Search Results - (( data transformation method algorithm ) OR ( using optimization based algorithm ))
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1
An efficient indexing and retrieval of iris biometrics data using hybrid transform and firefly based K-means algorithm title
Published 2019“…It uses a weighted K-means clustering algorithm based on the improved FA to optimize the initial clustering centers of K-means algorithm, known as Weighted K-means clustering-Improved Firefly Algorithm (WKIFA). …”
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Thesis -
2
Using genetic algorithms as image watermarking performance optimizer / Zuhaili Zahid
Published 2008“…The process of embedding and retracting the data cause the original embedded data to be distorted as a side effect of the current method used. …”
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3
Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)
Published 2020“…Clustering, an unsupervised method of grouping sets of data, is used as a solution technique in various fields to divide and restructure data to become more significant and transform them into more useful information. …”
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Article -
4
Enhanced Flipping Technique to Reduce Variability in Image Steganography
Published 2023“…Benchmarking; Discrete cosine transforms; Genetic algorithms; Image coding; Image enhancement; Mean square error; Signal to noise ratio; Bayes method; Cover-image; Data hidden; Embedding capacity; Flipping methods; Least significant bits; Medium; Optimisations; Variability; Visual qualities; Steganography…”
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5
Assisted History Matching by Using Genetic Algorithm and Discrete Cosine Transform
Published 2014“…Later, an algorithm combining both Genetic Algorithm and Discrete Cosine Transform was proposed, which shows the step-by-step sequence of both methods. …”
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Final Year Project -
6
Taguchi's method for optimized neural network based autoreclosure in extra high voltage lines
Published 2008“…The fault identification prior to reclosing is based on optimized artificial neural network associated with Levenberg Marquardt algorithm to train the ANN and Taguchi's Method to find optimal parameters of the algorithm and number of hidden neurons. …”
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Conference or Workshop Item -
7
Performance Analysis of ARMA based Magnetic Resonance Imaging (MRI) Reconstruction Algorithm
Published 2012“…The proposed model coefficients determination in conjunction with various methods of optimal model order determination were then applied on MRI data using both Transient Error Reconstruction Algorithm (TERA) and modified Transient Error Reconstruction Algorithm to obtain images with improved resolution. …”
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Monograph -
8
Analysis of multiexponential transient signals using interpolation-based deconvolution and parametric modeling techniques
Published 2003“…Direct deconvolution approach often leads to poor resolution of ihe estimated decay rates since the fast Fourier transform (FFT) algorithm is used to analyze the resulting deconvolved data. …”
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Proceeding Paper -
9
Autoreclosure in Extra High Voltage Lines using Taguchi’s Method and Optimized Neural Networks
Published 2008“…The fault identification prior to reclosing is based on optimized artificial neural network associated with standard Error Back-Propagation, Levenberg Marquardt Algorithm and Resilient Back-Propagation training algorithms together with Taguchi’s Method. …”
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10
Autoreclosure in Extra High Voltage Lines using Taguchi's Method and Optimized Neural Networks
Published 2009“…The algorithms are developed using MATLAB software. A range of faults are simulated on EHV modeled transmission line using SimPowerSytems, and the spectra of the fault data are analyzed using fast Fourier transform to extract features of each type of fault. …”
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11
Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
Published 2022“…Firstly, the pre-processing of medical data is done using log-transformation that converts the data to its uniform value range. …”
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Thesis -
12
Autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks
Published 2008“…The fault identification prior to reclosing is based on optimized artificial neural network associated with standard Error Back-Propagation, Levenberg Marquardt Algorithm and Resilient Back-Propagation training algorithms together with Taguchi's Method. …”
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13
Optimal Charging Strategy for Plug-in Hybrid Electric Vehicle Using Evolutionary Algorithm
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14
Genetic algorithm based ensemble framework for sentiment analysis
Published 2018“…Since there are many methods involved in each task of the multilayered ensemble, genetic algorithm is added to optimize the overall framework in order to select the optimal combinations of methods in each layer that can produce satisfactory results. …”
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Thesis -
15
Delay and energy-aware routing for efficient data collection in wireless sensor networks / Ihsan Ali
Published 2024“…The hybrid method, IDBA-AFW, aims to enhance the original IDBA by incorporating the Adaptive Floyd-Warshall algorithm for graph transformation and optimization. …”
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Thesis -
16
Optimized feature construction methods for data summarizations of relational data
Published 2014“…This thesis also presents the study of a method to improve the descriptive accuracy of DARA algorithm by generating multi-instances of summarized data. …”
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Thesis -
17
Efficient classifying and indexing for large iris database based on enhanced clustering method
Published 2018“…In the current work, the new Weighted K-means algorithm based on the Improved Firefly Algorithm (WKIFA) has been used to overcome the shortcomings in using the Fireflies Algorithm (FA). …”
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Article -
18
Estimation of Transformers Health Index Based on Condition Parameter Factor and Hidden Markov Model
Published 2023Conference Paper -
19
P300 detection of brain signals using a combination of wavelet transform techniques
Published 2012“…Wavelet transform (WT), student’s two-sample t-statistic (T-Test) and support vector machines (SVM) used in designing the algorithms. …”
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20
A maximal-clique-based clustering approach for multi-observer multi-view data by using k-nearest neighbor with S-pseudo-ultrametric induced by a fuzzy similarity
Published 2024“…Partitioning multi-view data is a recent challenge in clustering methods, which traditionally consider single-view data. …”
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