Lei Ling¹, Lijun Huang², Jie Wang², Li Zhang², Yue Wu², Yizhang Jiang¹, Kaijian Xia^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2353-2379, 2023, DOI:10.32604/cmes.2023.028828

Abstract In recent years, the soft subspace clustering algorithm has shown good results for high-dimensional data, which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features. The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information, which has strong results for image segmentation, but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center. However, the clustering algorithm is susceptible to the influence of noisy data and reliance on initialized clustering centers and falls into a local optimum; the clustering effect… More >

Mona Jamjoom¹, Ahmed Elhadad², Hussein Abulkasim^3,*, 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 >

Muhammad Yasir Khan¹, Muhammad Adnan^1,2, Jawaid Iqbal³, Noor ul Amin¹, Byeong-Hee Roh⁴, Jehad Ali^4,*

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 >

Sami Saeed Binyamin¹, Mahmoud Ragab^2,*

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 >

Mohammed Altaf Ahmed¹, T. Satyanarayana Murthy², Fayadh Alenezi³, E. Laxmi Lydia⁴, Seifedine Kadry^5,6,7, Yena Kim⁸, Yunyoung Nam^8,*

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 >

Xiong Xu¹, Chun Zhou^2,*, 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 >

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 >

Muhammad Aamir¹, Ziaur Rahman^1,*, 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 >

Lemei Da¹, Hai Liang^1,*, Yong Ding^1,2, 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 >

Jianfang Qi¹, Yue Li^1,3, Haibin Jin¹, Jianying Feng¹, Dong Tian¹, Weisong Mu^1,2,*

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 >