Yingnan Zhao^*, Yuyuan Ruan, Zhen Peng

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 549-566, 2024, DOI:10.32604/cmc.2024.056240 - 15 October 2024

Abstract As the penetration ratio of wind power in active distribution networks continues to increase, the system exhibits some characteristics such as randomness and volatility. Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization control. Based on the spatio-temporal features of Numerical Weather Prediction (NWP) data, it proposes the WVMD_DSN (Whale Optimization Algorithm, Variational Mode Decomposition, Dual Stream Network) model. The model first applies Pearson correlation coefficient (PCC) to choose some NWP features with strong correlation to wind power to form the feature set. Then, it decomposes the feature set More >

Brij Bhooshan Gupta^1,2,3,*, Akshat Gaurav⁴, Razaz Waheeb Attar⁵, Varsha Arya^6,7, Ahmed Alhomoud⁸, Kwok Tai Chui⁹

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4895-4916, 2024, DOI:10.32604/cmc.2024.050815 - 12 September 2024

Abstract Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape, necessitating the development of more sophisticated detection methods. Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishing Uniform Resource Locator (URLs). Addressing these challenge, we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network (RNN) with the hyperparameter optimization prowess of the Whale Optimization Algorithm (WOA). Our model capitalizes on an extensive Kaggle dataset, featuring over 11,000 URLs, each More >

Guangfei Jia^*, Yanchao Meng, Zhiying Qin

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 445-463, 2024, DOI:10.32604/sdhm.2024.049298 - 05 June 2024

Abstract The vibration signals of rolling bearings exhibit nonlinear and non-stationary characteristics under the influence of noise. In intelligent fault diagnosis, unprocessed signals will lead to weak fault characteristics and low diagnostic accuracy. To solve the above problem, a fault diagnosis method based on parameter optimization feature mode decomposition and improved deep belief networks is proposed. The feature mode decomposition is used to decompose the vibration signals. The parameter adaptation of feature mode decomposition is implemented by improved whale optimization algorithm including Levy flight strategy and adaptive weight. The selection of activation function and parameters is More > Graphic Abstract

Deng Yang¹, Chong Zhou^1,*, Xuemeng Wei², Zhikun Chen³, Zheng Zhang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1563-1593, 2024, DOI:10.32604/cmes.2024.048049 - 20 May 2024

Abstract In classification problems, datasets often contain a large amount of features, but not all of them are relevant for accurate classification. In fact, irrelevant features may even hinder classification accuracy. Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate. Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter, but the results obtained depend on the value of the parameter. To eliminate this parameter’s influence, the problem can be reformulated as a multi-objective optimization problem. The… More >

Chirag Chandrashekar¹, Pradeep Krishnadoss^1,*, Vijayakumar Kedalu Poornachary¹, Balasundaram Ananthakrishnan^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2593-2616, 2024, DOI:10.32604/cmc.2024.046304 - 27 February 2024

Abstract Cloud computing provides a diverse and adaptable resource pool over the internet, allowing users to tap into various resources as needed. It has been seen as a robust solution to relevant challenges. A significant delay can hamper the performance of IoT-enabled cloud platforms. However, efficient task scheduling can lower the cloud infrastructure’s energy consumption, thus maximizing the service provider’s revenue by decreasing user job processing times. The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm (MCWOA), combines elements of the Chimp Optimization Algorithm (COA) and the Whale Optimization Algorithm (WOA). To enhance MCWOA’s… More >

Thi-Kien Dao¹, Trong-The Nguyen^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2201-2237, 2024, DOI:10.32604/cmes.2023.029880 - 15 December 2023

Abstract Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging and fundamental operations in various monitoring or tracking applications because the network deploys a large area and allocates the acquired location information to unknown devices. The metaheuristic approach is one of the most advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditional methods that often suffer from computational time problems and small network deployment scale. This study proposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on the siege mechanism… More >

Xiang Wang¹, Liangsa Wang^2,*, Han Li¹, Yibin Guo¹

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2935-2969, 2023, DOI:10.32604/cmc.2023.044948 - 26 December 2023

Abstract The original whale optimization algorithm (WOA) has a low initial population quality and tends to converge to local optimal solutions. To address these challenges, this paper introduces an improved whale optimization algorithm called OLCHWOA, incorporating a chaos mechanism and an opposition-based learning strategy. This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase, thereby enhancing the quality of the initial whale population. Additionally, including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations. The work and contributions of this paper are primarily reflected in two aspects.… More >

Shorouq Alshawabkeh, Li Wu^*, Daojun Dong, Yao Cheng, Liping Li, Mohammad Alanaqreh

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 63-77, 2023, DOI:10.32604/cmc.2023.042183 - 31 October 2023

Abstract Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses. Recent advancements in deep learning (DL) techniques have shown promising results in detecting pavement cracks; however, the selection of relevant features for classification remains challenging. In this study, we propose a new approach for pavement crack detection that integrates deep learning for feature extraction, the whale optimization algorithm (WOA) for feature selection, and random forest (RF) for classification. The performance of the models was evaluated using accuracy, recall, precision, F1 score, and area under the receiver operating characteristic curve (AUC).… More >

Motasem S. Alsawadi^1,*, El-Sayed M. El-kenawy², Miguel Rio¹

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2767-2782, 2023, DOI:10.32604/iasc.2023.039440 - 11 September 2023

Abstract The BlazePose, which models human body skeletons as spatiotemporal graphs, has achieved fantastic performance in skeleton-based action identification. Skeleton extraction from photos for mobile devices has been made possible by the BlazePose system. A Spatial-Temporal Graph Convolutional Network (STGCN) can then forecast the actions. The Spatial-Temporal Graph Convolutional Network (STGCN) can be improved by simply replacing the skeleton input data with a different set of joints that provide more information about the activity of interest. On the other hand, existing approaches require the user to manually set the graph’s topology and then fix it across… More >

Tiantian Liang^*, Runze Wang, Xuxiu Zhang, Yingdong Wang, Jianxiong Yang

Structural Durability & Health Monitoring, Vol.17, No.5, pp. 433-455, 2023, DOI:10.32604/sdhm.2023.029331 - 07 September 2023

Abstract In this study, an optimized long short-term memory (LSTM) network is proposed to predict the reliability and remaining useful life (RUL) of rolling bearings based on an improved whale-optimized algorithm (IWOA). The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing. To provide covariates for reliability assessment, a kernel principal component analysis is used to reduce the dimensionality of the features. A Weibull distribution proportional hazard model (WPHM) is used for the reliability assessment of rolling bearing, and a beluga… More > Graphic Abstract