Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (16)
  • Open Access


    Unknown Attack Detection: Combining Relabeling and Hybrid Intrusion Detection

    Gun-Yoon Shin1, Dong-Wook Kim1, Sang-Soo Kim2, Myung-Mook Han3,*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3289-3303, 2021, DOI:10.32604/cmc.2021.017502

    Abstract Detection of unknown attacks like a zero-day attack is a research field that has long been studied. Recently, advances in Machine Learning (ML) and Artificial Intelligence (AI) have led to the emergence of many kinds of attack-generation tools developed using these technologies to evade detection skillfully. Anomaly detection and misuse detection are the most commonly used techniques for detecting intrusion by unknown attacks. Although anomaly detection is adequate for detecting unknown attacks, its disadvantage is the possibility of high false alarms. Misuse detection has low false alarms; its limitation is that it can detect only known attacks. To overcome such… More >

  • Open Access


    Determination of Cup to Disc Ratio Using Unsupervised Machine Learning Techniques for Glaucoma Detection

    R. Praveena*, T. R. GaneshBabu

    Molecular & Cellular Biomechanics, Vol.18, No.2, pp. 69-86, 2021, DOI:10.32604/mcb.2021.014622

    Abstract The cup nerve head, optic cup, optic disc ratio and neural rim configuration are observed as important for detecting glaucoma at an early stage in clinical practice. The main clinical indicator of glaucoma optic cup to disc ratio is currently determined manually by limiting the mass screening was potential. This paper proposes the following methods for an automatic cup to disc ratio determination. In the first part of the work, fundus image of the optic disc region is considered. Clustering means K is used automatically to extract the optic disc whereas K-value is automatically selected by algorithm called hill climbing.… More >

  • Open Access


    A User-Transformer Relation Identification Method Based on QPSO and Kernel Fuzzy Clustering

    Yong Xiao1, Xin Jin1, Jingfeng Yang2, Yanhua Shen3,*, Quansheng Guan4

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1293-1313, 2021, DOI:10.32604/cmes.2021.012562

    Abstract User-transformer relations are significant to electric power marketing, power supply safety, and line loss calculations. To get accurate user-transformer relations, this paper proposes an identification method for user-transformer relations based on improved quantum particle swarm optimization (QPSO) and Fuzzy C-Means Clustering. The main idea is: as energy meters at different transformer areas exhibit different zero-crossing shift features, we classify the zero-crossing shift data from energy meters through Fuzzy C-Means Clustering and compare it with that at the transformer end to identify user-transformer relations. The proposed method contributes in three main ways. First, based on the fuzzy C-means clustering algorithm (FCM),… More >

  • Open Access


    An Intelligent Incremental Filtering Feature Selection and Clustering Algorithm for Effective Classification

    U. Kanimozhi, D. Manjula

    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 701-709, 2018, DOI:10.1080/10798587.2017.1307626

    Abstract We are witnessing the era of big data computing where computing the resources is becoming the main bottleneck to deal with those large datasets. In the case of high-dimensional data where each view of data is of high dimensionality, feature selection is necessary for further improving the clustering and classification results. In this paper, we propose a new feature selection method, Incremental Filtering Feature Selection (IF2S) algorithm, and a new clustering algorithm, Temporal Interval based Fuzzy Minimal Clustering (TIFMC) algorithm that employs the Fuzzy Rough Set for selecting optimal subset of features and for effective grouping of large volumes of… More >

  • Open Access


    Resource Allocation and Power Control Policy for Device-toDevice Communication Using Multi-Agent Reinforcement Learning

    Yifei Wei1, *, Yinxiang Qu1, Min Zhao1, Lianping Zhang2, F. Richard Yu3

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1515-1532, 2020, DOI:10.32604/cmc.2020.09130

    Abstract Device-to-Device (D2D) communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity. In this paper, we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput. Firstly, we treat each D2D pair as an independent agent. Each agent makes decisions based on the local channel states information observed by itself. The multi-agent Reinforcement Learning (RL) algorithm is proposed for our multi-user system. We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected,… More >

  • Open Access


    Fuzzy C-Means Algorithm Automatically Determining Optimal Number of Clusters

    Ruikang Xing1,*, Chenghai Li1

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 767-780, 2019, DOI:10.32604/cmc.2019.04500

    Abstract In clustering analysis, the key to deciding clustering quality is to determine the optimal number of clusters. At present, most clustering algorithms need to give the number of clusters in advance for clustering analysis of the samples. How to gain the correct optimal number of clusters has been an important topic of clustering validation study. By studying and analyzing the FCM algorithm in this study, an accurate and efficient algorithm used to confirm the optimal number of clusters is proposed for the defects of traditional FCM algorithm. For time and clustering accuracy problems of FCM algorithm and relevant algorithms automatically… More >

Displaying 11-20 on page 2 of 16. Per Page