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1
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…The results reveal that the proposed hybrid algorithm is capable of achieving classification accuracy values of (95.82 % and 97.68 %), detection rates values of (95.8 % and 99.3 %) and false alarm rates values of (0.083 % and 0.045 %) on both KDD CUP 99 and NSL KDD. …”
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2
Web-based expert system for material selection of natural fiber- reinforced polymer composites
Published 2015“…So, the integration of Artificial Neural Network (ANN) with an Expert System for material classification was explored. The computational tool, Matlab was proposed for classification with Levenberg-Marquardt training algorithm, which provided faster rate of convergence for feed forward network. …”
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3
Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images
Published 2024“…This process consists of four steps: pre-processing, segmentation, feature extraction, and classification. …”
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4
An enhancement of classification technique based on rough set theory for intrusion detection system application
Published 2019“…Experimental results show the proposed technique increases accuracy classification percentage up to 99.95%; and the minimum number of bins determine good discretization algorithm. …”
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5
Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…To solve the problems mentioned, integration of unsupervised clustering algorithm and the supervised classifier is proposed. …”
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6
DATA CLASSIFICATION SYSTEM WITH FUZZY NEURAL BASED APPROACH
Published 2005“…But the proposed algorithm still require high computation, the processing time will be long if the dataset is huge. …”
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7
A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network
Published 2014“…The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. …”
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8
Optimization of blood vessel detection in retina images using multithreading and native code for portable devices
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9
Performance evaluation of real-time multiprocessor scheduling algorithms
Published 2016“…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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10
Development of a fingerprint gender classification algorithm using fingerprint global features
Published 2016“…This algorithm is highly recommended in extracting a fingerprint global feature for gender classification process.…”
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11
Knowledge base processing method based on text classification algorithm
Published 2023“…The text classification algorithm's knowledge base processing method utilizes existing data from the knowledge base to guide the construction and training of the classification model. …”
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12
Incremental continuous ant colony optimization for tuning support vector machine’s parameters
Published 2013“…Hence, in applying Ant Colony Optimization for optimizing Support Vector Machine parameters, which are continuous in nature, the values wil have to be discretized.The discretization process will result in loss of some information and, hence, affects the classification accuracy and seeks time.This paper presents an algorithm to optimize Support Vector Machine parameters using Incremental continuous Ant Colony Optimization without the need to discretize continuous values.Eight datasets from UCI were used to evaluate the performance of the proposed algorithm.The proposed algorithm demonstrates the credibility in terms of classification accuracy when compared to grid search techniques, GA with feature chromosome-SVM, PSO-SVM, and GA-SVM.Experimental results of the proposed algorithm also show promising performance in terms of classification accuracy and size of features subset.…”
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13
Intelligent classification algorithms in enhancing the performance of support vector machine
Published 2019“…The average classification accuracies for the proposed ACOMV–SVM and IACOMV-SVM algorithms are 97.28 and 97.91 respectively. …”
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14
Enhancing pineapple cultivar classification: a framework for image quality, feature extraction, and algorithmic refinement
Published 2025“…Accurate classification of pineapple cultivars is hindered by limitations in image acquisition, feature extraction, and classification algorithms. …”
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15
Hybrid ACO and SVM algorithm for pattern classification
Published 2013“…This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. …”
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16
A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification
Published 2023“…Moreover, K-Nearest Neighbor (KNN) classifier was used to evaluate the effectiveness of the features identified by the proposed SCSO algorithm. The performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy, optimum feature size, and computational cost in seconds. …”
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17
A New And Fast Rival Genetic Algorithm For Feature Selection
Published 2021“…Moreover, a dynamic mutation rate is proposed to enhance the search behaviour of the algorithm in the mutation process. …”
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Text spam messages classification using Artificial Immune System (AIS) algorithms
Published 2024thesis::master thesis -
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Route Optimization System
Published 2005“…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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20
Formulating new enhanced pattern classification algorithms based on ACO-SVM
Published 2013“…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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