Search Results - (( data optimization based algorithm ) OR ( parameters estimation learning algorithm ))
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Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle
Published 2023“…This paper presents the application of a recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) for optimizing the Deep Learning (DL) parameters to estimate the state of charge (SOC) of a battery for an electric vehicle in the real environment. …”
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Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach
Published 2022“…The most popular method to solve parameter estimation problem is using optimization algorithm that easily trap to local minima and poor in exploitation to find the good solutions. …”
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Conference or Workshop Item -
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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption
Published 2023“…Then, the developed algorithm is implemented to estimate the parameters of the Lorenz system. …”
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Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS
Published 2017“…Since extreme learning machine is a non-iterative estimation procedure, it is faster than gradient-based algorithms which are iterative. …”
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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
Published 2025Subjects:Article -
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Optimization of ANFIS with GA and PSO estimating α ratio in driven piles
Published 2020“…A learning fuzzy-based algorithm was used to train the ANFIS model in the MATLAB software. …”
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Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets
Published 2019“…Classification of imbalanced datasets remained a significant issue in data mining and machine learning (ML) fields. This research work proposed a new idea based on the optimization for handling the imbalanced datasets. …”
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Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications
Published 2025“…The algorithm not only aids in estimating the infrastructure cost of the charging lane but also predicts optimal design parameters using trained data. …”
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Identification of continuous-time model of hammerstein system using modified multi-verse optimizer
Published 2021“…his thesis implements a novel nature-inspired metaheuristic optimization algorithm, namely the modified Multi-Verse Optimizer (mMVO) algorithm, to identify the continuous-time model of Hammerstein system. …”
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Theory-guided machine learning for predicting and minimising surface settlement caused by the excavation of twin tunnels / Chia Yu Huat
Published 2024“…In addition, Coati Optimization algorithm, Particle Swarm Opimisation (PSO) and Bayesian Optimsiation (BO) are integrated to identify optimal parameters and minimize settlement during twin tunnel excavation and GBT with the optimisation algorithm has shown consistent capability identifying the least SS induced by twin tunnels Keyword: …”
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Evaluating Adan vs. Adam: an analysis of optimizer performance in deep learning
Published 2025“…With various optimization algorithms available, choosing the one that best suits the deep learning model and dataset can make a substantial difference in achieving optimal results. …”
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Proceeding Paper -
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Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network
Published 2006“…The back propagation algorithm was adopted for this study. The optimal model for predicting missing data found in this study is the network with the combination of learning rate and the number of neurons in the hidden layer of 0.2 and 60. …”
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Final Year Project Report / IMRAD -
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Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass
Published 2018“…ANN is a powerful tool capable of determining the relationship between the desired input and output data while PSO was used as a robust population-based search algorithm to optimize the performance of the ANN model. …”
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A Novel Hybrid Deep Learning Model Based on Simulated Annealing and Cuckoo Search Algorithms for Automatic Radiomics-Based COVID-19 Diagnosis
Published 2025“…The proposed models significantly outperform the baseline model, with accuracy improvements of 13.6 on Data1 and 2.5 on Data2. While the baseline model achieves 88 accuracy on Data1 and 97.6 on Data2, the proposed ALS-IOAP-DNN4 model attains perfect accuracy (100) on both datasets, demonstrating the effectiveness of ALS and advanced optimization techniques. …”
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Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources
Published 2021“…The cumbersome numerical computation and rudimentary empirical solutions hinder faster analysis over a wide range of parameters. However, machine and deep learning methods have higher accuracy but rely heavily on the quality and amount of training data, and the solution may become inconclusive if data is sparse. …”
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Machine-learning-based adaptive distance protection relay to eliminate zone-3 protection under-reach problem on statcom-compensated transmission lines
Published 2020“…These parameters are used to develop a standalone intelligently machine learning adaptive distance relay (ML-ADR) modification. …”
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An optimized ensemble for predicting reservoir rock properties in petroleum industry
Published 2013“…In the present thesis, we proposed a new method named optimized ensembleto improve the prediction of these reservoirs parameters from well log data with the aid of available core data. …”
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Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…To enhance the selection of most highly ranking features, irrelevant features are ‘pruned’ based on determined boundary threshold. In order to estimate the quality of ‘pruned’ features, self-adaptive DE algorithm is proposed. …”
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