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  • Open Access


    Adaptive Density-Based Spatial Clustering of Applications with Noise (ADBSCAN) for Clusters of Different Densities

    Ahmed Fahim1,2,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3695-3712, 2023, DOI:10.32604/cmc.2023.036820

    Abstract Finding clusters based on density represents a significant class of clustering algorithms. These methods can discover clusters of various shapes and sizes. The most studied algorithm in this class is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects. It requires two input parameters: epsilon (fixed neighborhood radius) and MinPts (the lowest number of objects in epsilon). However, it can’t handle clusters of various densities since it uses a global value for epsilon. This article proposes an adaptation of the DBSCAN method so… More >

  • Open Access


    Optimal Routing with Spatial-Temporal Dependencies for Traffic Flow Control in Intelligent Transportation Systems

    R. B. Sarooraj*, S. Prayla Shyry

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2071-2084, 2023, DOI:10.32604/iasc.2023.034716

    Abstract In Intelligent Transportation Systems (ITS), controlling the traffic flow of a region in a city is the major challenge. Particularly, allocation of the traffic-free route to the taxi drivers during peak hours is one of the challenges to control the traffic flow. So, in this paper, the route between the taxi driver and pickup location or hotspot with the spatial-temporal dependencies is optimized. Initially, the hotspots in a region are clustered using the density-based spatial clustering of applications with noise (DBSCAN) algorithm to find the hot spots at the peak hours in an urban area. Then, the optimal route is… More >

  • Open Access


    Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

    Byoungwook Kim1, Hong-Jun Jang2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1275-1294, 2023, DOI:10.32604/cmes.2022.022446

    Abstract As location information of numerous Internet of Thing (IoT) devices can be recognized through IoT sensor technology, the need for technology to efficiently analyze spatial data is increasing. One of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical data. In this paper, we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN algorithm. The existing DBSCAN algorithm has a problem in that… More > Graphic Abstract

    Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

  • Open Access


    Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

    R. Krishnaswamy1, Kamalraj Subramaniam2, V. Nandini3, K. Vijayalakshmi4, Seifedine Kadry5, Yunyoung Nam6,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 391-406, 2023, DOI:10.32604/csse.2023.024901

    Abstract Recently, a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two Exabytes. At the same time, clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in it. Density-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial dataset. Big data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map… More >

  • Open Access


    Vulnerability of Regional Aviation Networks Based on DBSCAN and Complex Networks

    Hang He1,*, Wanggen Liu1, Zhenhan Zhao1, Shan He1, Jinghui Zhang2

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 643-655, 2022, DOI:10.32604/csse.2022.027211

    Abstract To enhance the accuracy of performance analysis of regional airline network, this study applies complex network theory and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to investigate the topology of regional airline network, constructs node importance index system, and clusters 161 airport nodes of regional airline network. Besides, entropy power method and approximating ideal solution method (TOPSIS) is applied to comprehensively evaluate the importance of airport nodes and complete the classification of nodes and identification of key points; adopt network efficiency, maximum connectivity subgraph and network connectivity as vulnerability measurement indexes, and observe the changes of vulnerability indexes… More >

  • Open Access


    A Machine Learning Approach for Early COVID-19 Symptoms Identification

    Omer Ali1,2, Mohamad Khairi Ishak1,*, Muhammad Kamran Liaquat Bhatti2

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3803-3820, 2022, DOI:10.32604/cmc.2022.019797

    Abstract Symptom identification and early detection are the first steps towards a health condition diagnosis. The COVID-19 virus causes pneumonia-like symptoms such as fever, cough, and shortness of breath. Many COVID-19 contraction tests necessitate extensive clinical protocols in medical settings. Clinical studies help with the accurate analysis of COVID-19, where the virus has already spread to the lungs in most patients. The majority of existing supervised machine learning-based disease detection techniques are based on clinical data like x-rays and computerized tomography. This is heavily reliant on a larger clinical study and does not emphasize early symptom detection. The aim of this… More >

  • Open Access


    Enrichment of Crop Yield Prophecy Using Machine Learning Algorithms

    R. Kingsy Grace*, K. Induja, M. Lincy

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 279-296, 2022, DOI:10.32604/iasc.2022.019947

    Abstract Strong associations exist between the crop productivity and the seasonal, biological, economical causes in natural ecosystems. The linkages like climatic conditions, health of a soil, growth of crop, irrigation, fertilizers, temperature, rainwater, pesticides desired to be preserved in comprehensively managed crop lands which impacts the crop potency. Crop yield prognosis plays a vibrant part in agricultural planning, administration and environs sustainability. Advancements in the field of Machine Learning have perceived novel expectations to improve the prediction performance in Agriculture. Highly gratifying prediction of crop yield helps the majority of agronomists for their rapid decision-making in the choice of crop to… More >

  • Open Access


    Safest Route Detection via Danger Index Calculation and K-Means Clustering

    Isha Puthige1, Kartikay Bansal1, Chahat Bindra1, Mahekk Kapur1, Dilbag Singh1, Vipul Kumar Mishra1, Apeksha Aggarwal1, Jinhee Lee2, Byeong-Gwon Kang2, Yunyoung Nam2,*, Reham R. Mostafa3

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2761-2777, 2021, DOI:10.32604/cmc.2021.018128

    Abstract The study aims to formulate a solution for identifying the safest route between any two inputted Geographical locations. Using the New York City dataset, which provides us with location tagged crime statistics; we are implementing different clustering algorithms and analysed the results comparatively to discover the best-suited one. The results unveil the fact that the K-Means algorithm best suits for our needs and delivered the best results. Moreover, a comparative analysis has been performed among various clustering techniques to obtain best results. we compared all the achieved results and using the conclusions we have developed a user-friendly application to provide… More >

  • Open Access


    Power Data Preprocessing Method of Mountain Wind Farm Based on POT-DBSCAN

    Anfeng Zhu, Zhao Xiao, Qiancheng Zhao*

    Energy Engineering, Vol.118, No.3, pp. 549-563, 2021, DOI:10.32604/EE.2021.014177

    Abstract Due to the frequent changes of wind speed and wind direction, the accuracy of wind turbine (WT) power prediction using traditional data preprocessing method is low. This paper proposes a data preprocessing method which combines POT with DBSCAN (POT-DBSCAN) to improve the prediction efficiency of wind power prediction model. Firstly, according to the data of WT in the normal operation condition, the power prediction model of WT is established based on the Particle Swarm Optimization (PSO) Arithmetic which is combined with the BP Neural Network (PSO-BP). Secondly, the wind-power data obtained from the supervisory control and data acquisition (SCADA) system… More >

  • Open Access


    Lithium-Ion Battery Screening by K-Means with DBSCAN for Denoising

    Yudong Wang1, 2, Jie Tan1, *, Zhenjie Liu1, Allah Ditta3

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2111-2122, 2020, DOI:10.32604/cmc.2020.011098

    Abstract Batteries are often packed together to meet voltage and capability needs. However, due to variations in raw materials, different ages of equipment, and manual operation, there is inconsistency between batteries, which leads to reduced available capacity, variability of resistance, and premature failure. Therefore, it is crucial to pack similar batteries together. The conventional approach to screening batteries is based on their capacity, voltage and internal resistance, which disregards how batteries perform during manufacturing. In the battery discharge process, real time discharge voltage curves (DVCs) are collected as a set of unlabeled time series, which reflect how the battery voltage changes.… More >

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