Search Results - (( using composition clustering algorithm ) OR ( java data normalization algorithm ))
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Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…Even a normal people using clustering to grouping their data. …”
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SANAsms: Secure short messaging system for secure GSM mobile communication
Published 2008“…The system is developed using Java 2 Micro Edition (J2ME) which is written in Java. …”
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Seed disperser ant algorithm for optimization / Chang Wen Liang
Published 2018“…The classical benchmark problems and composite benchmark functions from Congress on Evolutionary Computation (CEC) 2005 special session is used for validate SDAA. …”
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Data redundancy reduction scheme for data aggregation in wireless sensor network
Published 2020“…This research proposes Data Redundancy Reduction Scheme (DRRS) which includes three algorithms namely, Metadata Classification (MC), Selection Active Nodes (SAN) and Anomaly Detection (AD) algorithms that works before data aggregation, when multiple composite events simultaneously occur in the different locations within the cluster. …”
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Electing the best set of features will help to improve the classifier predictions in terms of the normal and abnormal pattern. The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. …”
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On the earthquake distribution modeling in Sumatra by Cauchy cluster process : comparing log-linear and log-additive intensity models
Published 2023“…The estimation procedure follows the standard two-step estimation technique, where the first step adapts the method for the Generalized Additive Models (GAMs) using penalized iteratively reweighted least squares (PIRLS) algorithm, and the second step employs the second-order composite likelihood. …”
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Hybrid of fuzzy clustering neural network over NSL dataset for intrusion detection system
Published 2013“…In recent years, data mining approach for intrusion detection have been advised and used. The approach such as Genetic Algorithms , Support Vector Machines, Neural Networks as well as clustering has resulted in high accuracy and good detection rates but with moderate false alarm on novel attacks. …”
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Challenges of hidden data in the unused area two within executable files
Published 2009“…The designed algorithms were intended to help in proposed system aim to hide and retract information (data file) with in unused area 2 of any execution file(exe.file). …”
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Quantitative Analysis And Mapping Of Concrete Scanning Electron Microscope (SEM) Images
Published 2018“…By spatial segmentation of K-Means algorithms, the cluster groups generated were carefully reviewed before proceeding to the final analysis. …”
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Monograph -
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Unsupervised colour segmentation of white blood cell for acute leukaemia images
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Working Paper -
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Sustainable Location Identification Decision Protocol (SuLIDeP) For Determining The Location Of Recycling Centres In A Circular Economy
Published 2019“…More significantly, this process could be used for clustering and reducing supply chains complexity to enable the setting up of multiple and optimally located recycling centres. …”
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Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions
Published 2017“…Two sets of experimental data involving 20 diabetic patients and 20 healthy subjects were collected from CITO laboratory Semarang Central Java, Indonesia. …”
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Computational analysis of biological data: Where are we?
Published 2024“…Computer modeling allows such electrical stimulations using statistics, bioinformatics and advanced machine-learning algorithms. …”
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Book Chapter -
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Dietary patterns and health outcomes among African American maintenance hemodialysis patients
Published 2020“…Implausible energy intake reports were screened out by comparing reported energy intake (rEI) with predicted total energy expenditure (pTEE). Cluster analysis, using the k-means algorithm, identified two distinct dietary patterns in the study population: a high “sugar sweetened beverage” pattern (hiSSB) and a low “sugar sweetened beverage pattern” (loSSB). …”
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