Search Results - (( java application clustering algorithm ) OR ( knowledge selection learning algorithm ))

Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly by Zulkifly, Ahmad Zuladzlan

“…This project will use fuzzy k-means clustering algorithm to cluster the data because it is easy to implement and have many advantages. …”
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Multiview Laplacian semisupervised feature selection by leveraging shared knowledge among multiple tasks by Krishnasamy, Ganesh, Paramesran, Raveendran

“…Experiment results on several multimedia applications have shown that the proposed algorithm is competitive compared with the other single-view feature selection algorithms.…”
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A Toolkit for Simulation of Desktop Grid Environment by FOROUSHAN, PAYAM CHINI

“…The prototypes will be developed using JAVA language united with a MySQL database. Core functionality of the simulator are job generation, volunteer generation, simulating algorithms, generating graphical charts and generating reports. …”
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Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

“…The proposed algorithm is compared with the state-of-the-art feature selection algorithms using three different datasets. …”
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Application-Programming Interface (API) for Song Recognition Systems by Murtadha Arif Sahbudin, Chakib Chaouch, Salvatore Serrano

Lexicon-based and immune system based learning methods in Twitter sentiment analysis by Jantan, Hamidah, Drahman, Fatimatul Zahrah, Alhadi, Nazirah, Mamat, Fatimah

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Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform by Alias, Norma, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Hamzah, Norhafiza, Islam, Md. Rajibul

“…The development of this architecture is based on several programming language as it involves algorithm implementation on C, parallelization using Parallel Virtual Machine (PVM) and Java for web services development. …”
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A web-based implementation of k-means algorithms by Lee, Quan

“…Firstly, k-luster could incorporate additional clustering algorithms, or even classification algorithms in the future. …”
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Automated model selection for corporation credit risk assessment using machine learning / Zulkifli Halim by Halim, Zulkifli

“…This study contributed to the body of knowledge by proposing an automated machine learning model selection for the CCRA study. …”
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Constrained clustering approach to aid in remodularisation of object-oriented software systems / Chong Chun Yong by Chong, Chun Yong

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Machine learning cases in clinical and biomedical domains by Che Azemin, Mohd Zulfaezal, Ashimi, Tijani Ahmad, Md Mustafa, Md Muziman Syah

“…Learning algorithm can generally be categorised into supervised and unsupervised learning. …”
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Kernel and multi-class classifiers for multi-floor wlan localisation by Abd Rahman, Mohd Amiruddin

“…In this work, two AP selection algorithms are proposed which are Max Kernel and Kernel Logistic Discriminant that implement the knowledge of kernel density estimate and logistic regression machine learning classification. …”
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Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process by Ali Al-Assadi, Hayder M. A.

“…Artificial Neural Network (ANN) was selected from Machine Learning Algorithms to be the learning algorithm. …”
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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things by Hadi, Ahmed Adnan

“…Here, the memory consumption can be reduced by enabling a feature selection algorithm that excludes nonrelevant features and preserves the relevant ones. the algorithm is developed based on the variable length of the PSO. …”
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The implementation of z-numbers in fuzzy clustering algorithm for wellness of chronic kidney disease patients by N. J., Mohd Jamal, Ku Muhammad Naim, Ku Khalif, Mohd Sham, Mohamad

“…A case study of the CKD patients with the selected indicators is considered to demonstrate the capability of z-numbers to handle the knowledge of human being and uncertain information and also will present the idea in developing a robust and reliable fuzzy clustering algorithm particularly in dealing with knowledge of human being using z-numbers.…”
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Towards a better feature subset selection approach by Shiba, Omar A. A.

“…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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Binary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Pr... by Sammen S.S., Ehteram M., Sheikh Khozani Z., Sidek L.M.

The impact of virtual reality on programming algorithm courses on student learning outcomes by Dewi, Ika Parma, Ambiyar, Effendi, Hansi, Giatman, Muhammad, Hanafi, Hafizul Fahri, Ali, Siti Khadijah

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Talent classification using support vector machine technique / Hamidah Jantan, Norazmah Mat Yusof and Mohd Hanapi Abdul Latif by Jantan, Hamidah, Mat Yusof, Norazmah, Abdul Latif, Mohd Hanapi

“…Support Vector Machine (SVM) is among the popular learning algorithm for classification in soft computing techniques. …”
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Feature Selection and Ensemble Meta Classifier for Multiclass Imbalance Data Learning by Sainin, Mohd Shamrie, Alfred, Rayner, Alias, Suraya, Lammasha, Mohamed A.M.

“…The aim of this paper is to investigate the effects of combining feature selection and ensemble classifiers on the prediction performance in addressing the multiclass imbalance data learning .This research uses data obtained from the Malaysian medicinal leaf images shape data and three other large benchmark data sets in which six ensemble methods from Weka machine learning tool were selected to perform the classification task.These ensemble methods include the AdaboostM1, Bagging, Decorate, END, MultiboostAB, and Rotation Forest.In addition, five base classifiers were used; Naïve Bayes, SMO, J48, Random Forest, and Random Tree in order to examine the performance of the ensemble methods. …”
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