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83 results for “K-means Clustering”
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Research ArticleOpen accessIn the keywordsNon-Decimated Wavelet Transform and Vector Quantization for Lossy Medical Images CompressionHend A. Elsayed, Qusay E. Majeed, Mohammed M. El SherbinyThis study presents a new approach for lossy medical image compression using vector quantization. Recently, the digital image has been a reliable replacement for a hard copy of medical images, therefore, an effort has been made to ensure maintaining high-quality images to use for archiving, classification, or automated diagnostics support. Although the medical application contains all sorts of the images … …Journal of Computer Science, Vol. 19, Issue 3 · 24 Feb 2023 · 5,403 accessesView PDFCite
Research ArticleOpen accessIn the keywordsHigh Accurate Multicriteria Cluster-Based Collaborative Filtering Recommender SystemZhila Yaseen Taha, Sadegh Abdollah AminifarA Recommender System (RS) is one of the appropriate answers by researchers for alleviating the problem of customers' overload with information from an internet source. In RS, users' evaluation of the items assists in quickly determining the user's preferences and removing the choosing overhead for the user the next time they search. However, using user ratings for an item based … …Journal of Computer Science, Vol. 18, Issue 12 · 10 Dec 2022 · 5,914 accessesView PDFCite
Review ArticleOpen accessIn the keywordsEnhancement of GPS Position Accuracy Using Machine Vision and Deep Learning TechniquesAshwani Kumar AggarwalThe accuracy of GPS position estimation in urban cities is an issue which need to be resolved using machine vision and deep learning techniques. The accuracy of GPS in horizontal direction is better than in the vertical direction. Although for most of the navigation applications in intelligent transportation systems, horizontal positioning accuracy is vital, but vertical position accuracy gives idea … …Journal of Computer Science, Vol. 16, Issue 5 · 25 May 2020 · 18,669 accessesView PDFCite
Research ArticleOpen accessIn the keywordsClassification of Breast Tumor in Mammogram Images Using Unsupervised Feature LearningAidarus M. Ibrahim, Baharum Baharudin, Abas Md Said, P. N. HashimahIn this study, we propose a learning-based approach using feature learning to minimize the manual effort required to extract features. Firstly, we extracted features from equally spaced sub-patches covering the input Region of Interest (ROI). The dimensionality of the extracted features is reduced using max-pooling. Furthermore, spherical k-means clustering coupled with max pooling (k-means-max pooling) is compared with well-known feature … …American Journal of Applied Sciences, Vol. 13, Issue 5 · 13 May 2016 · 7,497 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA 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 keywordsHybrid Ant-Based Clustering Algorithm with Cluster Analysis TechniquesWafa’a Omar, Amr Badr, Abd El-Fattah HegazyCluster analysis is a data mining technology designed to derive a good understanding of data to solve clustering problems by extracting useful information from a large volume of mixed data elements. Recently, researchers have aimed to derive clustering algorithms from nature’s swarm behaviors. Ant-based clustering is an approach inspired by the natural clustering and sorting behavior of ant colonies. In … …Journal of Computer Science, Vol. 9, Issue 6 · 7 Jun 2013 · 7,306 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Cluster Feature-Based Incremental Clustering Approach to Mixed DataA. M. Sowjanya, M. ShashiProblem statement: The main objective of this study is to develop an incremental clustering algorithm that can handle numerical as well as categorical attributes in a given dataset. The authors have previously reported a cluster feature-based algorithm, CFICA that can handle only numerical data. Appraoch: Since many of the real life data mining applications work with datasets that contain both … …Journal of Computer Science, Vol. 7, Issue 12 · 21 Oct 2011 · 7,159 accessesView PDFCite
Research ArticleOpen accessIn the keywordsSingle Pass Seed Selection Algorithm for k-MeansK. Karteeka Pavan, Allam Appa Rao, A.V. Dattatreya Rao, G. R. SridharProblem statement: The k-means method is one of the most widely used clustering techniques for various applications. However, the k-means often converges to local optimum and the result depends on the initial seeds. Inappropriate choice of initial seeds may yield poor results. k-means++ is a way of initializing k-means by choosing initial seeds with specific probabilities. Due to the random … …Journal of Computer Science, Vol. 6, Issue 1 · 31 Jan 2010 · 7,541 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAnchoring 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 accessIn the keywordsA Descriptive Analysis of Students Learning Skills Using Bloom’s Revised TaxonomyJoy Christy Antony Sami, Umamakeswari ArumugamThe academic committees worldwide suggest technical institutions to follow Revised Bloom’s Taxonomy (RBT), a framework that helps to develop learning objectives. The model classifies a hierarchy of educational objectives such as cognitive, sensory and affective domains that are not only helping the students to evolve thinking abilities but also to identify the skills they are lacking with. Analysis of students … …Journal of Computer Science, Vol. 16, Issue 2 · 4 Sep 2019 · 8,539 accessesView PDFCite