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1
Improved whale optimization algorithm for feature selection in Arabic sentiment analysis
Published 2019“…In SA, feature selection phase is an important phase for machine learning classifiers specifically when the datasets used in training is huge. Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. …”
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Time series predictive analysis based on hybridization of meta-heuristic algorithms
Published 2018“…The identified meta-heuristic methods namely Moth-flame Optimization (MFO), Cuckoo Search algorithm (CSA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Differential Evolution (DE) are individually hybridized with a well-known machine learning technique namely Least Squares Support Vector Machines (LS-SVM). …”
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3
Classification with degree of importance of attributes for stock market data mining
Published 2004“…Alan Fan et aI., [2] use Support Vector Machine (SVM) to stock market prediction. The SVM is a training algorithm for learning classification and regression rules from data [7]. …”
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Time series predictive analysis based on hybridization of meta-heuristic algorithms
Published 2018“…The identified meta-heuristic methods namely Moth-flame Optimization (MFO), Cuckoo Search algorithm (CSA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Differential Evolution (DE) are individually hybridized with a well-known machine learning technique namely Least Squares Support Vector Machines (LS-SVM). …”
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5
Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems
Published 2011“…Furthermore, the accuracy of the state estimate with the smallest state error covariance is enhanced by using the Kalman filtering theory. This enhancement produces the filtering solution by using the second sub-algorithm. …”
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Thesis -
6
Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model
Published 2021“…The development of a large-scale metabolic model of Escherichia coli (E. coli) is very crucial to identify the potential solution of industrially viable productions. However, the large-scale kinetic parameters estimation using optimization algorithms is still not applied to the main metabolic pathway of the E. coli model, and they’re a lack of accuracy result been reported for current parameters estimation using this approach. …”
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Thesis -
7
An Improved Hybrid of Particle Swarm Optimization and the Gravitational Search Algorithm to Produce a Kinetic Parameter Estimation of Aspartate Biochemical Pathways
Published 2017“…Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. …”
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Taguchi-Grey Relational Analysis Method for Parameter Tuning of Multi-objective Pareto Ant Colony System Algorithm
Published 2023“…This research aims to find the optimal parameter values for the Pareto Ant Colony System (PACS) algorithm, which is used to obtain solutions for the generator maintenance scheduling problem. …”
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Hardware And Software Partitioning Using Genetic Algorithm In Image Processing Application
Published 2018“…The framework for HW-SW Partitioning using Genetic Algorithm (GA) is developed in MATLAB. …”
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Monograph -
10
Refinement of Tuned Mass Damper parameters on machine support structure using dynamic Cuckoo Search algorithm
Published 2025“…The research aims to optimize key TMD parameters (mass ratio, damping ratio, and frequency ratio) using the dynamic CS algorithm. …”
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Conference or Workshop Item -
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Stock market turning points rule-based prediction / Lersak Photong … [et al.]
Published 2021“…Finally, rule-based optimisation techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE) and Grey Wolf Optimizer (GWO) were used to minimise the amount of time employed in the stock market turning points prediction. …”
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Book Section -
12
Explicit solution of parameter estimate using multiparametric programming for boost converter
Published 2023“…This work proposes an approach to estimate the parameters of capacitance and inductance in a boost converter using an explicit solution. …”
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Conference or Workshop Item -
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On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing
Published 2015“…In many cases, improper tuning of specific parameters undesirably yields suboptimal solution. Addressing this issue, this paper proposes the adoption of parameter free optimization algorithms as the basis of future t-way strategies. …”
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Finite impulse response optimizers for solving optimization problems
Published 2019“…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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15
Finite impulse response optimizers for solving optimization problems
Published 2019“…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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16
Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems
Published 2016“…The proposed ssSKF algorithm is tested using the 30 benchmark functions of CEC 2014, and its performance is compared to the original SKF algorithm, Black Hole (BH) algorithm, Particle Swarm Optimisation (PSO) algorithm, Grey Wolf Optimiser (GWO) algorithm, and Genetic Algorithm (GA). …”
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Kinetic Parameter Estimation in Alkylation of Benzene with 1-Decene through Hybrid Particle Swarm Optimization
Published 2012“…Afterwards, the solution obtained using PSO algorithm is given as initial guesses to the Levenberg–Marquardt algorithm to determine the improved optimal solution. …”
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Final Year Project -
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Intelligent classification algorithms in enhancing the performance of support vector machine
Published 2019“…Eight benchmark datasets from UCI were used in the experiments to validate the performance of the proposed algorithms. …”
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Application Of Genetic Algorithms For Robust Parameter Optimization
Published 2010“…Genetic algorithms (GA) are fairly recent in this respect but afford a novel method of parameter optimization. …”
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