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Hybrid ACO and SVM algorithm for pattern classification by Alwan, Hiba Basim

“…The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. …”
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Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…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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Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

“…Finally, the algorithm found, which would solve the image segmentation problem.…”
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Integrated ACOR/IACOMV-R-SVM Algorithm by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…The first algorithm, ACOR-SVM, will tune SVM parameters, while the second IACOMV-R-SVM algorithm will simultaneously tune SVM parameters and select the feature subset. …”
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A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization by Ehteram M., Sammen S.S., Panahi F., Sidek L.M.

Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…In order to enhance SVM performance, these problems must be solved simultaneously because error produced from the feature subset selection phase will affect the values of the SVM parameters and resulted in low classification accuracy.Most approaches related with solving SVM model selection problem will discretize the continuous value of SVM parameters which will influence its performance.Incremental Mixed Variable Ant Colony Optimization (IACOMV) has the ability to solve SVM model selection problem without discretising the continuous values and simultaneously solve the two problems.This paper presents an algorithm that integrates IACOMV and SVM.Ten datasets from UCI were used to evaluate the performance of the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with small number of features.…”
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Anomaly behavior detection using flexible packet filtering and support vector machine algorithms by Abdul Wahid, Mohammed N.

“…The Entropy and SVM (EaSVM) is relying on DARPA 99 dataset for analyzing the captured traffics based on anomalies and the use of SVM is to classify the entropy values of the data traffic into normal and abnormal behavior for more accurate results. …”
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Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm by Yap K.S., Abidin I.Z., Ahmad A.R., Hussien Z.F., Pok H.L., Ismail F.I., Mohamad A.M.

“…This paper presents an intelligent system to reduce Non Technical Loss (NTL) using hybrid Support Vector Machine (SVM) and Genetic Algorithm (GA). …”

Intelligent classification algorithms in enhancing the performance of support vector machine by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…The average classification accuracies for the proposed ACOMV–SVM and IACOMV-SVM algorithms are 97.28 and 97.91 respectively. …”
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Solving SVM model selection problem using ACOR and IACOR by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…In applying ACO for optimizing SVM parameters which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretize process would result in loss of some information and hence affect the classification accuracy.In order to enhance SVM performance and solving the discretization problem, this study proposes two algorithms to optimize SVM parameters using Continuous ACO (ACOR) and Incremental Continuous Ant Colony Optimization (IACOR) without the need to discretize continuous value for SVM parameters.Eight datasets from UCI were used to evaluate the credibility of the proposed integrated algorithm in terms of classification accuracy and size of features subset.Promising results were obtained when compared to grid search technique, GA with feature chromosome-SVM, PSO-SVM, and GA-SVM. …”
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Detection and classification of conflict flows in SDN using machine learning algorithms by Mutaz Hamed Hussien Khairi, Sharifah Hafizah Syed Ariffin, Nurul Mu'azzah Abdul Latiff, Kamaludin Mohamad Yusof, Mohamed Khalafalla Hassan, Fahad Taha Al-Dhief, Mosab Hamda, Suleman Khan, Muzaffar Hamzah

“…The EFDT and hybrid DT-SVM algorithms were designed and deployed based on DT and SVM algorithms to achieve improved performance. …”
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Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

“…On the other hand, the traditional algorithm using template matching only obtained 83.65% recognition rate with 0.97 second processing time. …”
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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study by Got, Adel, Zouache, Djaafar, Moussaoui, Abdelouahab, Laith, Abualigah *, Alsayat, Ahmed

“…Support Vector Machine (SVM) has become one of the traditional machine learning algorithms the most used in prediction and classification tasks. …”
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An Arabic hadith text classification model using convolutional neural network and support vector machine / Mohd Irwan Mazlin by Mazlin, Mohd Irwan

“…In this study, the CNN-SVM model predicts all correct when using unseen data. …”
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Using the bees algorithm to optimise a support vector machine for wood defect classification by Pham, D.T, Muhammad, Zaidi, Mahmuddin, Massudi, Ghanbarzadeh, Afshin, Koc, Ebubekir, Otri, Sameh

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Novel approach for IP-PBX denial of service intrusion detection using support vector machine algorithm by Jama, Abdirisaq M., Khalifa, Othman Omran, Subramaniam, Nantha Kumar

“…Using NSL-KDD training dataset, SVM achieved 65.17%, while decision tree and Naïve Bayes has 51.96% & 38.26% of detection rate, respectively. …”
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Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm by Mohamadi S., Sammen S.S., Panahi F., Ehteram M., Kisi O., Mosavi A., Ahmed A.N., El-Shafie A., Al-Ansari N.

VEHICLE CLASSIFICATION FROM CCTV IMAGES AND VIDEOS by KAMARUDDIN, ERNA SHAFFIQA

“…The feature extraction obtain is trained using Linear SVM and Decision Trees. The result shows that the Linear SVM algorithm outperform ACF algorithm with accuracy percentage of 88.5% compared to 62.8% for smaller datasets.…”
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Hand gesture recognition for autism diagnosis using Support Vector Machine (SVM) Algorithm / Muhammad Asyraf Mohamad Zain by Mohamad Zain, Muhammad Asyraf

“…To counter this problem, a system has been proposed to detect the hand gesture using one of the machine learning technique which is Support Vector Machine (SVM) Algorithm. …”
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Rao-SVM machine learning algorithm for intrusion detection system by Abd, Shamis N., Alsajri, Mohammad, Ibraheem, Hind Raad

“…The prominent intrusion machine-learning dataset, UNSW-NB15, was used for the experiments and the results showed that Rao-SVM reached 92.5% accuracy on the UNSW-NB15 dataset.…”
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