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42 results for “fuzzy c-means”
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Research ArticleOpen accessIn the abstractHybrid Fog Resource Provisioning and Resource Allocation using MKFCM and FPANagarjun E, Dharamendra Chouhan, Dilip Kumar SMThe expansion of Internet of Things (IoT) deployments has motivated the Fog Computing (FC) paradigm, which brings mobile-edge and cloud resources together under a single model. Allocating and provisioning application tasks across such environments poses challenges due to constrained resource capabilities, heterogeneity, mobility, network hierarchy, and unpredictability. This paper addresses the need for additional computing devices that prioritize delay-sensitive applications … …Journal of Computer Science, Vol. 22, Issue 10 · 6 Oct 2026 · 462 accessesView PDFCite
Research ArticleOpen accessIn the abstractPolar Disentangled Non-Local Convolutional Neural Network for Skin Cancer Classification Using Dermoscopy ImagesUsha Thirugnanam, Nalini JosephSkin cancer classification is the process of identifying and categorizing various types of skin lesions into cancerous and non-cancerous. This process is performed to achieve accurate detection, which minimizes the risk of severe health complications. However, classifying the severity of skin lesion images is a difficult task because of subtle and overlapping visual patterns, which could lead to inaccurate performance. … …Journal of Computer Science, Vol. 22, Issue 6 · 16 Jun 2026 · 1,207 accessesView PDFCite
Research ArticleOpen accessIn the abstractA 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
EditorialOpen accessIn the abstractNew Kind of Statistical MethodsCagdas Hakan AladagAladag, C.H. and E. Egrioglu, 2012. Advanced Time Series Forecasting Methods. In: Advances in Time Series Forecasting, Aladag, C.H. and E. Egrioglu (Eds.), Bentham Science Publishers, Oak Park, ISBN-10: 1608053733. Aladag, C.H., 2011. A Hybrid Intelligent Technique Combines Neural Networks and Tabu Search Methods for Forecasting, Computer Search Algorithms. 1st Edn., Nova Publisher, ISBN: 978-1-61209-043-6. Aladag, C.H., E Egrioglu, U. … …Journal of Mathematics and Statistics, Vol. 10, Issue 4 · 28 Nov 2014 · 7,588 accessesView PDFCite
Research ArticleOpen accessIn the abstractFUZZY MODEL OPTIMIZATION FOR TIME SERIES DATA USING A TRANSLATION IN THE EXTENT OF MEAN ERRORNurhayadi, Subanar, Abdurakhman, Agus Maman AbadiRecently, many researchers in the field of writing about the prediction of stock price forecasting, electricity load demand and academic enrollment using fuzzy methods. However, in general, modeling does not consider the model position to actual data yet where it means that error is not been handled optimally. The error that is not managed well can reduce the accuracy of … …Journal of Mathematics and Statistics, Vol. 10, Issue 2 · 2 Jun 2014 · 6,733 accessesView PDFCite
Research ArticleOpen accessIn the abstractA NOVEL APPROACH BASED ON GENETIC FUZZY CLUSTERING AND ADAPTIVE NEURAL NETWORKS FOR SALES FORECASTINGAttariuas Hichama, Bouhorma Mohameda, El Fallahi AbdellahbThis article proposes a new hybrid sales forecasting system based on genetic fuzzy clustering and Back-Propagation (BP) Neural Networks with adaptive learning rate (GFCBPN).The proposed architecture consists of three stages: (1) utilizing Winter’s Exponential Smoothing method and Fuzzy C-Means clustering, all normalized data records will be categorized into k clusters; (2) using an adapted Genetic Fuzzy System (MCGFS), the fuzzy … …Journal of Computer Science, Vol. 9, Issue 8 · 2 Jul 2013 · 6,669 accessesView PDFCite
Research ArticleOpen accessIn the abstractEffect 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 abstractArtificial 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 abstractFetal Electrocardiogram Extraction Using Let TechniquesS. Hemajothi, K. Helen PrabhaFetal Electrocardiogram Extraction (FECG) identifies the congenital heart problems at the earlier stage. The major problem in the non invasive procedure is the extraction of FECG from Maternal ECG (MECG) and many interferences. The proposed methods (i) Combination of Adaptive Neuro Fuzzy Inference (ANFIS) and Fractional spline wavelet (ii) Combination of Fractional spline wavelet and ANFIS (iii) Combination of ANFIS … …Journal of Computer Science, Vol. 8, Issue 9 · 16 Aug 2012 · 7,311 accessesView PDFCite
Research ArticleOpen accessIn the abstractNovel Fuzzy Technique for Cancer Detection in Noisy Breast Ultrasound ImagesN. Alamelumangai, J. DeviShreeProblem statement: Detecting tumor areas in breast Ultrasound (US) images is a challenging task. The occurrence of benign areas in breast may result in false identification of malignant areas which may have serious outcome. Approach: The CAD system could act as a major function in the early detection of breast cancer and could decrease the death rate among women with … …American Journal of Applied Sciences, Vol. 9, Issue 5 · 12 Mar 2012 · 6,959 accessesView PDFCite