Search Results - (( using classification using algorithm ) OR ( (variable OR variables) active learning algorithm ))
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Classification of students' performance in computer programming course according to learning style
Published 2024Conference Paper -
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Classification of Students' Performance in Computer Programming Course According to Learning Style
Published 2024“…The critical point of this study is the use of classification algorithm to extract patterns which are examined from the cognitive factor specific learning style. …”
Proceedings Paper -
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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad
Published 2018“…Therefore, this research has designed fuzzy learning algorithm that is able to classify fruits based on their shape and size features using Harumanis dataset. …”
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Classifying corporates default and non-default using machine learning Artificial Neural Network: multilayer perceptron / Nur Insyirah Mohamad Radzi, Murni Salina Rosidi and Nur Asy...
Published 2023“…Asset volatility is found to be the most significant independent variable. Therefore, ANN is a machine learning algorithm that uses multiple layers perceptron to solve complex problems and predict analytics.…”
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Student Project -
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An evolutionary based features construction methods for data summarization approach
Published 2015“…Here, feature construction methods are applied in order to improve the descriptive accuracy of the DARA algorithm.This research proposes novel feature construction methods, called Variable Length Feature Construction without Substitution (VLFCWOS) and Variable Length Feature Construction with Substitution(VLFCWS), in order to construct a set of relevant features in learning relational data. …”
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Research Report -
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Input significance analysis: Feature selection through synaptic weights manipulation for EFuNNs classifier
Published 2017“…Specifically for the classification process, Big Data can cause the classifiers to process longer than necessary, and the redundant or irrelevant data may misguide the learning classification algorithms to learn the random error or noise related to them. …”
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Article -
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Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
Published 2004“…The neural network learns the rough set’s upper and lower approximations as feature extractors simultaneously with classification. …”
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Risk perception modeling based on physiological and emotional responses / Ding Huizhe
Published 2024“…It was demonstrated that the primary results derived from statistical analysis and machine learning modelling were remarkably consistent, thereby confirming the effectiveness and generalization of the proposed weight adjustment algorithm, particularly in situations with closely matched risk levels. …”
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Examining the potential of machine learning for predicting academic achievement: A systematic review
Published 2023“…Predicting student academic performance is a critical area of education research. Machine learning (ML) algorithms have gained significant popularity in recent years. …”
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Article -
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Examining the potential of machine learning for predicting academic achievement: A systematic review
Published 2023“…Predicting student academic performance is a critical area of education research. Machine learning (ML) algorithms have gained significant popularity in recent years. …”
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Article -
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Algorithm enhancement for host-based intrusion detection system using discriminant analysis
Published 2004“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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A study on advanced statistical analysis for network anomaly detection
Published 2005“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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Monograph -
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Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance
Published 2019“…The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). …”
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Evaluation of postgraduate academic performance using artificial intelligence models
Published 2022“…Exploring other variables such as research activities, marital status, and living conditions would have improved the overall accuracy of the proposed ML models. …”
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Article -
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Evaluation of postgraduate academic performance using artificial intelligence models
Published 2022“…Exploring other variables such as research activities, marital status, and living conditions would have improved the overall accuracy of the proposed ML models. …”
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Article -
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Evaluation of postgraduate academic performance using artificial intelligence models
Published 2022“…Exploring other variables such as research activities, marital status, and living conditions would have improved the overall accuracy of the proposed ML models. …”
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Article -
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Dengue classification system using clonal selection algorithm / Karimah Mohd
Published 2012“…This project can be improved by making a comparative study on Artificial Immune System and other techniques or algorithms used to solve dengue classification problems.…”
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Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms
Published 2005“…The comparison showed that, the accuracy of the unsupervised classification map with value of 88.4% that was generated by using the cluster labelling algorithm was slightly more than the maximum-likelihood supervised classification map with value of 87.5%. …”
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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…The performance of ANNs depend on many factors, including the network structure, the selection of activation function, the learning rate of the training algorithm, and initial synaptic weight values, the number of input variables, and the number of units in the hidden layer. …”
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