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Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd
Published 2022“…There are various decision tree algorithms, but the most commonly used are Iterative Dichotomiser 3 (ID3), CART, and C4.5. …”
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Thesis -
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Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions
Published 2024“…Furthermore, the taxonomy was used to evaluate the most recent machine learning algorithm and analysis. …”
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Deep continual learning for predicting blast-induced overbreak in tunnel construction / He Biao
Published 2024“…The developed model is expected to possess the ability of continual learning, which is particularly advantageous in dynamic environments like tunnel blasting. …”
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An improve unsupervised discretization using optimization algorithms for classification problems
Published 2024“…This paper addresses the classification problem in machine learning focusing on predicting class labels for datasets with continuous features. …”
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An improve unsupervised discretization using optimization algorithms for classification problems
Published 2024“…This paper addresses the classification problem in machine learning, focusing on predicting class labels for datasets with continuous features. …”
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Automated feature selection using boruta algorithm to detect mobile malware
Published 2020“…Boruta algorithm is used to select features automatically for assisting the machine learning. …”
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Advanced flood prediction at forest with rainfall data using various machine learning algorithms
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Conference or Workshop Item -
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Autonomous path planning robot using geographical information
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Learning Object -
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Hybrid harmony search algorithm for continuous optimization problems
Published 2020“…Taken together, the proposed hybrid algorithm, called IHS-GWO, aims to address continuous 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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Tracing the real power transfer of individual generators to loads using least squares support vector machine with continuous genetic algorithm
Published 2011“…This paper attempts to trace the real power transfer of individual generators to loads in pool based power system by incorporating the hybridization of Least Squares Support Vector Machine (LS-SVM) with Continuous Genetic Algorithm (CGA)- CGA-LSSVM. The idea is to use CGA to find the optimal values of regularization parameter, γ and Kernel RBF parameter, σ 2 , and adapt a supervised learning approach to train the LS-SVM model. …”
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Conference or Workshop Item -
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Modeling total quality management in higher education with case-based reasoning
Published 2023“…The two main objectives of using TQM in education, which are to improve learning and to improve cost effectiveness, are emphasized. …”
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Classification of JPEG files by using extreme learning machine
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Multi-Robot Learning with Bat Algorithm With Mutation (Bam)
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Undergraduates Project Papers -
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Supervised deep learning algorithms for process fault detection and diagnosis under different temporal subsequence length of process data
Published 2025“…Deep learning algorithms were widely used among all the data-driven algorithms. …”
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PREDICTING THE PRICE OF COTTON USING RNN AND LSTM
Published 2020“…This research is aimed to utilize the machine learning algorithm to try and predict the future price of cotton by using the historical price of cotton provided by investing.com. …”
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Final Year Project -
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The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement
Published 2025“…In this study, a new idea centered on seed growth, Seed Growth Algorithm (SGA), as a conditional convergent evolutionary algorithm is proposed for optimizing several discrete and continuous optimization problems. …”
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Weather prediction in Kota Kinabalu using linear regressions with multiple variables
Published 2021“…Numerical weather prediction is the process of using existing numerical data on weather conditions to forecast the weather using machine learning algorithms. …”
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