Search Results - (( data classification based algorithm ) OR ( data equalization based algorithm ))
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1
An enhancement of classification technique based on rough set theory for intrusion detection system application
Published 2019“…Thus, to deal with huge dataset, data mining technique can be improved by introducing discretization algorithm to increase classification performance. …”
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2
Classification of credit card holder behavior using K Nearest Neighbor algorithm / Ahmad Faris Rahimi
Published 2017“…The second one is to develop prototype for classification of credit cardholder behavior based on k Nearest Neighbors Algorithm. …”
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3
Classification for large number of variables with two imbalanced groups
Published 2020“…Based on the findings, Algorithm 2 outperforms Algorithm 1 in classifying the minority group, while both proposed algorithms perform equally well in classifying the majority group. …”
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4
A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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5
A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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6
A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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7
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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8
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40…”
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9
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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10
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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11
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2023“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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12
Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification
Published 2020“…Thus, TC suggested being the main step in data pre-processing for mountainous terrain before the RBF-based SVM classification process. …”
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13
Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant
Published 2007“…In this research, a vision system algorithm has been developed to identify and locate base of young corn trees based upon robot vision technology, pattern recognition techniques, and knowledge-based decision theory. …”
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14
Applying SAX-based time series analysis to classify EEG signal using a COTS EEG device
Published 2021“…SAX algorithm changes the original time series data into a symbolic string and perform the discretization by dividing a time series into equal-sized segments. …”
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15
A review of feature selection on text classification
Published 2018“…However, in filter approach, the classification accuracies cannot be guaranteed because it does not incorporate with any learning algorithm. …”
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16
Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification
Published 2017“…The performance of improved GA has been evaluated using highly complicated and multimodal benchmark test functions and compared with the standard GA. Based on the occurrences of the best result obtained by an algorithm across different test functions; it is proven that the proposed method outperforms standard GA. …”
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17
Assessment of cognitive load using multimedia learning and resting states with deep learning perspective
Published 2019“…The important EEG electrodes were selected based on the effective connectivity method and after preprocessing, EEG data has been segmented into equal number of segments using selected electrodes to investigate the deep temporal information for cognitive load assessment. …”
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18
Joint routing protocol and image compression algorithm for prolonging node lifetime in wireless sensor network
Published 2018“…Additionally in order to reduce the transmitted data size, a lossy image compression technique based on adaptive Haar wavelet transform has been implemented. …”
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19
Machine Learning and Dyslexia-Diagnostic and Classification System (DCS) for Kids with Learning Disabilities
Published 2018“…This research proposes such a machine learning based diagnostic and classification system. The system is trained by human expert classified data of 857 school children scores in various tests. …”
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Enhanced emotion recognition in videos: a convolutional neural network strategy for human facial expression detection and classification
Published 2023“…Despite extensive research employing machine learning algorithms like convolutional neural networks (CNN), challenges remain concerning input data processing, emotion classification scope, data size, optimal CNN configurations, and performance evaluation. …”
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