Search Results - (( teaching learning algorithm ) OR ( machine ((mining algorithm) OR (bees algorithm)) ))*
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Application of the bees algorithm for constrained mechanical design optimisation problem
Published 2019“…Nowadays, many optimisation algorithms have been introduced due to the advancement of technology such as Teaching Learning Based Optimisation (TLBO), Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO) and the Bees Algorithm. …”
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E4ML: Alat untuk pembelajaran perlombongan data
Published 2004“…However, most of the tool is complicated to be used by the beginner user especially to student.The absence of specific and easy tool was made the student unable to understand the use and applications in this field.This paper discusses the development of the teaching aid tool (software) that consist of several machine learning algorithms for the purpose of explaining data mining processes.With this tool, teaching and learning for such course can be enhanced in order to provide better understanding in data mining and machine learning.…”
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Using the bees algorithm to optimise a support vector machine for wood defect classification
Published 2007“…This paper describes a new application of the Bees Algorithm to the optimization of a Support Vector Machine (SVM) for the problem of classifying defects in plywood. …”
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Application of the Bees Algorithm to find optimal drill path sequence
Published 2024“…The Bees Algorithm was run using R Software. The results found are compared with the results of other algorithms in terms of the drill path length and machining time. …”
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Proceeding Paper -
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Machine learning application in predicting anterior cruciate ligament injury among basketball players
Published 2025“…A one-year follow-up was conducted to monitor ACL injury, identifying n=11 injured players. Four machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR)—were developed to predict ACL injury. …”
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Optimisation of Surface Roughness in 3D Printing Using the Bees Algorithm
Published 2025“…The focus of this study is to determine the optimum input parameter of the 3D printer using the Bees Algorithm (BA). This study uses the Bees Algorithm to predict the best combination parameters to optimise the surface roughness of parts printed by a fused deposition modelling (FDM) machine. …”
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Book Chapter -
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Artificial bee colony in optimizing process parameters of surface roughness in end milling and abrasive waterjet machining
Published 2012“…Optimizing the process parameters is essential in order to provide a better quality and economics machining. This research develops an optimization algorithm using artificial bee colony (ABC) algorithm to optimize the process parameters that will lead to minimum surface roughness (Ra) value for both end miling and abrasive waterjet machining. …”
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Final Year Project Report / IMRAD -
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Surface roughness optimization based on hybrid harmony search and artificial bee colony algorithm in electric discharge machining process
Published 2023“…Electric discharges; Optimal systems; Optimization; Surface roughness; Artificial bee colonies (ABC); Artificial bee colony algorithms; Convergence rates; Electric discharge machining (EDM); Hybrid approach; Numerical applications; Optimal solutions; Surface roughness (Ra); Electric discharge machining…”
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Optimization of drilling path using the bees algorithm
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Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction
Published 2014“…This study proposed a hybrid algorithm, based on Artificial Bee Colony (ABC) and LSSVM, that consists of three algorithms; ABC-LSSVM, lvABC-LSSVM and cmABC-LSSVM. …”
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Application of the bees algorithm to the selection features for manufacturing data
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Estimation of optimal machining control parameters using artificial bee colony
Published 2013“…This research employed ABC algorithm to optimize the machining control parameters that lead to a minimum surface roughness (R a) value for AWJ machining. …”
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An early warning system for students at risk using supervised machine learning
Published 2024“…For that, this study will perform a study of the current issues, factors, and solutions in education student’s data, determine the supervised machine learning algorithms, compare which model is the best predict the students’ performances, develop an early warning system for the educators to make an early decision in order to assist and consult the at-risk students, and finally conduct testing and evaluation of the system. …”
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Machine Learning Algorithms for Diabetes Prediction: A Review Paper
Published 2023“…Computer aided diagnosis; Data mining; Learning systems; Patient treatment; Predictive analytics; Robotics; Support vector machines; Data mining algorithm; Diabetes mellitus; Early diagnosis; Knowledge accumulation; Literature reviews; Prediction techniques; Review papers; Support vector machine algorithm; Learning algorithms…”
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Lévy mutation in artificial bee colony algorithm for gasoline price prediction
Published 2012“…In this paper, a mutation strategy that is based on Lévy Probabily Distribution is introduced in Artificial Bee Colony algorithm. The purpose is to better exploit promising solutions found by the bees.Such an approach is used to improve the performance of the original ABC in optimizing Least Squares Support Vector Machine hyper parameters.From the conducted experiment, the proposed lvABC shows encouraging results in optimizing parameters of interest.The proposed.lvABC-LSSVM has outperformed existing prediction model, Backpropogation Neural Network (BPNN), in predicting gasoline price.…”
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An improved bees algorithm local search mechanism for numerical dataset
Published 2015“…Bees Algorithm (BA), a heuristic optimization procedure, represents one of the fundamental search techniques is based on the food foraging activities of bees. …”
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Thesis -
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Privacy Preserving Features Selection for Data Mining using Machine Learning Algorithms
Published 2023“…Data Analytics; Data mining; Decision making; Feature extraction; Machine learning; Predictive analytics; Privacy by design; Features selection; Fine grains; No leakages; Predictive modeling; Privacy preserving; Learning algorithms…”
Conference Paper
