Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (40)
  • Open Access

    ARTICLE

    Design of Clustering Techniques in Cognitive Radio Sensor Networks

    R. Ganesh Babu1,*, D. Hemanand2, V. Amudha3, S. Sugumaran4

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 441-456, 2023, DOI:10.32604/csse.2023.024049 - 01 June 2022

    Abstract In recent decades, several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during transmission to a shorter distance while restricting the Primary Users (PUs) interference. The Cognitive Radio (CR) system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm (ASDIC) that shows better spectrum sensing among group of multiusers in terms of sensing error, power saving, and convergence time. In this research paper, the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity.… More >

  • Open Access

    ARTICLE

    Improving Method of Anomaly Detection Performance for Industrial IoT Environment

    Junwon Kim1, Jiho Shin2, Ki-Woong Park3, Jung Taek Seo4,*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5377-5394, 2022, DOI:10.32604/cmc.2022.026619 - 21 April 2022

    Abstract Industrial Control System (ICS), which is based on Industrial IoT (IIoT), has an intelligent mobile environment that supports various mobility, but there is a limit to relying only on the physical security of the ICS environment. Due to various threat factors that can disrupt the workflow of the IIoT, machine learning-based anomaly detection technologies are being presented; it is also essential to study for increasing detection performance to minimize model errors for promoting stable ICS operation. In this paper, we established the requirements for improving the anomaly detection performance in the IIoT-based ICS environment by… More >

  • Open Access

    ARTICLE

    Analyzing the Urban Hierarchical Structure Based on Multiple Indicators of Economy and Industry: An Econometric Study in China

    Jing Cheng1, Yang Xie2, Jie Zhang1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1831-1855, 2022, DOI:10.32604/cmes.2022.020178 - 19 April 2022

    Abstract For a city, analyzing its advantages, disadvantages and the level of economic development in a country is important, especially for the cities in China developing at flying speed. The corresponding literatures for the cities in China have not considered the indicators of economy and industry in detail. In this paper, based on multiple indicators of economy and industry, the urban hierarchical structure of 285 cities above the prefecture level in China is investigated. The indicators from the economy, industry, infrastructure, medical care, population, education, culture, and employment levels are selected to establish a new indicator… More >

  • Open Access

    ARTICLE

    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 - 23 March 2022

    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… More >

  • Open Access

    ARTICLE

    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 - 08 February 2022

    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… More >

  • Open Access

    ARTICLE

    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 - 09 December 2021

    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 More >

  • Open Access

    ARTICLE

    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 - 26 October 2021

    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, More >

  • Open Access

    ARTICLE

    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 - 11 August 2021

    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 More >

  • Open Access

    ARTICLE

    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 - 26 July 2021

    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… More >

  • Open Access

    ARTICLE

    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 - 28 December 2020

    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… More >

Displaying 21-30 on page 3 of 40. Per Page