine-tuning of hyperparameters, which can often be a cumbersome manual task. The main aim of this study is to propose a more efficient, less labour-intensive approach to hyperparameter optimization in FCNs for segmenting fundus images. To this end, our research introduces a hyperparameter-optimized Fully Convolutional Encoder-Decoder Network (FCEDN). The optimization is handled by a novel Genetic Grey Wolf Optimization (G-GWO) algorithm. This algorithm employs the Genetic Algorithm (GA) to… More >

Xianjing Zhong¹, Xianbo Sun^1,*, Yuhan Wu²

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1599-1619, 2023, DOI:10.32604/cmc.2023.039912 - 30 August 2023

Abstract To reduce the comprehensive costs of the construction and operation of microgrids and to minimize the power fluctuations caused by randomness and intermittency in distributed generation, a double-layer optimizing configuration method of hybrid energy storage microgrid based on improved grey wolf optimization (IGWO) is proposed. Firstly, building a microgrid system containing a wind-solar power station and electric-hydrogen coupling hybrid energy storage system. Secondly, the minimum comprehensive cost of the construction and operation of the microgrid is taken as the outer objective function, and the minimum peak-to-valley of the microgrid’s daily output is taken as the… More >

Shehab Abdulhabib Alzaeemi¹, Saratha Sathasivam^2,*, Majid Khan bin Majahar Ali², K. G. Tay¹, Muraly Velavan³

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1471-1491, 2023, DOI:10.32604/csse.2023.037366 - 28 July 2023

Abstract Rubber producers, consumers, traders, and those who are involved in the rubber industry face major risks of rubber price fluctuations. As a result, decision-makers are required to make an accurate estimation of the price of rubber. This paper aims to propose hybrid intelligent models, which can be utilized to forecast the price of rubber in Malaysia by employing monthly Malaysia’s rubber pricing data, spanning from January 2016 to March 2021. The projected hybrid model consists of different algorithms with the symbolic Radial Basis Functions Neural Network k-Satisfiability Logic Mining (RBFNN-kSAT). These algorithms, including Grey Wolf… More >

Chandana Gouri Tekkali, Karthika Natarajan^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3171-3187, 2023, DOI:10.32604/cmc.2023.036865 - 31 March 2023

Abstract Fraud Transactions are haunting the economy of many individuals with several factors across the globe. This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions. This research proposes a novel methodology through three stages. Firstly, Synthetic Minority Oversampling Technique (SMOTE) is applied to get balanced data. Secondly, SMOTE is fed to the nature-inspired Meta Heuristic (MH) algorithm, namely Binary Harris Hawks Optimization (BinHHO), Binary Aquila Optimization (BAO), and Binary Grey Wolf Optimization (BGWO), for feature selection. BinHHO has performed well when compared with More >

Ahmed Najat Ahmed¹, JinHyung Kim², Yunyoung Nam^3,*, Mohamed Abouhawwash^4,5

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2527-2542, 2023, DOI:10.32604/csse.2023.036106 - 09 February 2023

Abstract In this current century, most industries are moving towards automation, where human intervention is dramatically reduced. This revolution leads to industrial revolution 4.0, which uses the Internet of Things (IoT) and wireless sensor networks (WSN). With its associated applications, this IoT device is used to compute the received WSN data from devices and transfer it to remote locations for assistance. In general, WSNs, the gateways are a long distance from the base station (BS) and are communicated through the gateways nearer to the BS. At the gateway, which is closer to the BS, energy drains… More >

Ansar Munir Shah¹, Mohammed Aljubayri², Muhammad Faheem Khan¹, Jarallah Alqahtani^2,*, Mahmood ul Hassan³, Adel Sulaiman², Asadullah Shaikh²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2471-2488, 2023, DOI:10.32604/csse.2023.034951 - 09 February 2023

Abstract In the network field, Wireless Sensor Networks (WSN) contain prolonged attention due to afresh augmentations. Industries like health care, traffic, defense, and many more systems espoused the WSN. These networks contain tiny sensor nodes containing embedded processors, Tiny OS, memory, and power source. Sensor nodes are responsible for forwarding the data packets. To manage all these components, there is a need to select appropriate parameters which control the quality of service of WSN. Multiple sensor nodes are involved in transmitting vital information, and there is a need for secure and efficient routing to reach the… More >