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Search Results (19)
  • Open Access


    Hyperspectral Image Based Interpretable Feature Clustering Algorithm

    Yaming Kang1,*, Peishun Ye1, Yuxiu Bai1, Shi Qiu2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2151-2168, 2024, DOI:10.32604/cmc.2024.049360

    Abstract Hyperspectral imagery encompasses spectral and spatial dimensions, reflecting the material properties of objects. Its application proves crucial in search and rescue, concealed target identification, and crop growth analysis. Clustering is an important method of hyperspectral analysis. The vast data volume of hyperspectral imagery, coupled with redundant information, poses significant challenges in swiftly and accurately extracting features for subsequent analysis. The current hyperspectral feature clustering methods, which are mostly studied from space or spectrum, do not have strong interpretability, resulting in poor comprehensibility of the algorithm. So, this research introduces a feature clustering algorithm for hyperspectral… More >

  • Open Access


    Optical Fibre Communication Feature Analysis and Small Sample Fault Diagnosis Based on VMD-FE and Fuzzy Clustering

    Xiangqun Li1,*, Jiawen Liang2, Jinyu Zhu2, Shengping Shi2, Fangyu Ding2, Jianpeng Sun2, Bo Liu2

    Energy Engineering, Vol.121, No.1, pp. 203-219, 2024, DOI:10.32604/ee.2023.029295

    Abstract To solve the problems of a few optical fibre line fault samples and the inefficiency of manual communication optical fibre fault diagnosis, this paper proposes a communication optical fibre fault diagnosis model based on variational modal decomposition (VMD), fuzzy entropy (FE) and fuzzy clustering (FC). Firstly, based on the OTDR curve data collected in the field, VMD is used to extract the different modal components (IMF) of the original signal and calculate the fuzzy entropy (FE) values of different components to characterize the subtle differences between them. The fuzzy entropy of each curve is used More >

  • Open Access


    Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data

    Pham Huy Thong1,2,3, Florentin Smarandache4, Phung The Huan5, Tran Manh Tuan6, Tran Thi Ngan6,*, Vu Duc Thai5, Nguyen Long Giang2, Le Hoang Son3

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1981-1997, 2023, DOI:10.32604/csse.2023.035692

    Abstract Clustering is a crucial method for deciphering data structure and producing new information. Due to its significance in revealing fundamental connections between the human brain and events, it is essential to utilize clustering for cognitive research. Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties. Noisy data can lead to incorrect object recognition and inference. This research aims to innovate a novel clustering approach, named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering (PNTS3FCM), to solve the clustering problem with noisy data… More >

  • Open Access


    Energy-Efficient Routing Protocol with Multi-Hop Fuzzy Logic for Wireless Networks

    J. Gobinath1,*, S. Hemajothi2, J. S. Leena Jasmine3

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2457-2471, 2023, DOI:10.32604/iasc.2023.031171

    Abstract A Wireless Sensor Network (WSN) becomes a newer type of real-time embedded device that can be utilized for a wide range of applications that make regular networking which appears impracticable. Concerning the energy production of the nodes, WSN has major issues that may influence the stability of the system. As a result, constructing WSN requires devising protocols and standards that make the most use of constrained capacity, especially the energy resources. WSN faces some issues with increased power utilization and an on going development due to the uneven energy usage between the nodes. Clustering has… More >

  • Open Access


    Deep Fake Detection Using Computer Vision-Based Deep Neural Network with Pairwise Learning

    R. Saravana Ram1, M. Vinoth Kumar2, Tareq M. Al-shami3, Mehedi Masud4, Hanan Aljuaid5, Mohamed Abouhawwash6,7,*

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2449-2462, 2023, DOI:10.32604/iasc.2023.030486

    Abstract Deep learning-based approaches are applied successfully in many fields such as deepFake identification, big data analysis, voice recognition, and image recognition. Deepfake is the combination of deep learning in fake creation, which states creating a fake image or video with the help of artificial intelligence for political abuse, spreading false information, and pornography. The artificial intelligence technique has a wide demand, increasing the problems related to privacy, security, and ethics. This paper has analyzed the features related to the computer vision of digital content to determine its integrity. This method has checked the computer vision More >

  • Open Access


    gscaLCA in R: Fitting Fuzzy Clustering Analysis Incorporated with Generalized Structured Component Analysis

    Ji Hoon Ryoo1,*, Seohee Park2, Seongeun Kim3, Heungsun Hwang4

    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 801-822, 2022, DOI:10.32604/cmes.2022.019708

    Abstract Clustering analysis identifying unknown heterogenous subgroups of a population (or a sample) has become increasingly popular along with the popularity of machine learning techniques. Although there are many software packages running clustering analysis, there is a lack of packages conducting clustering analysis within a structural equation modeling framework. The package, gscaLCA which is implemented in the R statistical computing environment, was developed for conducting clustering analysis and has been extended to a latent variable modeling. More specifically, by applying both fuzzy clustering (FC) algorithm and generalized structured component analysis (GSCA), the package gscaLCA computes membership prevalence and… More >

  • Open Access


    Object Detection in Remote Sensing Images Using Picture Fuzzy Clustering and MapReduce

    Tran Manh Tuan*, Tran Thi Ngan, Nguyen Tu Trung

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1241-1253, 2022, DOI:10.32604/csse.2022.024265

    Abstract In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order to perform next steps in image processing. Remote sensing images usually have large size and various spatial resolutions. Thus, detecting objects in remote sensing images is very complicated. In this paper, we develop a model to detect objects in remote sensing images based on the combination of picture fuzzy clustering and MapReduce method (denoted as MPFC). Firstly, picture fuzzy clustering is applied to segment the input images. Then, MapReduce is… More >

  • Open Access


    Internal Validity Index for Fuzzy Clustering Based on Relative Uncertainty

    Refik Tanju Sirmen1,*, Burak Berk Üstündağ2

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2909-2926, 2022, DOI:10.32604/cmc.2022.023947

    Abstract Unsupervised clustering and clustering validity are used as essential instruments of data analytics. Despite clustering being realized under uncertainty, validity indices do not deliver any quantitative evaluation of the uncertainties in the suggested partitionings. Also, validity measures may be biased towards the underlying clustering method. Moreover, neglecting a confidence requirement may result in over-partitioning. In the absence of an error estimate or a confidence parameter, probable clustering errors are forwarded to the later stages of the system. Whereas, having an uncertainty margin of the projected labeling can be very fruitful for many applications such as… More >

  • Open Access


    MRI Brain Tumor Segmentation with Intuitionist Possibilistic Fuzzy Clustering and Morphological Operations

    J. Anitha*, M. Kalaiarasu

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 363-379, 2022, DOI:10.32604/csse.2022.022402

    Abstract Digital Image Processing (DIP) is a well-developed field in the biological sciences which involves classification and detection of tumour. In medical science, automatic brain tumor diagnosis is an important phase. Brain tumor detection is performed by Computer-Aided Diagnosis (CAD) systems. The human image creation is greatly achieved by an approach namely medical imaging which is exploited for medical and research purposes. Recently Automatic brain tumor detection from MRI images has become the emerging research area of medical research. Brain tumor diagnosis mainly performed for obtaining exact location, orientation and area of abnormal tissues. Cancer and… More >

  • Open Access


    Research on Power Consumption Anomaly Detection Based on Fuzzy Clustering and Trend Judgment

    Wei Xiong1,2, Xianshan Li1,2,*, Yu Zou3, Shiwei Su1,2, Li Zhi1,2

    Energy Engineering, Vol.119, No.2, pp. 755-765, 2022, DOI:10.32604/ee.2022.018009

    Abstract Among the end-users of the power grid, especially in the rural power grid, there are a large number of users and the situation is complex. In this complex situation, there are more leakage caused by insulation damage and a small number of users stealing electricity. Maintenance staff will take a long time to determine the location of the abnormal user meter box. In view of this situation, the method of subjective fuzzy clustering and quartile difference is adopted to determine the partition threshold. The power consumption data of end-users are divided into three regions: high, More >

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