Search Results - (( java adaptation optimization algorithm ) OR ( pattern estimation means algorithm ))
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Parallel distributed genetic algorithm development based on microcontrollers framework
Published 2023Conference paper -
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An improved machine learning model of massive Floating Car Data (FCD) based on Fuzzy-MDL and LSTM-C for traffic speed estimation and prediction
Published 2023“…This thesis uses Fuzzy C-Mean and concept of MDL to constitute patterns and estimate the missing traffic state based on n previous states. …”
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Thesis -
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Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition
Published 2018“…Subsequently,the filtered signal containing useful information was extracted by three methods root mean square (RMS),mean absolute value (MAV),and autoregressive (AR) covariance,all of which are commonly used in TD.A comparative analysis of the three different techniques was performed based on the accuracy performance of the EMG pattern classification using linear vector quantization (LVQ) neural network.In the experimental work undertaken,six healthy subjects comprised of males and females were selected. …”
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A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection
Published 2022“…Overall, the proposed methods achieve a 4.26% mean absolute error of chewing count estimation.…”
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A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection
Published 2022“…Overall, the proposed methods achieve a 4.26% mean absolute error of chewing count estimation.…”
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The enhanced BPNN-NAR and BPNN-NARMA models for Malaysian aggregate cost indices with outlying data
Published 2015“…In theory, the most common training algorithm for Backpropagation algorithms leans on reducing ordinary least squares estimator (OLS) or more specifically, the mean squared error (MSE). …”
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Improved undetected error probability model for JTEC and JTEC-SQED coding schemes
Published 2013“…According to the decoding algorithm the errors are classified into patterns and their decoding result is checked for failures. …”
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Effect Of Fringe Parameter On Phase Measuring Deflectometry Performance
Published 2021“…For the second experiment, it is observed that there is at least 3 fringe pattern in the captured image in order for the phase unwrapping algorithm to work. …”
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Monograph -
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Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region
Published 2024“…The RF model exhibited remarkable accuracy, presenting relative mean absolute error (RMAE), relative root mean square error (RRMSE), and R2 values of 14.33%, 22.23%, and 0.81, respectively. …”
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DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS
Published 2011“…It was found that (by the Group Method of Data Handling algorithm), length of the pipe, wellhead pressure, and angle of inclination have a pronounced effect on the pressure drop estimation under these conditions. …”
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Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms
Published 2024“…The class imbalance makes estimating building damage grades difficult, emphasizing the necessity for careful modeling. …”
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Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions
Published 2017“…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
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Monitoring and assessment of weld penetration condition during pulse mode laser welding using air-borne acoustic signal
Published 2021“…However, the ANN model recorded a more accurate and precise estimation with the lowest estimation error, i.e., 3.3%. …”
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Error concealment technique using wavelet neural network for wireless transmitted digital images
Published 2012“…The method is used to estimate the lost coefficients in the wavelet-domain. …”
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River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network
Published 2024“…Therefore, it is necessary to precisely estimate how the river flow will alter as a result of changing rainfall patterns. …”
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