Search Results - (( data optimization method algorithm ) OR ( parameter implementation new algorithm ))
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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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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption
Published 2023“…In addition, the hyperparameter tuning problem is considered in this research to improve the developed hybrid model by using the AOA algorithm. Lastly, a new hybrid technique suggests tackling the current image encryption application problem by using the estimated parameters of chaotic systems with an optimization algorithm, the SKF algorithm. …”
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1D Multigrid Solver For Finite Element Method
Published 2022“…Usually, the mathematical problem can be solved using only one of those two methods. A sample of such code written in MATLAB programming language was found in the GitHub repository, where the implemented algorithms are far from optimal. …”
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Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm
Published 2022“…Therefore, in this paper, we enhance the GMDH model by implementing the ABC algorithm to optimize the parameter of partial description GMDH model with some transfer functions, namely polynomial, radial basis, sigmoid and hyperbolic tangent function. …”
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Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters
Published 2024“…This research provides new insights into the application of machine learning algorithms for water quality management as well as guidance for optimal algorithm selection.…”
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Power generation allocation in smart grid using Dwarf Mongoose Optimization / Mohamad Irfan Shamani
Published 2025“…Dwarf Mongoose Optimization (DMO) is a new metaheuristic algorithm that published in 2022 by Jeffrey O. …”
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A fast learning network with improved particle swarm optimization for intrusion detection system
Published 2019“…The Fast Learning Network (FLN) is one of the new machine learning algorithms that are easy to implement, computationally efficient, and with excellent learning performance characteristics. …”
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Interferometric array planning using division algorithm for radio astronomy applications
Published 2017“…Finally, the algorithm is able to reduce SLL to -25.23 dB. The third method develops a new algorithm named Division Algorithm (DA) to solve the optimization problems. …”
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An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines
Published 2020“…This problem is often formulated as a typical optimization problem. Metaheuristic algorithms are known to be excellent tools for solving optimization problems. …”
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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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The application of conjugate gradient methods to optimize 3D printed parameters
Published 2024“…In this paper, three types of new CG coefficients are presented with applications in optimizing data. Numerical experiments show that the proposed methods have succeeded in solving problems under exact line search conditions.…”
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Improved genetic algorithm for direct current motor high speed controller implemented on field programmable gate array
Published 2019“…There are many researches have been done to optimize PI controller based evolutionary algorithm, such as Genetic Algorithm (GA). …”
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A hybrid simple exponential smoothing-barnacles mating optimization approach for parameter estimation: Enhancing COVID-19 forecasting in Malaysia
Published 2025“…However, SES is seen to underperform compared to other models due to parameter selection and initial value setting. Therefore, this study aims to propose a new hybrid model, the Single Exponential Smoothing (SES)-Barnacles Mating Optimization (BMO) algorithm, to estimate the optimal smoothing parameter alpha and initial value that can improve the percentage of forecast accuracy. …”
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Optimal overcurrent relay coordination in wind farm using genetic algorithm
Published 2015“…Many suggestions have been made and approaches implemented, however one of the most prominent methods is the use of Genetic Algorithm (GA) to improve the function and coordination of OCRs. …”
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The Application of Conjugate Gradient Methods to Optimize 3D Printed Parameters
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Improving Attentive Sequence-to-Sequence Generative-Based Chatbot Model Using Deep Neural Network Approach
Published 2022“…Hence, this research’s objective aimed to propose an optimization strategy based on Structural Modification and Optimizing Training Network for improving the lacking of accuracy of response in the chatbot application, to propose the algorithm enhancement to improve the current attention mechanism in the Attentive Sequence-to-Sequence model and the network’s training optimization of its inability to memorize the dialogue history, and lastly, to evaluate the accuracy of response of the proposed solution through data training on loss function and real data testing. …”
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Predictive Functional Control With Reduced-Order Observer Design Using Particle Swarm Optimization For Pneumatic System
Published 2020“…This research aimed to develop a Predictive Functional Control using Reduced-Order Observer (PFC-ROO) to reduce the complexity of the pneumatic system. An optimization technique will be implemented in this project using Particle Swarm Optimization (PSO) algorithm. …”
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Prediction Of Petroleum Reservoir Properties Using Nonlinear Feature Selection And Ensembles Of Computational Intelligence Techniques
Published 2015“…The algorithms were implemented with optimized tuning parameters and validated with real-life porosity and permeability datasets obtained from diverse and heterogeneous petroleum reservoirs after they have passed on testing them with a benchmark dataset from the UCI Machine Learning Repository. …”
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