Search Results - (( model evaluation result algorithm ) OR ( miner classifications learning algorithm ))
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Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method
Published 2023“…Copper deposits; Deposits; Geology; Learning algorithms; Mineralogy; Static Var compensators; Support vector machines; Three dimensional computer graphics; Alteration zones; Grid search; Grid-search method; Mineralization zone; Model Selection; Particle swarm optimization algorithm; Penalty parameters; Performance; Support vector classifiers; Support vectors machine; Particle swarm optimization (PSO); accuracy assessment; algorithm; classification; computer simulation; copper; geological survey; mineral alteration; mineralization; numerical model; ore deposit; parameterization; performance assessment; porphyry; resource assessment; support vector machine; three-dimensional modeling; Iran…”
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Hybrid ant colony optimization and genetic algorithm for rule induction
Published 2020“…In this study, a hybrid rule-based classifier namely, ant colony optimization/genetic algorithm ACO/GA is introduced to improve the classification accuracy of Ant-Miner classifier by using GA. …”
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Crytojacking Classification based on Machine Learning Algorithm
Published 2024journal::journal article -
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Postsurgery classification of best-corrected visual acuity changes based on Pterygium Characteristics using the Machine Learning Technique
Published 2021“…Conclusion. Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics.…”
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A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules
Published 2013“…The successful work on hybridization of ACO and SA algorithms has led to the improved learning ability of ACO for classification. …”
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Adaptive parameter control strategy for ant-miner classification algorithm
Published 2020“…This paper presents a new hybrid Ant-Miner classification algorithm and ant colony system (ACS), called ACS-Ant Miner. …”
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An Analysis Of Various Algorithms For Text Spam Classification And Clustering Using Rapidminer And Weka
Published 2024“…By using the same dataset, which is downloaded from UCI, Machine Learning Repository, various algorithms used in classification and clustering in this simulation has been analysed comparatively. …”
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Predicting breast cancer using ant colony optimisation / Siti Sarah Aqilah Che Ani
Published 2021“…This study implements a machine learning algorithm called Ant Colony Optimization (ACO) algorithm to develop an accurate classification model for predicting breast cancer cells. …”
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Implementation of machine learning algorithm in preventing network congestion
Published 2023text::Final Year Project -
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Development of predictive modeling and deep learning classification of taxi trip tolls
Published 2022“…Several studies discussed the predictive modeling of deep learning in different applications such as classifying tissue features from microstructural data, Crude Oil Prices, mechanical constitutive behavior of materials, microbiome data, and mineral prospectively. …”
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A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets
Published 2021“…This study proposed an enhanced algorithm called hellingerant-tree-miner (HATM) which is inspired by ant colony optimization (ACO) metaheuristic for imbalanced learning using decision tree classification algorithm. …”
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DEVELOPMENT OF PREDICTIVE MODELING AND DEEP LEARNING CLASSIFICATION OF TAXI TRIP TOLLS
Published 2023“…Several studies discussed the predictive modeling of deep learning in different applications such as classifying tissue features from microstructural data, Crude Oil Prices, mechanical constitutive behavior of materials, microbiome data, and mineral prospectively. …”
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Sentiment analysis of customer reviews for Konda Kondi Cafe & Bistro
Published 2025“…The data was pre-processed using tokenization, stop-word removal, and stemming techniques to ensure quality inputs. Machine learning algorithms such as Support Vector Machine (SVM), Naive Bayes (NB), and Decision Tree (DT) were applied using RapidMiner to build classification models. …”
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Neural Networks Ensemble: Evaluation of Aggregation Algorithms for Forecasting
Published 2013“…It can be concluded from the results that the aggregation algorithms of NNs ensemble can improve the accuracy of forecast than the individual NN models with a test data set. …”
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A Model for Evaluation of Cryptography Algorithm on UUM Portal
Published 2004“…The development of the simulation model consists of seven steps. The steps are problem definition, construct the simulation model, test and validate the model, design the simulation experiments, conduct the simulation experiments, evaluate the result and implement the result. …”
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Social Media Sentiment Analysis of Thermal Engineering Students for Continuous Quality Improvement in Engineering Education / Wandeep Kaur ...[et al.]
Published 2017“…A supervised machine learning algorithm was employed for sentiment classification purpose which was implemented using Rapid Miner. …”
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Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition
Published 2010“…The model is evaluated against two previously proposed models and the results confirm the potentiality of dynamic Bayesian networks for dialogue act recognition. …”
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Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data
Published 2023“…Five supervised machine learning methods are being tested which are: Regression Logistics, K-Nearest Neighbour (KNN), Decision Tree (DT), Support Vector Machine (SVM), and Naïve Bayesian Classifier with the aid of a tool, RapidMiner. …”
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Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH
Published 2018“…Based on the empirical results, the proposed algorithm of multistep ahead forecast to the algorithm of BJ-G provides a promising procedure to assess the performance of the BJ-G model in forecasting a highly volatile time series data. …”
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