Search Results - (( evolution optimization _ algorithm ) OR ( features selection modified algorithm ))
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1
EMG Feature Selection And Classification Using A Pbest-Guide Binary Particle Swarm Optimization
Published 2019“…In order to measure the effectiveness of PBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA), modified binary tree growth algorithm (MBTGA), and binary differential evolution (BDE) were used for performance comparison. …”
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2
Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…For this purpose, the feature selection (FS) method is applied to evaluate the best feature subset from a large available feature set. …”
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3
Hybrid genetic algorithm with multi-parents recombination for job shop scheduling problems / Ong Chung Sin
Published 2013“…Metaheuristic is one of the “approximation methods” that is able to find practically acceptable solutions especially for large-scale problems within a limited amount of time. Genetic Algorithms (GA) which is based on biological evolution is one of the metaheuristics that has been successfully applied to JSSP. …”
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4
Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF)
Published 2019“…This research synthesizes an evaluation of feature selection algorithm by utilizing Term Frequency-Inverse Document Frequency (TF-IDF) as the main algorithm in Android malware detection. …”
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5
Evaluation of feature selection algorithm for android malware detection
Published 2018“…This paper synthesizes an evaluation of feature selection algorithm by utilizing Term Frequency Inverse Document Frequency (TF-IDF) as the main algorithm in Android malware detection. …”
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6
Feature fusion using a modified genetic algorithm for face and signature recognition system
Published 2015“…To overcome the issue of incompatible features to be combined, Wrapper Genetic Algorithm (GA) was implemented as the feature selection algorithm due to its ability to evaluate the features irrespective of which domain by masking the features with bit number. …”
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7
Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm
Published 2024“…This study presents a new approach for ozone level detection through feature selection by the modified Whale Optimization Algorithm (mWOA). …”
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8
Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
Published 2023“…Therefore, to overcome this issue, a modified WOA (mWOA) and modified GWO (mGWO) for wrapper-based feature selection were proposed in this study. …”
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Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
Published 2023“…Therefore, to overcome this issue, a modified WOA (mWOA) and modified GWO (mGWO) for wrapper-based feature selection were proposed in this study. …”
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10
Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm
Published 2024“…This study presents a new approach for ozone level detection through feature selection by the modified Whale Optimization Algorithm (mWOA). …”
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11
Binary whale optimization algorithm with logarithmic decreasing time-varying modified sigmoid transfer function for descriptor selection problem
Published 2023“…The new Binary Whale Optimization Algorithm is integrated with wrapper feature selection and validated on descriptor selection problem to improve Amphetamine-type stimulants drug classification result. …”
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Conference or Workshop Item -
12
Facial features point localization using modified SIFT scale space / Zulfikri Paidi
Published 2020“…Three tests were used on the original SIFT algorithm and modified SIFT. The first test is to evaluate the accuracy of the built-in vector feature. …”
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13
Content-based feature selection for music genre classification
Published 2022“…The feature selection process focuses on the supervised and unsupervised methods with the reason to select significant generalized and specialized music features. …”
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14
Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection
Published 2022“…Experimental results of the EBGWO algorithm on the NSL-KDD dataset in terms of number of selected features and classification accuracy are superior to other benchmark optimisation algorithms. …”
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15
Aco-based feature selection algorithm for classification
Published 2022“…The modified graph clustering ant colony optimisation (MGCACO) algorithm is an effective FS method that was developed based on grouping the highly correlated features. …”
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16
A refined differential evolution algorithm for improving the performance of optimization process
Published 2011“…Various Artificial Intelligent (AI) algorithms can be applied in solving optimization problems. …”
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Conference or Workshop Item -
17
A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network
Published 2014“…Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. …”
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18
Feature selection for high dimensional data: An evolutionary filter approach.
Published 2011“…Approach: In this study, we proposed an adapted version of genetic algorithm that can be applied for feature selection in high dimensional data. …”
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19
Two level Differential Evolution algorithms for ARMA parameters estimatio
Published 2013“…The problem of determining simultaneously the model order and coefficient of an Autoregressive Moving Average (ARMA) model is examined in this paper. An Evolutionary Algorithm (EA) comprising two-level Differential Evolution (DE) optimization scheme is proposed. …”
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Proceeding Paper -
20
Comparison between Lamarckian Evolution and Baldwin Evolution of neural network
Published 2006“…We presented hybrid genetic algorithm for optimizing weights as well as the topology of artificial neural networks, by introducing the concepts of Lamarckian and Baldwin evolution effects. …”
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