Search Results - (( model evaluation swarm algorithm ) OR ( _ classification based algorithm ))
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1
Improved cuckoo search based neural network learning algorithms for data classification
Published 2014“…This research proposed an improved CS called hybrid Accelerated Cuckoo Particle Swarm Optimization algorithm (HACPSO) with Accelerated particle Swarm Optimization (APSO) algorithm. …”
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Thesis -
2
Dingle's Model-based EEG Peak Detection using a Rule-based Classifier
Published 2015“…Rule-based classifier with an estimation technique based on particle swarm optimization (PSO) is employed in the classification stage. …”
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3
Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam
Published 2017“…In the preliminary study, the algorithm is evaluated on the four different peak models of the three EEG signals using the artificial neural network (ANN) with particle swarm optimization (PSO) as learning algorithm. …”
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4
Optimized image enhancement of colour processing for retinal fundus image
Published 2025“…Secondly, the enhanced Tuned Brightness Controlled Single-Scale Retinex with Hybrid Particle Swarm Optimization - Contrast stretching (eTBCSSR-HPSOCS) algorithm is introduced to tackle the limitations of the standard Particle Swarm Optimization (PSO) algorithm in HPSOCS, which is prone to local optima and exhibits low convergence rates. …”
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5
Optimized image enhancement of colour processing for retinal fundus image
Published 2025“…Secondly, the enhanced Tuned Brightness Controlled Single-Scale Retinex with Hybrid Particle Swarm Optimization - Contrast stretching (eTBCSSR-HPSOCS) algorithm is introduced to tackle the limitations of the standard Particle Swarm Optimization (PSO) algorithm in HPSOCS, which is prone to local optima and exhibits low convergence rates. …”
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6
A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification
Published 2023“…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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7
Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images
Published 2024“…For segmentation, the first proposed algorithm is based on the boundary condition model, which is tested over the ISIC dataset and achieved 96% of accuracy. …”
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8
An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…Recently, various techniques based on different algorithms have been developed. …”
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9
Evaluation of different peak models of eye blink EEG for signal peak detection using artificial neural network
Published 2016“…In this study, performance evaluation of the existing peak models is conducted based on Artificial Neural Network (ANN) with particle swarm optimization (PSO) as learning algorithm. …”
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10
Software defect prediction framework based on hybrid metaheuristic optimization methods
Published 2015“…For the purpose of this study, ten classification algorithms have been selected. The selection aims at achieving a balance between established classification algorithms used in software defect prediction. …”
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Thesis -
11
Evaluation Of Different Peak Models Of Eye Blink Eeg For Signal Peak Detection Using Artificial Neural Network
Published 2016“…In this study, performance evaluation of the existing peak models is conducted based on Artificial Neural Network (ANN) with particle swarm optimization (PSO) as learning algorithm. …”
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Article -
12
Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection
Published 2020“…Four state-of-the-art algorithms include particle swarm optimization, flower pollination algorithm, butterfly optimization algorithm, and CBGWO are used to examine the effectiveness of proposed methods in feature selection. …”
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13
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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14
Hybrid binary grey Wolf with Harris hawks optimizer for feature selection
Published 2021“…The performance of the proposed hybrid method was compared with Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), Binary Harris Hawks Optimizer (BHHO), Binary Genetic Algorithm (BGA) and Binary Hybrid BWOPSO. …”
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15
Hybrid Binary Grey Wolf with Harris Hawks Optimizer for Feature Selection
Published 2021“…The performance of the proposed hybrid method was compared with Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), Binary Harris Hawks Optimizer (BHHO), Binary Genetic Algorithm (BGA) and Binary Hybrid BWOPSO. …”
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16
Hybrid binary grey Wolf with Harris hawks optimizer for feature selection
Published 2021“…The performance of the proposed hybrid method was compared with Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), Binary Harris Hawks Optimizer (BHHO), Binary Genetic Algorithm (BGA) and Binary Hybrid BWOPSO. …”
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17
Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…The segmentation process was optimized using Fuzzy-based Segmentation Parameter. Also, six techniques: Ant Colony Optimization (ACO), Gain Ratio (GR), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), Random forest (RF), and Correlation-based Feature Selection (CFS) were used for the feature selection. …”
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Thesis -
18
Intelligent image noise types recognition and denoising system using deep learning / Khaw Hui Ying
Published 2019“…Based on the final denoised images, the model has proven its reliability, in terms of both visual quality and quantitative evaluation. …”
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Evaluation of data mining models for predicting concrete strength
Published 2024“…These models are all evaluated with hyperparameter tuning and different feature selection techniques. …”
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Final Year Project / Dissertation / Thesis
