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  • Open Access


    Energy-Efficient Cluster in Wireless Sensor Network Using Life Time Delay Clustering Algorithms

    V. Kumar1,*, N. Jayapandian2, P. Balasubramanie3

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 77-86, 2022, DOI:10.32604/csse.2022.023481

    Abstract Through Wireless Sensor Networks (WSN) formation, industrial and academic communities have seen remarkable development in recent decades. One of the most common techniques to derive the best out of wireless sensor networks is to upgrade the operating group. The most important problem is the arrangement of optimal number of sensor nodes as clusters to discuss clustering method. In this method, new client nodes and dynamic methods are used to determine the optimal number of clusters and cluster heads which are to be better organized and proposed to classify each round. Parameters of effective energy use and the ability to decide… More >

  • Open Access


    Research on Thunderstorm Identification Based on Discrete Wavelet Transform

    Xiaopeng Li1, Ziyuan Xu3,4, Jin Han1,*, Xingming Sun1,2, Yi Cao5

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1153-1166, 2022, DOI:10.32604/iasc.2022.023261

    Abstract Lightning has been one of the most talked-about natural disasters worldwide in recent years, as it poses a great threat to all industries and can cause huge economic losses. Thunderstorms are often accompanied by natural phenomena such as lightning strikes and lightning, and many scholars have studied deeply the regulations of thunderstorm generation, movement and dissipation to reduce the risk of lightning damage. Most of the current methods for studying thunderstorms focus on using more complex algorithms based on radar or lightning data, which increases the computational burden and reduces the computational efficiency to some extent. This paper proposes a… More >

  • Open Access


    Spectrum Prediction in Cognitive Radio Network Using Machine Learning Techniques

    D. Arivudainambi1, S. Mangairkarasi1,*, K. A. Varun Kumar2

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1525-1540, 2022, DOI:10.32604/iasc.2022.020463

    Abstract Cognitive Radio (CR) aims to achieve efficient utilization of scarcely available radio spectrum. Spectrum sensing in CR is a basic process for identifying the existence or absence of primary users. In spectrum sensing, CR users suffer from deep fading effects and it requires additional sensing time to identify the primary user. To overcome these challenges, we frame Spectrum Prediction-Channel Allocation (SP-CA) algorithm which consists of three phases. First, clustering mechanisms to select the spectrum coordinator. Second, Eigenvalue based detection method to expand the sensing accuracy of the secondary user. Third, Bayesian inference approach to reduce the performance degradation of the… More >

  • Open Access


    Automated Learning of ECG Streaming Data Through Machine Learning Internet of Things

    Mwaffaq Abu-Alhaija, Nidal M. Turab*

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 45-53, 2022, DOI:10.32604/iasc.2022.021426

    Abstract Applying machine learning techniques on Internet of Things (IoT) data streams will help achieve better understanding, predict future perceptions, and make crucial decisions based on those analytics. The collaboration between IoT, Big Data and machine learning can be found in different domains such as Health care, Smart cities, and Telecommunications. The aim of this paper is to develop a method for automated learning of electrocardiogram (ECG) streaming data to detect any heart beat anomalies. A promising solution is to use medical sensors that transfer vital signs to medical care computer systems, combined with machine learning, such that clinicians can get… More >

  • Open Access


    Performances of K-Means Clustering Algorithm with Different Distance Metrics

    Taher M. Ghazal1,2, Muhammad Zahid Hussain3, Raed A. Said5, Afrozah Nadeem6, Mohammad Kamrul Hasan1, Munir Ahmad7, Muhammad Adnan Khan3,4,*, Muhammad Tahir Naseem3

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 735-742, 2021, DOI:10.32604/iasc.2021.019067

    Abstract Clustering is the process of grouping the data based on their similar properties. Meanwhile, it is the categorization of a set of data into similar groups (clusters), and the elements in each cluster share similarities, where the similarity between elements in the same cluster must be smaller enough to the similarity between elements of different clusters. Hence, this similarity can be considered as a distance measure. One of the most popular clustering algorithms is K-means, where distance is measured between every point of the dataset and centroids of clusters to find similar data objects and assign them to the nearest… More >

  • Open Access


    A Shadowed Rough-fuzzy Clustering Algorithm Based on Mahalanobis Distance for Intrusion Detection

    Lina Wang1,2,*, Jie Wang3, Yongjun Ren4, Zimeng Xing1, Tao Li1, Jinyue Xia5

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 31-47, 2021, DOI:10.32604/iasc.2021.018577

    Abstract Intrusion detection has been widely used in many application domains; thus, it has caught significant attention in academic fields these years. Assembled with more and more sub-systems, the network is more vulnerable to multiple attacks aiming at the network security. Compared with the other issues such as complex environment and resources-constrained devices, network security has been the biggest challenge for Internet construction. To deal with this problem, a fundamental measure for safeguarding network security is to select an intrusion detection algorithm. As is known, it is less effective to determine the abnormal behavior as an intrusion and learn the entire… More >

  • Open Access


    Clustering Algorithms: Taxonomy, Comparison, and Empirical Analysis in 2D Datasets

    Samih M. Mostafa1,2,*

    Journal on Artificial Intelligence, Vol.2, No.4, pp. 189-215, 2020, DOI:10.32604/jai.2020.014944

    Abstract Because of the abundance of clustering methods, comparing between methods and determining which method is proper for a given dataset is crucial. Especially, the availability of huge experimental datasets and transactional and the emerging requirements for data mining and the like needs badly for clustering algorithms that can be applied in various domains. This paper presents essential notions of clustering and offers an overview of the significant features of the most common representative clustering algorithms of clustering categories presented in a comparative way. More specifically the study is based on the numerical type of the data that the algorithm supports,… More >

  • Open Access


    Trade-Off between Efficiency and Effectiveness: A Late Fusion Multi-View Clustering Algorithm

    Yunping Zhao1, Weixuan Liang1, Jianzhuang Lu1,*, Xiaowen Chen1, Nijiwa Kong2

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2709-2722, 2021, DOI:10.32604/cmc.2021.013389

    Abstract Late fusion multi-view clustering (LFMVC) algorithms aim to integrate the base partition of each single view into a consensus partition. Base partitions can be obtained by performing kernel k-means clustering on all views. This type of method is not only computationally efficient, but also more accurate than multiple kernel k-means, and is thus widely used in the multi-view clustering context. LFMVC improves computational efficiency to the extent that the computational complexity of each iteration is reduced from O(n3) to O(n) (where n is the number of samples). However, LFMVC also limits the search space of the optimal solution, meaning that… More >

  • Open Access


    A Clustering Method Based on Brain Storm Optimization Algorithm

    Tianyu Wang, Yu Xue, Yan Zhao, Yuxiang Wang*, Yan Zhang, Yuxiang He

    Journal of Information Hiding and Privacy Protection, Vol.2, No.3, pp. 135-142, 2020, DOI:10.32604/jihpp.2020.010362

    Abstract In the field of data mining and machine learning, clustering is a typical issue which has been widely studied by many researchers, and lots of effective algorithms have been proposed, including K-means, fuzzy c-means (FCM) and DBSCAN. However, the traditional clustering methods are easily trapped into local optimum. Thus, many evolutionary-based clustering methods have been investigated. Considering the effectiveness of brain storm optimization (BSO) in increasing the diversity while the diversity optimization is performed, in this paper, we propose a new clustering model based on BSO to use the global ability of BSO. In our experiment, we apply the novel… More >

  • Open Access


    Weighted Particle Swarm Clustering Algorithm for Self-Organizing Maps

    Guorong Cui, Hao Li, Yachuan Zhang, Rongjing Bu, Yan Kang*, Jinyuan Li, Yang Hu

    Journal of Quantum Computing, Vol.2, No.2, pp. 85-95, 2020, DOI:10.32604/jqc.2020.09717

    Abstract The traditional K-means clustering algorithm is difficult to determine the cluster number, which is sensitive to the initialization of the clustering center and easy to fall into local optimum. This paper proposes a clustering algorithm based on self-organizing mapping network and weight particle swarm optimization SOM&WPSO (Self-Organization Map and Weight Particle Swarm Optimization). Firstly, the algorithm takes the competitive learning mechanism of a self-organizing mapping network to divide the data samples into coarse clusters and obtain the clustering center. Then, the obtained clustering center is used as the initialization parameter of the weight particle swarm optimization algorithm. The particle position… More >

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