Search Results - (( using factor method algorithm ) OR ( software classification methods algorithm ))
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Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength
Published 2015“…The ANFIS optimized by AMBA is used employed to model classification of Malaysian small medium enterprises (SMEs) based on strength using non-financial factors. …”
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Predicting Accuracy of Income a Year Using Rough Set Theory
Published 2009“…The result obtained from the experiments showed that the best discretization method is Naive Algorithm, the best split factor is 0.6, the best reduction method is Johnson's Algorithm and the best classifier is Standard Voting. …”
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Early detection of spots high water saturation for landslide prediction using thermal imaging analysis
“…The performance of these segmentation algorithms are measured using misclassification error. …”
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Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield.
Published 2005“…At the core of our system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data non-linearly separable in input space. …”
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Evaluations of oil palm fresh fruit bunches maturity degree using multiband spectrometer
Published 2017“…In this research, several machine learning algorithms housed in WEKA data mining tool were proposed for the building of a classifier models, as compared with other earlier manual, and statistical analytical method which require high computational knowledge in coding, time consumption, and prion to human or computational error. …”
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Analysis of Spatiotemporal Land Use and Land Cover Changes using Remote Sensing and GIS: A Review
Published 2018“…Also, supervised classification in Maximum Likelihood Algorithm method was mostly employed for the LULC classification. …”
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A framework for predicting oil-palm yield from climate data
Published 2006“…At the core of our system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data. …”
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Computational intelligence approach for classification and risk quantification of metabolic syndrome / Habeebah Adamu Kakudi
Published 2019“…The "Cohort study on clustering of lifestyle risk factors and understanding its association with stress on health and well-being among school teachers in Malaysia" (CLUSTer) dataset was used to compare the performance of the proposed Genetically Optimised Bayesian ARTMAP (GOBAM) model and three other classic Adaptive Resonance Theory Mapping (ARTMAP) models –Genetic Algorithm Fuzzy ARTMAP (GAFAM), Fuzzy ARTMAP (FAM), and Bayesian ARTMAP (BAM). …”
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Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning
Published 2024“…This research investigates the multifaceted relationship between various factors and obesity rates in Mexico, Peru, and Colombia using a publicly available dataset. …”
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Recommendation System Model For Decision Making in the E-Commerce Application
Published 2024thesis::doctoral thesis -
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Early detection of high water saturation spots for landslide prediction using thermal image analysis
Published 2018“…There are three segmentation algorithm used in this study which are HSV, K-Means and Feature Matching. …”
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Software defect prediction framework based on hybrid metaheuristic optimization methods
Published 2015“…The classification algorithm is a popular machine learning approach for software defect prediction. …”
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Enhanced Adaptive Neuro-Fuzzy Inference System Classification Method for Intrusion Detection
Published 2024“…The method is then compared to single CART, single ANFIS, BPNN, GA-ANFIS, PSO-ANFIS, and CSO-ANFIS. …”
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Knowledge base processing method based on text classification algorithm
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Fuzzy modeling using Bat Algorithm optimization for classification
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Application of the bees algorithm to the selection features for manufacturing data
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