Search Results - (( using factorization learning algorithm ) OR ( simulation optimization mining algorithm ))
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Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm
Published 2023“…In reducing the learning complexity, a genetic algorithm was implemented to optimize the logical rule throughout the learning phase in performing a 2-satisfiability-based reverse analysis method, implemented during the learning phase as this method was compared. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…This merit is provided by balancing the exploitation of solution structure and exploration of its appropriate weighting factors through use of a robust and efficient optimization algorithm in learning process of GEP approach. …”
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Modelling of optimized hybrid debris flow using airborne laser scanning data in Malaysia
Published 2019“…The general objective of the study was the development of optimized hybrid debris flow models using airborne laser scanning data and Machine learning algorithms in Malaysia. …”
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Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets
Published 2019“…A new self-adaptive hybrid algorithm (CSCMAES) is introduced for optimization. …”
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Logic mining method via hybrid discrete hopfield neural network
Published 2025“…To demonstrate the efficacy of the proposed logic mining approach, experiments were conducted using both simulated and real-world datasets. …”
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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Metaheuristic optimization of perovskite solar cell performance using Taguchi grey relational analysis with grey wolf optimizer
Published 2025“…The metaheuristic approach sequentially employs the L27 orthogonal array (OA) Taguchi-based design of experiment (DoE), Grey Relational Analysis (GRA), Multiple Linear Regression (MLR) and Grey Wolf Optimizer (GWO). The L27 OA Taguchi-based DoE is initially employed to mine sufficient output data simulated using one dimensional solar cell capacitance simulator (SCAPS-1D). …”
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Enhancement processing time and accuracy training via significant parameters in the batch BP algorithm
Published 2020“…We created the dynamic learning rate and dynamic momentum factor for increasing the efficiency of the algorithm. …”
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A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment
Published 2013“…The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…This study showed the task of optimizing the topology structure and the parameter values (e.g., weights) used in the BPNN learning algorithm by using the GA. …”
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Prediction models of heritage building based on machine learning / Nur Shahirah Ja'afar
Published 2021“…These algorithms were developed by using prewar shophouses dataset from 2004 until 2018 based on factors of heritage properties. …”
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Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding
Published 2007“…One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. …”
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Optimization of chest X-ray exposure factors using machine learning algorithm
Published 2023“…In this study, the chest X-ray exposure factors for 178 patients with different body mass index (BMI) values have been analyzed using the Python Machine Learning algorithm. …”
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A bayesian network approach to identify factors affecting learning of Additional Mathematics
Published 2015“…Constraint-based algorithms and score-based algorithms are used to generate the networks into several categories to compare and identify the strong relationships among the factors that affect the students’ learning of the subject. …”
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Three-term backpropagation algorithm for classification problem
Published 2006“…This algorithm utilizes two term parameters which are Learning Rate, α and Momentum Factor,β. …”
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An improved teaching-learning-based optimization for extreme learning machine in floating photovoltaic power forecasting
Published 2025“…This study presents an improved teaching-learning-based optimization algorithm with extreme learning machine for floating photovoltaic power forecasting. …”
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Reverse migration prediction model based on machine learning / Azreen Anuar
Published 2024“…A significant way to minimize the errors is by using a machine learning approach that can predict reverse migration intelligently depending on the tested dataset. …”
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