Search Results - (( variables classification techniques algorithm ) OR ( using function method algorithm ))
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Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification
Published 2020“…All algorithms showed more stability and accuracy when training size applied is more than 6% by the Equal Sample Rate (ESR) method with six variables. …”
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Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…An analysis of the choice of classification techniques is also required. Therefore, the LiDAR derived data were combined with WV-3 image using different fusion methods such as layer stacking (LS), Gram–Schmidt (GS), and PC spectral sharpening (PCSS). …”
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Augmentation of basic-line-search and quick-simplex-method algorithms to enhance linear programming computational performance
Published 2021“…The LP’s application is need to be further computed with a technique and Simplex algorithm is the one that commonly used. …”
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Data Analysis and Rating Prediction on Google Play Store Using Data-Mining Techniques
Published 2022“…This biggest Android Application (App) provides a wide variety of details on requirements such as reviews, quality, number of installs, and explanations for device functionality. This study aims to predict the ratings of Google Play Store apps using decision trees for classification in machine learning algorithms. …”
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Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
Published 2004“…The capability of these models to accommodate wide variety and variability of conditions, and the ability to imitate brain functions, make them popular research area. …”
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A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping
Published 2014“…Thus, the main objective of this paper is to use CHAID method to perform the best classification fit for each conditioning factors, then, combined it with logistic regression (LR) to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. …”
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Recommendation System Model For Decision Making in the E-Commerce Application
Published 2024thesis::doctoral thesis -
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Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor
Published 2019“…Remote sensing technologies are used globally to derive some of crucial spatial variable parameter such as vegetation cover. Three different classification algorithm, minimum distance classifier, Mahalanobis distance classifier and maximum likelihood algorithm was applied to classify the forest area in Gunung Basor. …”
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Hybrid ACO and SVM algorithm for pattern classification
Published 2013“…The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. …”
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Mixed variable ant colony optimization technique for feature subset selection and model selection
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A hybrid-based modified adaptive fuzzy inference engine for pattern classification
Published 2011“…A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data set. …”
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A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification
Published 2023“…The SCSO algorithm replicates the hunting and searching strategies of the sand cat while having the advantage of avoiding local optima and finding the ideal solution with minimal control variables. …”
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Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms
Published 2024“…The classification algorithm used in this research is the Convolutional Neural Network (CNN) algorithm. …”
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Finding an effective classification technique to develop a software team composition model
Published 2017“…Ineffective software team composition has become recognized as a prominent aspect of software project failures.Reports from results extracted from different theoretical personality models have produced contradicting fits, validity challenges, and missing guidance during software development personnel selection.It is also believed that the technique/s used while developing a model can impact the overall results.Thus, this study aims to: 1) discover an effective classification technique to solve the problem, and 2) develop a model for composition of the software development team.The model developed was composed of three predictors: team role, personality types, and gender variables; it also contained one outcome: team performance variable.The techniques used for model development were logistic regression, decision tree, and Rough Sets Theory (RST).Higher prediction accuracy and reduced patte rn complexity were the two parameters forselecting the effective technique.Based on the results, the Johnson Algorithm (JA) of RST appeared to be an effective technique for a team composition model.The study has proposed a set of 24 decision rules for finding effective team members.These rules involve gender classification to highlight the appropriate personality profile for software developers.In the end, this study concludes that selecting an appropriate classification technique is one of the most important factors in developing effective models.…”
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Finding an effective classification technique to develop a software team composition model
Published 2018“…In the end, this study concludes that selecting an appropriate classification technique is one of the most important factors in developing effective models. …”
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Finding an effective classification technique to develop a software team composition model
Published 2018“…In the end, this study concludes that selecting an appropriate classification technique is one of the most important factors in developing effective models. …”
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An improved directed random walk framework for cancer classification using gene expression data
Published 2020“…Numerous cancer studies have combined different machine learning techniques for the cancer diagnosis to improve the accuracy of cancer classification. …”
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Integrated ACOR/IACOMV-R-SVM Algorithm
Published 2017“…Support Vector Machine (SVM), which originates from the statistical approach, is a present day classification technique. The main problems of SVM are selecting feature subset and tuning the parameters. …”
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