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25 results for “FCM”
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Research ArticleOpen accessEvery word in the titleIncorporation of Modified Region Growing into FCM Clustering for Extraction of Tumors in MR ImagesLovepreet Singh Brar, Jaget Singh, Bhawana Agrawal et al.Due to the tremendous growth of medical Magnetic Resonance (MR) images, it becomes an essential requirement for an automated extraction of diagnostically salient parts of the images to refine the transmission and storage process. Research method: This paper proposes an automated unsupervised machine learning segmentation technique named fuzzy c-means clustering based on modified region growing algorithm (RG-FCM) for the extraction … …Journal of Computer Science, Vol. 21, Issue 12 · 12 Feb 2026 · 1,477 accessesView PDFCite
Research ArticleOpen accessIn the keywordsDetecting Anomalies in IoT Using Intuitionistic Fuzzy Clustering AlgorithmsVijo Arul Selvi M., Fehmin Nadira Laskar, Fokrul Alom Mazarbhuiya et al.Intuitionistic Fuzzy Sets (IFS) are convenient ways to represent the vagueness and uncertainty inherent in any dataset. There are many uses of these. One such use is the Fuzzy C-Means (FCM) algorithm, which clusters the data objects into a pre-assigned (c) number of fuzzy clusters, which has been adopted in many fields, namely, pattern classification, anomaly detection, fraud identification, etc. … …Journal of Computer Science, Vol. 22, Issue 2 · 2 Mar 2026 · 1,600 accessesView PDFCite
Research ArticleOpen accessIn the keywordsSwarm Optimization Techniques for Segmenting Gel Electrophoresis ImagesSara Ibrahim Ibrahim, Mohamed Abd Allah Makhlouf, Ghada.S. El-Tawel, M.E. WahedGel Electrophoresis (GE) are discussed as the main tool to dissociate DNA sequences. It helps in analyzing the genome such that each image resulting from it consists of lanes that include several bands. Image segmentation plays the foremost role in image processing. It helps in producing accurate results in medical diagnosis. Image segmentation works by dividing an image into regions … …Current Research in Bioinformatics, Vol. 5, Issue 1 · 18 Jun 2016 · 8,092 accessesView PDFCite
Research ArticleOpen accessIn the keywordsEffect of Clustering in Designing a Fuzzy Based Hybrid Intrusion Detection System for Mobile Ad Hoc NetworksD. Vydeki, R. S. BhuvaneswaranIntrusion Detection System (IDS) provides additional security for the most vulnerable Mobile Adhoc Networks (MANET). Use of Fuzzy Inference System (FIS) in the design of IDS is proven to be efficient in detecting routing attacks in MANETs. Clustering is a vital means in the detection process of FIS based hybrid IDS. This study describes the design of such a system … …Journal of Computer Science, Vol. 9, Issue 4 · 14 May 2013 · 6,469 accessesView PDFCite
Research ArticleOpen accessIn the keywordsArtificial Bee Colony Algorithm Integrated with Fuzzy C-mean Operator for Data ClusteringM. Krishnamoorthi, A. M. NatarajanClustering task aims at the unsupervised classification of patterns in different groups. To enhance the quality of results, the emerging swarm-based algorithms now-a-days become an alternative to the conventional clustering methods. In this study, an optimization method based on the swarm intelligence algorithm is proposed for the purpose of clustering. The significance of the proposed algorithm is that it uses … …Journal of Computer Science, Vol. 9, Issue 4 · 6 May 2013 · 6,299 accessesView PDFCite
Research ArticleOpen accessIn the keywordsSelf Organizing Map Cluster Approach for Wavelet Based Medical Image CompressionS. Sridevi, V. R. Vijayakumar, V. Sutha JebaKumariDigital medical powerful tools for diagnosis, treatment and surgery and plays a vital role in modern healthcare delivery. Large storage capacity is needed for storing these images and for transmitting them. This leads to the strong demand for digital medical image compression and reliable transmission. In this study, we have applied three compression methods to medical images. In all the … …American Journal of Engineering and Applied Sciences, Vol. 5, Issue 4 · 2 Jan 2013 · 7,422 accessesView PDFCite
Research ArticleOpen accessIn the keywordsDermoscopic Image Segmentation using Machine Learning AlgorithmL. Padma Suresh, K.L. Shunmuganathan, S.H. Krishna VeniProblem statement: Malignant melanoma is the most frequent type of skin cancer. Its incidence has been rapidly increasing over the last few decades. Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Approach: This study explains the task of segmenting skin lesions in … …American Journal of Applied Sciences, Vol. 8, Issue 11 · 7 Oct 2011 · 7,342 accessesView PDFCite
Research ArticleOpen accessIn the keywordsSurface Electromyography-Based Facial Expression Recognition in Bi-Polar ConfigurationMahyar Hamedi, Sh-Hussain Salleh, Tan Tian Swee, KamarulafizamProblem statement: Facial expression recognition has been improved recently and it has become a significant issue in diagnostic and medical fields, particularly in the areas of assistive technology and rehabilitation. Apart from their usefulness, there are some problems in their applications like peripheral conditions, lightening, contrast and quality of video and images. Approach: Facial Action Coding System (FACS) and some … …Journal of Computer Science, Vol. 7, Issue 9 · 25 Jul 2011 · 9,943 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAn Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy TechniqueS. Murugavalli, V. RajamaniImplementation of a neuro-fuzzy segmentation process of the MRI data is presented in this study to detect various tissues like white matter, gray matter, csf and tumor. The advantage of hierarchical self organizing map and fuzzy c means algorithms are used to classify the image layer by layer. The lowest level weight vector is achieved by the abstraction level. We … …Journal of Computer Science, Vol. 3, Issue 11 · 30 Nov 2007 · 7,107 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Fuzzy Clustering Based Method for the Spatio-Temporal Data AnalysisAnahita Zolghadr, Afsaneh JalalianSpatiotemporal data is a type of data that is collected by the sensors. This type of data has two spatial and temporal dimensions. There are many challenges in analyzing spatiotemporal big data. Common evaluation metrics of clustering methods are not appropriate for spatiotemporal data. Previous clustering methods and the conventional evaluation metrics are efficient for data like time series with … …Journal of Computer Science, Vol. 18, Issue 6 · 13 Jun 2022 · 5,638 accessesView PDFCite