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  • Open Access

    ARTICLE

    Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation

    Mona Jamjoom1, Ahmed Elhadad2, Hussein Abulkasim3,*, Safia Abbas4

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 367-382, 2023, DOI:10.32604/cmc.2023.037310

    Abstract Several pests feed on leaves, stems, bases, and the entire plant, causing plant illnesses. As a result, it is vital to identify and eliminate the disease before causing any damage to plants. Manually detecting plant disease and treating it is pretty challenging in this period. Image processing is employed to detect plant disease since it requires much effort and an extended processing period. The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases, including Phytophthora infestans, Fusarium graminearum,… More >

  • Open Access

    ARTICLE

    A Hierarchal Clustered Based Proactive Caching in NDN-Based Vehicular Network

    Muhammad Yasir Khan1, Muhammad Adnan1,2, Jawaid Iqbal3, Noor ul Amin1, Byeong-Hee Roh4, Jehad Ali4,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1185-1208, 2023, DOI:10.32604/csse.2023.039352

    Abstract An Information-Centric Network (ICN) provides a promising paradigm for the upcoming internet architecture, which will struggle with steady growth in data and changes in access models. Various ICN architectures have been designed, including Named Data Networking (NDN), which is designed around content delivery instead of hosts. As data is the central part of the network. Therefore, NDN was developed to get rid of the dependency on IP addresses and provide content effectively. Mobility is one of the major research dimensions for this upcoming internet architecture. Some research has been carried out to solve the mobility issues, but it still has… More >

  • Open Access

    ARTICLE

    Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering Scheme for Cognitive Radio Wireless Sensor Networks

    Sami Saeed Binyamin1, Mahmoud Ragab2,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 105-119, 2023, DOI:10.32604/csse.2023.037311

    Abstract Cognitive radio wireless sensor networks (CRWSN) can be defined as a promising technology for developing bandwidth-limited applications. CRWSN is widely utilized by future Internet of Things (IoT) applications. Since a promising technology, Cognitive Radio (CR) can be modelled to alleviate the spectrum scarcity issue. Generally, CRWSN has cognitive radio-enabled sensor nodes (SNs), which are energy limited. Hierarchical cluster-related techniques for overall network management can be suitable for the scalability and stability of the network. This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering (MDMO-EAC) Scheme for CRWSN. The MDMO-EAC technique mainly intends to group the… More >

  • Open Access

    ARTICLE

    Design of Evolutionary Algorithm Based Unequal Clustering for Energy Aware Wireless Sensor Networks

    Mohammed Altaf Ahmed1, T. Satyanarayana Murthy2, Fayadh Alenezi3, E. Laxmi Lydia4, Seifedine Kadry5,6,7, Yena Kim8, Yunyoung Nam8,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1283-1297, 2023, DOI:10.32604/csse.2023.035786

    Abstract Wireless Sensor Networks (WSN) play a vital role in several real-time applications ranging from military to civilian. Despite the benefits of WSN, energy efficiency becomes a major part of the challenging issue in WSN, which necessitate proper load balancing amongst the clusters and serves a wider monitoring region. The clustering technique for WSN has several benefits: lower delay, higher energy efficiency, and collision avoidance. But clustering protocol has several challenges. In a large-scale network, cluster-based protocols mainly adapt multi-hop routing to save energy, leading to hot spot problems. A hot spot problem becomes a problem where a cluster node nearer… More >

  • Open Access

    ARTICLE

    Classification d’aires de dispersion à l’aide d’un facteur géographique

    Application à la dialectologie

    Clément Chagnaud1,3, Philippe Garat2, Paule-Annick Davoine1,3, Guylaine Brun-Trigaud4

    Revue Internationale de Géomatique, Vol.30, No.1, pp. 67-83, 2020, DOI:10.3166/rig.2020.00107

    Abstract We propose a multidimensional statistical analysis procedure using projection and classification methods, in order to identify coherent clusters into a set of surficial entities called dispersion areas. The methodology includes a geographical factor to build the representation space for the projection of the data. By applying this method on geolinguistic data, we are able to identify and explain new spatial patterns among a set of dispersion areas of linguistic features.

    RÉSUMÉ
    Nous proposons une procédure d’analyse statistique multidimensionnelle couplant des méthodes de projection et de classification pour identifier des ensembles cohérents au sein d’un corpus d’entités géographiques surfaciques que l’on… More >

  • Open Access

    ARTICLE

    Semi-Supervised Clustering Algorithm Based on Deep Feature Mapping

    Xiong Xu1, Chun Zhou2,*, Chenggang Wang1, Xiaoyan Zhang2, Hua Meng2

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 815-831, 2023, DOI:10.32604/iasc.2023.034656

    Abstract Clustering analysis is one of the main concerns in data mining. A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other. Therefore, measuring the distance between sample points is crucial to the effectiveness of clustering. Filtering features by label information and measuring the distance between samples by these features is a common supervised learning method to reconstruct distance metric. However, in many application scenarios, it is very expensive to obtain a large number of labeled samples. In this paper, to solve the clustering… More >

  • Open Access

    ARTICLE

    Index-adaptive Triangle-Based Graph Local Clustering

    Zhe Yuan*, Zhewei Wei, Ji-rong Wen

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5009-5026, 2023, DOI:10.32604/cmc.2023.038531

    Abstract Motif-based graph local clustering (MGLC) algorithms are generally designed with the two-phase framework, which gets the motif weight for each edge beforehand and then conducts the local clustering algorithm on the weighted graph to output the result. Despite correctness, this framework brings limitations on both practical and theoretical aspects and is less applicable in real interactive situations. This research develops a purely local and index-adaptive method, Index-adaptive Triangle-based Graph Local Clustering (TGLC+), to solve the MGLC problem w.r.t. triangle. TGLC+ combines the approximated Monte-Carlo method Triangle-based Random Walk (TRW) and deterministic Brute-Force method Triangle-based Forward Push (TFP) adaptively to estimate… More >

  • Open Access

    ARTICLE

    A Progressive Approach to Generic Object Detection: A Two-Stage Framework for Image Recognition

    Muhammad Aamir1, Ziaur Rahman1,*, Waheed Ahmed Abro2, Uzair Aslam Bhatti3, Zaheer Ahmed Dayo1, Muhammad Ishfaq1

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6351-6373, 2023, DOI:10.32604/cmc.2023.038173

    Abstract Object detection in images has been identified as a critical area of research in computer vision image processing. Research has developed several novel methods for determining an object’s location and category from an image. However, there is still room for improvement in terms of detection efficiency. This study aims to develop a technique for detecting objects in images. To enhance overall detection performance, we considered object detection a two-fold problem, including localization and classification. The proposed method generates class-independent, high-quality, and precise proposals using an agglomerative clustering technique. We then combine these proposals with the relevant input image to train… More >

  • Open Access

    ARTICLE

    Blockchain-Based Data Acquisition with Privacy Protection in UAV Cluster Network

    Lemei Da1, Hai Liang1,*, Yong Ding1,2, Yujue Wang1, Changsong Yang1, Huiyong Wang3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 879-902, 2023, DOI:10.32604/cmes.2023.026309

    Abstract The unmanned aerial vehicle (UAV) self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale, which can quickly and accurately complete complex tasks such as path planning, situational awareness, and information transmission. Due to the openness of the network, the UAV cluster is more vulnerable to passive eavesdropping, active interference, and other attacks, which makes the system face serious security threats. This paper proposes a Blockchain-Based Data Acquisition (BDA) scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario. Each UAV cluster has an… More >

  • Open Access

    ARTICLE

    An Adaptive Parameter-Free Optimal Number of Market Segments Estimation Algorithm Based on a New Internal Validity Index

    Jianfang Qi1, Yue Li1,3, Haibin Jin1, Jianying Feng1, Dong Tian1, Weisong Mu1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 197-232, 2023, DOI:10.32604/cmes.2023.026113

    Abstract An appropriate optimal number of market segments (ONS) estimation is essential for an enterprise to achieve successful market segmentation, but at present, there is a serious lack of attention to this issue in market segmentation. In our study, an independent adaptive ONS estimation method BWCON-NSDK-means++ is proposed by integrating a new internal validity index (IVI) Between-Within-Connectivity (BWCON) and a new stable clustering algorithm Natural-SDK-means++ (NSDK-means++) in a novel way. First, to complete the evaluation dimensions of the existing IVIs, we designed a connectivity formula based on the neighbor relationship and proposed the BWCON by integrating the connectivity with other two… More >

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