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1
Formulating new enhanced pattern classification algorithms based on ACO-SVM
Published 2013“…This paper presents two algorithms that integrate new Ant Colony Optimization (ACO) variants which are Incremental Continuous Ant Colony Optimization (IACOR) and Incremental Mixed Variable Ant Colony Optimization (IACOMV) with Support Vector Machine (SVM) to enhance the performance of SVM.The first algorithm aims to solve SVM model selection problem. …”
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2
Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems
Published 2022“…To achieve this goal, an improved Teaching Learning-Based Optimization (ITLBO) algorithm was proposed in dealing with subset feature selection. …”
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Thesis -
3
Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters
Published 2024“…The methodology used includes data collection, preprocessing, and algorithm implementation with evaluation using crossvalidation techniques. …”
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A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…The main keys of the new classifier are based on the new kernel method, new learning metric and a new optimization algorithm in order to optimize the SVM decision function. …”
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5
Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves
Published 2024“…With SSA optimization, the Sauvola method combined with SVM reaches an accuracy of 99.58%, surpassing other methods that use image processing and ANN classification. …”
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6
Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…The accuracy of each algorithm was evaluated using LibSVM, Bayes network, and Adaboost classifier. …”
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7
The influence of sentiments in digital currency prediction using hybrid sentiment-based Support Vector Machine with Whale Optimization Algorithm (SVMWOA)
Published 2021“…Support Vector Machine (SVM) technique is used with the Whale Optimization Algorithm (WOA) which is inspired by the swarm optimization algorithms. …”
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Proceeding Paper -
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An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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10
Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui
Published 2019“…One of the major research problems is the computation resources required by machine learning algorithm used for classification for HAR. Numerous researchers have tried different methods to enhance the algorithm to improve performance, some of these methods include Support Vector Machine (SVM), Decision Trees, Extreme Learning Machine (ELM), Kernel Extreme Learning Machine (KELM), and Deng’s Reduced Kernel Extreme Learning Machine (RKELM). …”
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11
Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…The segmentation process was optimized using Fuzzy-based Segmentation Parameter. …”
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12
Kinect-based human gait recognition using locally linear embedded and support vector machine
Published 2018“…Firstly, dynamic gait features for one gait cycle are extracted from each subject’s walking gait that is acquired using Kinect sensor. Next, the extracted gait features were then optimized using LLE known as DG-LLE and further classified by multi-class SVM with Error Correcting Output Code (ECOC) algorithm. …”
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Predicting Customer Buying Decisions for Online Shopping with Unbalanced Data Set
Published 2022“…Weka, a data mining tool, provides the facility to classify the data set with different machine learning algorithms. Six machine learning algorithms were applied and compared based on the classification evaluation methods. …”
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Final Year Project / Dissertation / Thesis -
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Radiomics analysis and supervised machine learning model for classification of cervical cancer images using diffusion weighted imaging-MRI
Published 2024“…Additionally, the SVM algorithm was evaluated based on its performance across different DWI bvalues, aiming to optimize scanning time. …”
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15
A study on component-based technology for development of complex bioinformatics software
Published 2004“…The second layer uses discriminative SVM algorithm with a state-of-the-art string kernel based on PSI-BLAST profiles that is used to leverage the unlabeled data. …”
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Monograph -
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Durian (Durio zibethinus) ripeness detection using thermal imaging with multivariate analysis
Published 2021“…Linear discriminant analysis (LDA), k-nearest neighbour (kNN), and support vector machine (SVM) were applied for the establishment of the optimal classification modelling algorithms. …”
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Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images
Published 2024“…The second segmentation algorithm combines Delaunay triangulation clustering in the spatial domain and Particle Swarm Optimization (PSO). …”
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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning
Published 2023“…A new model of stacking ensemble learning by combining three base-learner algorithms namely KNN, SVM and Random Forest into the XGBoost meta-learner algorithm. …”
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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning
Published 2023“…A new model of stacking ensemble learning by combining three base-learner algorithms namely KNN, SVM and Random Forest into the XGBoost meta-learner algorithm. …”
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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning
Published 2023“…A new model of stacking ensemble learning by combining three base-learner algorithms namely KNN, SVM and Random Forest into the XGBoost meta-learner algorithm. …”
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