Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (512)
  • Open Access

    ARTICLE

    Two-Stage Planning of Distributed Power Supply and Energy Storage Capacity Considering Hierarchical Partition Control of Distribution Network with Source-Load-Storage

    Junhui Li1, Yuqing Zhang1, Can Chen2, Xiaoxiao Wang2, Yinchi Shao2, Xingxu Zhu1, Cuiping Li1,*

    Energy Engineering, Vol.121, No.9, pp. 2389-2408, 2024, DOI:10.32604/ee.2024.050239 - 19 August 2024

    Abstract Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network, it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system. This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load. Firstly, an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number… More >

  • Open Access

    ARTICLE

    A Feature Selection Method Based on Hybrid Dung Beetle Optimization Algorithm and Slap Swarm Algorithm

    Wei Liu*, Tengteng Ren

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2979-3000, 2024, DOI:10.32604/cmc.2024.053627 - 15 August 2024

    Abstract Feature Selection (FS) is a key pre-processing step in pattern recognition and data mining tasks, which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models. In recent years, meta-heuristic algorithms have been widely used in FS problems, so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization (HBCSSDBO) algorithm is proposed in this paper to improve the effect of FS. In this hybrid algorithm, the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem. By combining the… More >

  • Open Access

    ARTICLE

    A Pre-Selection-Based Ant Colony System for Integrated Resources Scheduling Problem at Marine Container Terminal

    Rong Wang1, Xinxin Xu2, Zijia Wang3,*, Fei Ji1, Nankun Mu4

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2363-2385, 2024, DOI:10.32604/cmc.2024.053564 - 15 August 2024

    Abstract Marine container terminal (MCT) plays a key role in the marine intelligent transportation system and international logistics system. However, the efficiency of resource scheduling significantly influences the operation performance of MCT. To solve the practical resource scheduling problem (RSP) in MCT efficiently, this paper has contributions to both the problem model and the algorithm design. Firstly, in the problem model, different from most of the existing studies that only consider scheduling part of the resources in MCT, we propose a unified mathematical model for formulating an integrated RSP. The new integrated RSP model allocates and… More >

  • Open Access

    ARTICLE

    An Attention-Based Approach to Enhance the Detection and Classification of Android Malware

    Abdallah Ghourabi*

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2743-2760, 2024, DOI:10.32604/cmc.2024.053163 - 15 August 2024

    Abstract The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in malware. These malicious applications have become a serious concern to the security of Android systems. To address this problem, researchers have proposed several machine-learning models to detect and classify Android malware based on analyzing features extracted from Android samples. However, most existing studies have focused on the classification task and overlooked the feature selection process, which is crucial to reduce the training time and maintain or improve the classification results. The… More >

  • Open Access

    ARTICLE

    5G Resource Allocation Using Feature Selection and Greylag Goose Optimization Algorithm

    Amel Ali Alhussan1, S. K. Towfek2,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1179-1201, 2024, DOI:10.32604/cmc.2024.049874 - 18 July 2024

    Abstract In the contemporary world of highly efficient technological development, fifth-generation technology (5G) is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second (Gbps). As far as the current implementations are concerned, they are at the level of slightly below 1 Gbps, but this allowed a great leap forward from fourth generation technology (4G), as well as enabling significantly reduced latency, making 5G an absolute necessity for applications such as gaming, virtual conferencing, and other interactive electronic processes. Prospects of this change are not limited to connectivity… More >

  • Open Access

    ARTICLE

    A Multivariate Relevance Frequency Analysis Based Feature Selection for Classification of Short Text Data

    Saravanan Arumugam*

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 989-1008, 2024, DOI:10.32604/csse.2024.051770 - 17 July 2024

    Abstract Text mining presents unique challenges in extracting meaningful information from the vast volumes of digital documents. Traditional filter feature selection methods often fall short in handling the complexities of short text data. To address this issue, this paper presents a novel approach to feature selection in text classification, aiming to overcome challenges posed by high dimensionality and reduced accuracy in the face of increasing digital document volumes. Unlike traditional filter feature selection techniques, the proposed method, Multivariate Relevance Frequency Analysis, offers a tailored solution for diverse text data types. By integrating positive, negative, and dependency… More >

  • 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

    Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

    Asad Raza1,*, Shahzad Memon1, Muhammad Ali Nizamani1, Mahmood Hussain Shah2

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 545-566, 2024, DOI:10.32604/iasc.2024.051779 - 11 July 2024

    Abstract Smart Industrial environments use the Industrial Internet of Things (IIoT) for their routine operations and transform their industrial operations with intelligent and driven approaches. However, IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet. Traditional signature-based IDS are effective in detecting known attacks, but they are unable to detect unknown emerging attacks. Therefore, there is the need for an IDS which can learn from data and detect new threats. Ensemble Machine Learning (ML) and individual Deep Learning (DL) based IDS have been developed, and these individual models achieved… More >

  • Open Access

    ARTICLE

    Enhanced Arithmetic Optimization Algorithm Guided by a Local Search for the Feature Selection Problem

    Sana Jawarneh*

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 511-525, 2024, DOI:10.32604/iasc.2024.047126 - 11 July 2024

    Abstract High-dimensional datasets present significant challenges for classification tasks. Dimensionality reduction, a crucial aspect of data preprocessing, has gained substantial attention due to its ability to improve classification performance. However, identifying the optimal features within high-dimensional datasets remains a computationally demanding task, necessitating the use of efficient algorithms. This paper introduces the Arithmetic Optimization Algorithm (AOA), a novel approach for finding the optimal feature subset. AOA is specifically modified to address feature selection problems based on a transfer function. Additionally, two enhancements are incorporated into the AOA algorithm to overcome limitations such as limited precision, slow More >

  • Open Access

    ARTICLE

    An Integrated Bipolar Picture Fuzzy Decision Driven System to Scrutinize Food Waste Treatment Technology through Assorted Factor Analysis

    Navaneethakrishnan Suganthi Keerthana Devi1, Samayan Narayanamoorthy1, Thirumalai Nallasivan Parthasarathy1, Chakkarapani Sumathi Thilagasree2, Dragan Pamucar3,4,*, Vladimir Simic5,6, Hasan Dinçer7,8, Serhat Yüksel7,8

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2665-2687, 2024, DOI:10.32604/cmes.2024.050954 - 08 July 2024

    Abstract Food Waste (FW) is a pressing environmental concern that affects every country globally. About one-third of the food that is produced ends up as waste, contributing to the carbon footprint. Hence, the FW must be properly treated to reduce environmental pollution. This study evaluates a few available Food Waste Treatment (FWT) technologies, such as anaerobic digestion, composting, landfill, and incineration, which are widely used. A Bipolar Picture Fuzzy Set (BPFS) is proposed to deal with the ambiguity and uncertainty that arise when converting a real-world problem to a mathematical model. A novel Criteria Importance Through… More >

Displaying 21-30 on page 3 of 512. Per Page