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A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection
Published 2023Subjects: “…e-Learning…”
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Selective harmonic elimination in cascaded H-bridge multilevel inverter using hybrid APSO algorithm / Mudasir Ahmed
Published 2019“…In both algorithms, global searching is performed by APSO algorithm. …”
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Hyperparameter tuned deep learning enabled intrusion detection on internet of everything environment
Published 2022“…In addition, Chaotic Local Search Whale Optimization Algorithm-based Feature Selection (CLSWOA-FS) technique is employed to choose the optimal feature subsets. …”
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Improving Patient Rehabilitation Performance In Exercise Games Using Collaborative Filtering Approach
Published 2024journal::journal article -
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A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation
Published 2023“…Grid search cross validation (CV) is applied for hyperparameter tuning of the algorithms. …”
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Prediction of photovoltaic system output using hybrid Cuckoo Search Least Square Support Vector Machine / Muhammad Aidil Adha Aziz
Published 2019“…This thesis presents a practical and reliable approach for the prediction of PV power output using an intelligent-based technique namely Cuckoo Search Algorithm - Least Square Support Vector Machine (CS-LSSVM). …”
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Bats echolocation-inspired algorithms for global optimisation problems
Published 2016“…The algorithm is a hybrid algorithm that operates using dual level search strategy that takes merits of a particle swarm optimisation algorithm and a modified adaptive bats sonar algorithm. …”
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Neutralisation state driven single-agent search strategy for solving constraint satisfaction problem / Saajid Akram Ahmed Abuluaih
Published 2019“…Since Constraint Satisfaction Problem (CSP) is an NP-complete problem, brute-force search algorithms such as Backtracking algorithm (BT) are required as the guarantee to find a solution, when there is one. …”
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Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection
Published 2020“…The first improvement includes the use of Opposition Based Learning (OBL) at initialization phase of SSA to improve its population diversity in the search space. The second improvement includes the development and use of new Local Search Algorithm with SSA to improve its exploitation. …”
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Hub Angle Control for A Single Link Flexible Manipulator Based on Cuckoo Search Algorithm
Published 2021“…System identification was implemented via swarm intelligence algorithm known as cuckoo search algorithms based on auto regressive with exogenous model structure. …”
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Proceeding -
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A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization
Published 2020“…Therefore, researchers extensively try to improve methods of solving complex optimization problems. Many SI search algorithms are widely applied to solve such problems. …”
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Simulation and design of A DC-DC synchronous converter by intelligent optimization techniques
Published 2010“…The derived optimal parameters of the converter from Genetic Algorithm method are compared with those obtained using the other two intelligent techniques.…”
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Conference or Workshop Item -
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An Intelligent Process Model for Manufacturing System Optimization
Published 2012“…The paper aims to develop an intelligent modeling system using Microsoft Excel spreadsheet interface through mathematical language, mathematical reasoning and algorithms flow chart technique for manufacturing system optimization without human involvement. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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