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Impact learning : A learning method from feature’s impact and competition
Published 2023“…Machine learning is the study of computer algorithms that can automatically improve based on data and experience. …”
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2
Impact learning: A learning method from feature’s impact and competition
Published 2023“…Machine learning is the study of computer algorithms that can automatically improve based on data and experience. …”
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3
Impact learning: A learning method from feature's impact and competition
Published 2023“…Machine learning is the study of computer algorithms that can automatically improve based on data and experience. …”
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Fuzzy Adaptive Teaching Learning-based Optimization Strategy for the Problem of Generating Mixed Strength T-Way Test Suites
Published 2017“…The teaching learning-based optimization (TLBO) algorithm has shown competitive performance in solving numerous real-world optimization problems. …”
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Broadening selection competitive constraint handling algorithm for faster convergence
Published 2020“…In this paper, a new algorithm incorporating broadening selection strategy in competitive constraint handling paradigm for finding the optimum solution in constrained problems has been proposed, referred as Broadening Selection Competitive Constraint Handling (BSCCH). …”
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Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites
Published 2017“…The teaching learning-based optimization (TLBO) algorithm has shown competitive performance in solving numerous real-world optimization problems. …”
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Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems
Published 2022“…Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. …”
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Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic
Published 2004“…These two adaptive routing algorithms enhance the existing Confidence-based Q (CQ) and Confidence-based Dual Reinforcement Q (CDRQ) Routing Algorithms. …”
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Q-learning whale optimization algorithm for test suite generation with constraints support
Published 2023“…This paper introduces a new variant of a metaheuristic algorithm based on the whale optimization algorithm (WOA), the Q-learning algorithm and the Exponential Monte Carlo Acceptance Probability called (QWOA-EMC). …”
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Exploring a Q-learning-based chaotic naked mole rat algorithm for S-box construction and optimization
Published 2023“…This paper introduces a new variant of the metaheuristic algorithm based on the naked mole rat (NMR) algorithm, called the Q-learning naked mole rat algorithm (QL-NMR), for substitution box construction and optimization. …”
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Software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm
Published 2019“…Although showing competitive performances in many real-world optimization problems, Teaching Learning based Optimization Algorithm (TLBO) has been criticized for having poor control on exploration and exploitation. …”
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Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System
Published 2011“…The proposed algorithms have been tested on a variety of datasets from the UCI machine learning repository. …”
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Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization
Published 2024“…Forecasting wind power generation is crucial for ensuring grid security and the competitiveness of the power market. This paper presents an innovative approach that combines deep learning (DL) with Teaching-Learning-Based Optimization (TLBO) to predict wind power output accurately. …”
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Job position prediction based on skills and experience using machine learning algorithm / Ezaryf Hamdan
Published 2024“…This paper proposes a sophisticated Job Position Prediction system utilizing Machine Learning algorithms and leveraging data from LinkedIn profiles. …”
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An adaptive opposition-based learning selection: The case for jaya algorithm
Published 2021“…Over the years, opposition-based Learning (OBL) technique has been proven to effectively enhance the convergence of meta-heuristic algorithms. …”
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Performance Comparison of Neural Network Training Algorithms for Modeling Customer Churn Prediction
Published 2017“…Predicting customer churn has become the priority of every telecommunication service provider as the market is becoming more saturated and competitive. This paper presents a comparison of neural network learning algorithms for customer churn prediction. …”
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A new experiential learning electromagnetism-like mechanism for numerical optimization
Published 2023Article -
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