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19 results for “K-Mean Clustering”
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Research ArticleOpen accessIn the abstractA HYBRID FIREFLY ALGORITHM WITH FUZZY-C MEAN ALGORITHM FOR MRI BRAIN SEGMENTATIONMutasem K. AlsmadiImage processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI). A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is … …American Journal of Applied Sciences, Vol. 11, Issue 9 · 10 Sep 2014 · 8,074 accessesView PDFCite
Research ArticleOpen accessIn the abstractCOMPARATIVE STUDY OF K-MEANS AND K-MEANS++ CLUSTERING ALGORITHMS ON CRIME DOMAINBashar Aubaidan, Masnizah Mohd, Mohammed AlbaredThis study presents the results of an experimental study of two document clustering techniques which are k-means and k-means++. In particular, we compare the two main approaches in crime document clustering. The drawback of k-means is that the user needs to define the centroid point. This becomes more critical when dealing with document clustering because each center point represented by … …Journal of Computer Science, Vol. 10, Issue 7 · 19 Feb 2014 · 7,762 accessesView PDFCite
Research ArticleOpen accessIn the abstractDermoscopic 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 abstractA Computer Aided Diagnosis System for Lung Cancer Detection\Using Support Vector MachineM. Gomathi, P. ThangarajProblem statement: Computer Tomography (CT) has been considered as the most sensitive imaging technique for early detection of lung cancer. Approach: On the other hand, there is a requirement for automated methodology to make use of large amount of data obtained CT images. Computer Aided Diagnosis (CAD) can be used efficiently for early detection of Lung Cancer. Results: The usage … …American Journal of Applied Sciences, Vol. 7, Issue 12 · 31 Dec 2010 · 8,285 accessesView PDFCite
Research ArticleOpen accessIn the abstractMemory Convergence and Optimization with Fuzzy PSO and ACSSubhash C. Pandey, Dr. P. MisraAssociative neural memories are models of biological phenomena that allow for the storage of pattern associations and the retrieval of the desired output pattern upon presentation of a possibly noisy or incomplete version of an input pattern. In this study, we introduce fuzzy swarm particle optimization technique for convergence of associative neural memories based on fuzzy set theory. A Fuzzy … …Journal of Computer Science, Vol. 4, Issue 2 · 28 Feb 2008 · 7,169 accessesView PDFCite
Research ArticleOpen accessIn the abstractAssociation of Anthropometric and Metabolic Variables with Cardiovascular Disease among Urban and Rural OriginBadaruddoza, Kushal Gill, Raman KumarProblem statement: We have examined the prevalence and association of cardiovascular diseases with respect to obesity and metabolic risk factors clustering among urban and rural Punjabi males aged 20-55 years. Obesity has been defined by increased Waist Circumference (WC), Body Mass Index (BMI) and Waist Hip Ratio (WHR). Metabolic traits such as increased total cholesterol, triglycerides and lipoproteins have also … …American Journal of Applied Sciences, Vol. 8, Issue 10 · 20 Aug 2011 · 7,468 accessesView PDFCite
Research ArticleOpen accessIn the abstractAre There Smaller Leverage Effects in Less-Developed Markets? Evidence from an Oil Exporting CountryMosayeb Phalavani, Parinaz EzzatiProblem statement: This study uses daily data from the Tehran Stock Market (TSM) to illustrate the nature of stock market volatility in an undeveloped and young stock market. Although most studies suggest that a negative shock to stock prices will generate more volatility than a positive shock of equal magnitude but there is no evidence of asymmetric effect in TSM. … …American Journal of Applied Sciences, Vol. 7, Issue 1 · 31 Jan 2010 · 7,519 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordAnchoring Public Health Forecasts: High-Precision Obesity Prevalence Estimation Via Hybrid Geodemographic Ensemble RegressionTrenton Ward, Isaac OsunmakindeObesity remains a major driver of chronic disease and rising healthcare costs in the United States. Yet, most existing predictive models rely on single-model classifiers and rarely incorporate spatial structure or ensemble-based regression, thereby limiting their ability to capture geodemographic variation. This study introduces a hybrid unsupervised–supervised ensemble framework that integrates K-Means spatial profiling with a Stacking Regressor to address … …Journal of Computer Science, Vol. 22, Issue 7 · 31 Jul 2026 · 632 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordSwarm 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