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  • Open Access

    ARTICLE

    A Federated Learning Framework with Blockchain-Based Auditable Participant Selection

    Huang Zeng, Mingtian Zhang, Tengfei Liu, Anjia Yang*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5125-5142, 2024, DOI:10.32604/cmc.2024.052846 - 20 June 2024

    Abstract Federated learning is an important distributed model training technique in Internet of Things (IoT), in which participant selection is a key component that plays a role in improving training efficiency and model accuracy. This module enables a central server to select a subset of participants to perform model training based on data and device information. By doing so, selected participants are rewarded and actively perform model training, while participants that are detrimental to training efficiency and model accuracy are excluded. However, in practice, participants may suspect that the central server may have miscalculated and thus… More >

  • Open Access

    ARTICLE

    Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

    Imtiaz Ahmad1, Tariq Hussain2, Babar Shah3, Altaf Hussain4, Iqtidar Ali1, Farman Ali5,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3585-3629, 2024, DOI:10.32604/cmc.2024.050596 - 20 June 2024

    Abstract Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost. One of them is a sensor network with embedded sensors working as the primary nodes, termed Wireless Sensor Networks (WSNs), in which numerous sensors are connected to at least one Base Station (BS). These sensors gather information from the environment and transmit it to a BS or gathering location. WSNs have several challenges, including throughput, energy usage, and network lifetime concerns. Different strategies have been applied to get over these… More >

  • Open Access

    ARTICLE

    Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

    Mehrdad Shoeibi1, Mohammad Mehdi Sharifi Nevisi2, Reza Salehi3, Diego Martín3,*, 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 >

  • Open Access

    ARTICLE

    A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics—A Supply Chain Backlog Elimination Framework

    Yasser Hachaichi1, Ayman E. Khedr1, Amira M. Idrees2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4081-4105, 2024, DOI:10.32604/cmc.2024.048929 - 20 June 2024

    Abstract The diversity of data sources resulted in seeking effective manipulation and dissemination. The challenge that arises from the increasing dimensionality has a negative effect on the computation performance, efficiency, and stability of computing. One of the most successful optimization algorithms is Particle Swarm Optimization (PSO) which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task. This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which… 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 >

  • Open Access

    ARTICLE

    Research on the Icing Diagnosis of Wind Turbine Blades Based on FS–XGBoost–EWMA

    Jicai Guo1,2, Xiaowen Song1,2,*, Chang Liu1,2, Yanfeng Zhang1,2, Shijie Guo1,2, Jianxin Wu1,2, Chang Cai3, Qing’an Li3,*

    Energy Engineering, Vol.121, No.7, pp. 1739-1758, 2024, DOI:10.32604/ee.2024.048854 - 11 June 2024

    Abstract In winter, wind turbines are susceptible to blade icing, which results in a series of energy losses and safe operation problems. Therefore, blade icing detection has become a top priority. Conventional methods primarily rely on sensor monitoring, which is expensive and has limited applications. Data-driven blade icing detection methods have become feasible with the development of artificial intelligence. However, the data-driven method is plagued by limited training samples and icing samples; therefore, this paper proposes an icing warning strategy based on the combination of feature selection (FS), eXtreme Gradient Boosting (XGBoost) algorithm, and exponentially weighted… More >

  • Open Access

    ARTICLE

    A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine

    Sen-Hui Wang1,2,*, Xi Kang1, Cheng Wang1, Tian-Bing Ma1, Xiang He2, Ke Yang2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1405-1427, 2024, DOI:10.32604/cmes.2024.049281 - 20 May 2024

    Abstract Accurately predicting the remaining useful life (RUL) of bearings in mining rotating equipment is vital for mining enterprises. This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features. This study proposes a hybrid predictive model to assess the RUL of rolling element bearings. The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features. The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm. Subsequently,… More >

  • Open Access

    ARTICLE

    Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

    Deng Yang1, Chong Zhou1,*, 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 >

  • Open Access

    ARTICLE

    Suboptimal Feature Selection Techniques for Effective Malicious Traffic Detection on Lightweight Devices

    So-Eun Jeon1, Ye-Sol Oh1, Yeon-Ji Lee1, Il-Gu Lee1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1669-1687, 2024, DOI:10.32604/cmes.2024.047239 - 20 May 2024

    Abstract With the advancement of wireless network technology, vast amounts of traffic have been generated, and malicious traffic attacks that threaten the network environment are becoming increasingly sophisticated. While signature-based detection methods, static analysis, and dynamic analysis techniques have been previously explored for malicious traffic detection, they have limitations in identifying diversified malware traffic patterns. Recent research has been focused on the application of machine learning to detect these patterns. However, applying machine learning to lightweight devices like IoT devices is challenging because of the high computational demands and complexity involved in the learning process. In… More >

  • Open Access

    ARTICLE

    A Novel Approach to Energy Optimization: Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

    Muhammad Salman Qamar1,*, Ihsan ul Haq1, Amil Daraz2, Atif M. Alamri3, Salman A. AlQahtani4, Muhammad Fahad Munir1

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2945-2970, 2024, DOI:10.32604/cmc.2024.050168 - 15 May 2024

    Abstract In pursuit of enhancing the Wireless Sensor Networks (WSNs) energy efficiency and operational lifespan, this paper delves into the domain of energy-efficient routing protocols. In WSNs, the limited energy resources of Sensor Nodes (SNs) are a big challenge for ensuring their efficient and reliable operation. WSN data gathering involves the utilization of a mobile sink (MS) to mitigate the energy consumption problem through periodic network traversal. The mobile sink (MS) strategy minimizes energy consumption and latency by visiting the fewest nodes or pre-determined locations called rendezvous points (RPs) instead of all cluster heads (CHs). CHs… More >

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