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A stochastic iterative learning control algorithm with application to an induction motor
Published 2004“…Another suboptimal recursive algorithm is also proposed based on unknown system dynamics and unknown disturbance statistics. …”
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Wild Blueberry Harvesting Losses Predicted with Selective Machine Learning Algorithms
Published 2022“…The performance of three machine learning (ML) algorithms was assessed to predict the wild blueberry harvest losses on the ground. …”
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Rigorous Phase Equilibrium Calculation Methods for Strong Electrolyte Solutions: The Isothermal Flash
Published 2022“…<p dir="ltr">New algorithms for vapor – liquid (VLE) and liquid - liquid (LLE) equilibrium calculations at constant temperature, pressure and feed phase composition (PT flash), with application to mixtures containing fully dissociating electrolytes are presented. …”
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Evolutionary support vector regression for monitoring Poisson profiles
Published 2023“…This paper aims to monitor Poisson profile monitoring problem in Phase II and develops a new robust control chart using support vector regression by incorporating some novel input features and evolutionary training algorithm. The new method is quicker in detecting out-of-control signals as compared to conventional statistical methods. …”
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Degree-Based Network Anonymization
Published 2020“…Enormous amounts of data collected from social networks or other online platforms are being published publicly for the sake of statistics, marketing, and research, among other objectives. …”
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An Optimal Approach for Assessing Weibull Parameters and Wind Power Potential for Six Coastal Cities in Pakistan
Published 2024“…In this research, we have ameliorated the performance of the recently-introduced novel energy pattern factor method (NEPFM) via a direct search algorithm, i.e., simplex search algorithm (SSA). We designate the resulting algorithm as NEPFM-SSA as it took NEPFM’s Weibull distribution parameters as an initial guess and retuned them with the help of the simplex search algorithm to get updated Weibull distribution parameters, which ensure better fitting characteristics. …”
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Bootstrap-based Aggregations and their Stability in Feature Selection
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Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy
Published 2023“…The complex gas hydrate prevention unit is simulated using the MLPNN model trained by 20 different optimization algorithms. This study investigates the gradient-based, evolutionary, and Bayesian-based optimization algorithms. …”
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Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems
Published 2022“…This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. …”
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Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression
Published 2023“…Subgroup analyses revealed that there is a statistically significant difference in the highest accuracy, lowest accuracy, highest sensitivity, highest specificity, and lowest specificity between algorithms, and there is a statistically significant difference in the lowest sensitivity and lowest specificity between wearable devices. …”
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A novel network-based SIS framework for improved GA performance
Published 2025“…Then, infected nodes can spread their genetic traits to neighboring susceptible nodes through basic genetic algorithm operations within the SIS framework and based on defined probabilities. …”
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A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System
Published 2023“…CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm. …”
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Stability improvement of the PSS-connected power system network with ensemble machine learning tool
Published 2022“…The backtracking search algorithm (BSA) based proposed ensemble model is formed by combining three machine learning (ML) techniques, namely the extreme learning machine (ELM), neurogenetic (NG) system, and multi-gene genetic programming (MGGP). …”
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