Search Results - (( _ validation study algorithm ) OR ( user identification swarm algorithm ))*
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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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Neuro-modelling and fuzzy logic control of a two-wheeled wheelchair system
Published 2024“…This research addresses these challenges by developing a dynamic non-linear model and stability control using computational algorithms. A neural network-based Nonlinear Autoregressive model with Exogenous Inputs (NARX) was developed, capturing and behaving similar to the complex dynamics of the wheelchair system with the identification on the experimental input–output data. …”
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Validation on an enhanced dendrite cell algorithm using statistical analysis
Published 2017“…In this study, we evaluated the performance of the enhanced algorithm called dendrite cell algorithm using sensitivity, specificity, false positive rate, and accuracy and validated the result using parametric and non parametric statistical significant tests. …”
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Optimization of assembly line balancing with energy efficiency by using tiki-taka algorithm
Published 2023“…Lastly, a study of the industrial case was performed as a validation of the developed model and algorithm. …”
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A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation
Published 2023“…Grid search cross validation (CV) is applied for hyperparameter tuning of the algorithms. …”
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Text Extraction Algorithm for Web Text Classification
Published 2010“…The extraction algorithm consists of three phases namely web page extraction, rule formulation, and algorithm validation. …”
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Linear-pso with binary search algorithm for DNA motif discovery / Hazaruddin Harun
Published 2015“…Evaluation of these algorithms showed that the results are unsatisfactory due to the lower validity and accuracy of these algorithms. …”
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Phylogenetic tree classification system using machine learning algorithm
Published 2015“…This study adopted supervised machine learning algorithm which is the Support Vector Machine (SVM) for classification. …”
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Final Year Project Report / IMRAD -
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A Comparison Study On Pca_Modular Pca And Lda For Face Recognition
Published 2017“…This paper will study the performance of three face recognition algorithms which are PCA, Modular PCA and LDA. …”
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Extended development of a Computer Aided Detection (CAD) system for brain bleed in CT / Muhammad Illyas Abdul Muhji
Published 2018“…Thus the main objective of this study is to implement an automatic algorithm to previous algorithm to make it fully automatic system. …”
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Landslide susceptibility mapping at VAZ watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF)...
Published 2013“…In this study, both MLP and RBF algorithms were used in artificial neural network model. …”
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Crypt Edge Detection Using PSO,Label Matrix And BI-Cubic Interpolation For Better Iris Recognition(PSOLB)
Published 2017“…Recently,there has been renewed interest in iris features detection.Gabor filter,cross entrophy, upport vector,and canny edge detection are methods which produce iris codes in binary codes representation.However,problems have occurred in iris recognition since low quality iris images are created due to blurriness,indoor or outdoor settings, and camera specifications.Failure was detected in 21% of the intra-class comparisons cases which were taken between intervals of three and six months intervals.However,the mismatch or False Rejection Rate (FRR) in iris recognition is still alarmingly high.Higher FRR also causes the value of Equal Error Rate (EER) to be high.The main reason for high values of FRR and EER is that there are changes in the iris due to the amount of light entering into the iris that changes the size of the unique features in the iris.One of the solutions to this problem is by finding any technique or algorithm to automatically detect the unique features.Therefore a new model is introduced which is called Crypt Edge Detection which combines PSO,Label Matrix,and Bi-Cubic Interpolation for Iris Recognition (PSOLB) to solve the problem of detection in iris features.In this research, the unique feature known as crypts has been chosen due to its accessibility and sustainability.Feature detection is performed using particle swarm optimisation (PSO) as an algorithm to select the best iris texture among the unique iris features by finding the pixel values according to the range of selected features.Meanwhile, label matrix will detect the edge of the crypt and the bi-cubic interpolation technique creates sharp and refined crypt images.In order to evaluate the proposed approach,FAR and FRR are measured using Chinese Academy of Sciences' Institute of Automation (CASIA) database for high quality images.For CASIA version 3 image databases, the crypt feature shows that the result of FRR is 21.83% and FAR is 78.17%.The finding from the experiment indicates that by using the PSOLB,the intersection between FAR and FRR produces the Equal Error Rate (EER) with 0.28%,which indicated that equal error rate is lower than previous value, which is 0.38%.Thus,there are advantages from using PSOLB as it has the ability to adapt with unique iris features and use information in iris template features to determine the user.The outcome of this new approach is to reduce the EER rates since lower EER rates can produce accurate detection of unique features.In conclusion,the contribution of PSOLB brings an innovation to the extraction process in the biometric technology and is beneficial to the communities.…”
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Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking
Published 2012“…The first contribution is automatic detection of seed points which serve as a prerequisite step for centerline extraction algorithm. The solution consists of an algorithm for automatic collection of candidate seed points using efficient grid line searching mechanism and a validation method which uses local geometric and intensity based features as effective validation rules to discriminate between the actual seed point and false alarms. …”
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Priority and dynamic quantum time algorithms for central processing unit scheduling
Published 2018“…For statistical analysis, a Wilcoxon non-parametric test is used to test and validate the results of the experiments. This study illustrated some contributions, 1. …”
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Compressing Images Using Multi-Level Wavelet Transform Algorithm (MWTA)
Published 2012“…This study aims to use Wavelet Transform Algorithm for image compression. …”
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Multi-Objective Search Group Algorithm for engineering design problems
Published 2022“…This study proposes a new multi-objective version of the Search Group Algorithm (SGA) called the Multi-Objective Search Group Algorithm (MOSGA). …”
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A Study On Gene Selection And Classification Algorithms For Classification Of Microarray Gene Expression Data
Published 2005“…Gene Selection Plays An Important Role Prior To Tissue Classification. In This Paper, A Study On Numerous Combinations Of Gene Selection Techniques And Classification Algorithms For Classification Of Microarray Gene Expression Data Is Presented. …”
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Identifying homogeneous rainfall catchments for non-stationary time series using TOPSIS algorithm and bootstrap K-sample Anderson-Darling test
Published 2018“…This study also suggests the use of Bootstrap K-sample Anderson Darling (BKAD) test for validating regionalized homogeneous rainfall catchments. …”
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An enhancement of sliding window algorithm for rainfall forecasting
Published 2017“…The validation analysis shows that the proposed method has a higher forecasting accuracy than the previous method of sliding window algorithm.…”
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