Aneel Manan¹, Pu Zhang^1,*, Shoaib Ahmad², Jawad Ahmad²

Journal of Polymer Materials, Vol.41, No.1, pp. 15-44, 2024, DOI:10.32604/jpm.2024.053859

Abstract Fiber-reinforced polymer (FRP) bars are gaining popularity as an alternative to steel reinforcement due to their advantages such as corrosion resistance and high strength-to-weight ratio. However, FRP has a lower modulus of elasticity compared to steel. Therefore, special attention is required in structural design to address deflection related issues and ensure ductile failure. This research explores the use of machine learning algorithms such as gene expression programming (GEP) to develop a simple and effective equation for predicting the elastic modulus of hybrid fiber-reinforced polymer (HFPR) bars. A comprehensive database of 125 experimental results of HFPR… More >

Djeldjli Halima^1,*, Benatiallah Djelloul¹, Ghasri Mehdi², Tanougast Camel³, Benatiallah Ali⁴, Benabdelkrim Bouchra¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4725-4740, 2024, DOI:10.32604/cmc.2024.051002

Abstract When designing solar systems and assessing the effectiveness of their many uses, estimating sun irradiance is a crucial first step. This study examined three approaches (ANN, GA-ANN, and ANFIS) for estimating daily global solar radiation (GSR) in the south of Algeria: Adrar, Ouargla, and Bechar. The proposed hybrid GA-ANN model, based on genetic algorithm-based optimization, was developed to improve the ANN model. The GA-ANN and ANFIS models performed better than the standalone ANN-based model, with GA-ANN being better suited for forecasting in all sites, and it performed the best with the best values in the… More > Graphic Abstract

Mohammad Aldossary^1,*, Hatem A. Alharbi², Nasir Ayub³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4603-4620, 2024, DOI:10.32604/cmc.2024.050862

Abstract Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure, thereby revolutionizing computer processes. However, the rising energy consumption in cloud centers poses a significant challenge, especially with the escalating energy costs. This paper tackles this issue by introducing efficient solutions for data placement and node management, with a clear emphasis on the crucial role of the Internet of Things (IoT) throughout the research process. The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around… More >

Jing Gao^*, Mingxuan Ji, Hongjiang Wang, Zhongxiao Du

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5017-5030, 2024, DOI:10.32604/cmc.2024.050158

Abstract With the continuous advancement of China’s “peak carbon dioxide emissions and Carbon Neutrality” process, the proportion of wind power is increasing. In the current research, aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data, a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine (IL-Bagging-DHKELM) error affinity propagation cluster analysis is proposed. The algorithm effectively combines deep hybrid kernel extreme learning machine (DHKELM) with incremental learning (IL). Firstly, an initial wind power prediction model is trained using the Bagging-DHKELM… More >

Abdulelah Alwabel^1,*, Chinmaya Kumar Swain²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4127-4148, 2024, DOI:10.32604/cmc.2024.048833

Abstract Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources. However, the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes, thus making the application placement problem more complex than that in cloud computing. An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the… More >

Monerah Alawadh^*, Ahmed Barnawi

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4995-5015, 2024, DOI:10.32604/cmc.2024.048762

Abstract Association rule learning (ARL) is a widely used technique for discovering relationships within datasets. However, it often generates excessive irrelevant or ambiguous rules. Therefore, post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors. Recently, several post-processing methods have been proposed, each with its own strengths and weaknesses. In this paper, we propose THAPE (Tunable Hybrid Associative Predictive Engine), which combines descriptive and predictive techniques. By leveraging both techniques, our aim is to enhance the quality of analyzing generated rules. This includes removing irrelevant… More >

Bo Zhu^*, Xiaona Jing, Lan Qiu, Runbo Li

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3977-3999, 2024, DOI:10.32604/cmc.2024.048062

Abstract When building a classification model, the scenario where the samples of one class are significantly more than those of the other class is called data imbalance. Data imbalance causes the trained classification model to be in favor of the majority class (usually defined as the negative class), which may do harm to the accuracy of the minority class (usually defined as the positive class), and then lead to poor overall performance of the model. A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article, which is based on a new hybrid… More >

Mutasem K. Alsmadi^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5175-5200, 2024, DOI:10.32604/cmc.2024.044065

Abstract Lung cancer is among the most frequent cancers in the world, with over one million deaths per year. Classification is required for lung cancer diagnosis and therapy to be effective, accurate, and reliable. Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner. Machine Learning (ML) has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique. Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification. Normally,… More >

Quan Zhou^*, Haiyang Li

Energy Engineering, Vol.121, No.7, pp. 1781-1803, 2024, DOI:10.32604/ee.2024.049387

Abstract Renewable energy is becoming more attractive as traditional fossil fuels are rapidly depleted and expensive, and their use would release pollutants. Power systems that use both wind and solar energy are more reliable and efficient than those that utilize only one energy. Hybrid renewable energy systems (HRES) are viable for remote areas operating in standalone mode. This paper aims to present the state-of-the-art research on off-grid solar-wind hybrid energy systems over the last two decades. More than 1500 published articles extracted from the Web of Science are analyzed by bibliometric methods and processed by CiteSpace… More >

Lin Ma¹, Liyong Wang¹, Shuang Zeng¹, Yutong Zhao¹, Chang Liu¹, Heng Zhang¹, Qiong Wu^2,*, Hongbo Ren²

Energy Engineering, Vol.121, No.6, pp. 1473-1493, 2024, DOI:10.32604/ee.2024.047332

Abstract Accurate load forecasting forms a crucial foundation for implementing household demand response plans and optimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations, a single prediction model is hard to capture temporal features effectively, resulting in diminished prediction accuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neural network (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), is proposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features from the original data, enhancing the quality of data… More >