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  • Open Access

    ARTICLE

    Microarray Gene Expression Classification: An Efficient Feature Selection Using Hybrid Swarm Intelligence Algorithm

    Punam Gulande*, R. N. Awale

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 937-952, 2024, DOI:10.32604/csse.2024.046123 - 17 July 2024

    Abstract The study of gene expression has emerged as a vital tool for cancer diagnosis and prognosis, particularly with the advent of microarray technology that enables the measurement of thousands of genes in a single sample. While this wealth of data offers invaluable insights for disease management, the high dimensionality poses a challenge for multiclass classification. In this context, selecting relevant features becomes essential to enhance classification model performance. Swarm Intelligence algorithms have proven effective in addressing this challenge, owing to their ability to navigate intricate, non-linear feature-class relationships. This paper introduces a novel hybrid swarm More >

  • Open Access

    ARTICLE

    Evaluations of Chris-Jerry Data Using Generalized Progressive Hybrid Strategy and Its Engineering Applications

    Refah Alotaibi1, Hoda Rezk2, Ahmed Elshahhat3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3073-3103, 2024, DOI:10.32604/cmes.2024.050606 - 08 July 2024

    Abstract A new one-parameter Chris-Jerry distribution, created by mixing exponential and gamma distributions, is discussed in this article in the presence of incomplete lifetime data. We examine a novel generalized progressively hybrid censoring technique that ensures the experiment ends at a predefined period when the model of the test participants has a Chris-Jerry (CJ) distribution. When the indicated censored data is present, Bayes and likelihood estimations are used to explore the CJ parameter and reliability indices, including the hazard rate and reliability functions. We acquire the estimated asymptotic and credible confidence intervals of each unknown quantity. More >

  • Open Access

    ARTICLE

    Optimizing Hybrid Fibre-Reinforced Polymer Bars Design: A Machine Learning Approach

    Aneel Manan1, Pu Zhang1,*, Shoaib Ahmad2, Jawad Ahmad2

    Journal of Polymer Materials, Vol.41, No.1, pp. 15-44, 2024, DOI:10.32604/jpm.2024.053859 - 21 June 2024

    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 >

  • Open Access

    ARTICLE

    Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

    Djeldjli Halima1,*, 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 - 20 June 2024

    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

    Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

  • Open Access

    ARTICLE

    Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

    Mohammad Aldossary1,*, Hatem A. Alharbi2, Nasir Ayub3

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4603-4620, 2024, DOI:10.32604/cmc.2024.050862 - 20 June 2024

    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 >

  • Open Access

    ARTICLE

    Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

    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 - 20 June 2024

    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 >

  • Open Access

    ARTICLE

    Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

    Abdulelah Alwabel1,*, Chinmaya Kumar Swain2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4127-4148, 2024, DOI:10.32604/cmc.2024.048833 - 20 June 2024

    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 >

  • Open Access

    ARTICLE

    THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

    Monerah Alawadh*, Ahmed Barnawi

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4995-5015, 2024, DOI:10.32604/cmc.2024.048762 - 20 June 2024

    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 >

  • Open Access

    ARTICLE

    An Imbalanced Data Classification Method Based on Hybrid Resampling and Fine Cost Sensitive Support Vector Machine

    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 - 20 June 2024

    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 >

  • Open Access

    ARTICLE

    Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

    Mutasem K. Alsmadi*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5175-5200, 2024, DOI:10.32604/cmc.2024.044065 - 20 June 2024

    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 >

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