Search Results - (( variable learning bayes algorithm ) OR ( java application learning algorithm ))
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Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
Published 2007“…In the selection phase, a new variable length genetic algorithm is applied to select the lexical cues. …”
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Predicting Sudden Deaths Following Myocardial Infarction in Malaysia Using Machine Learning Classifiers
Published 2024journal::journal article -
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Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit
Published 2025“…This study demonstrates the performances of different machine learning algorithms in the classification of multiple organ failures. …”
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Machine learning classifications of multiple organ failures in a malaysian intensive care unit
Published 2024“…Several machine learning algorithms which are decision tree, linear discriminant, naïve Bayes, support vector machines, k-nearest neighbor, AdaBoost, and random forest were used for the classification. …”
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An Educational Tool Aimed at Learning Metaheuristics
Published 2020“…In this paper, we introduce an education tool for learning metaheuristic algorithms that allows displaying the convergence speed of the corresponding metaheuristic upon setting/changing the dependable parameters. …”
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A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment
Published 2013“…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models
Published 2023“…The selected wavelengths were incorporated into machine learning models, including Naïve Bayes, random forest, and support vector machine (SVM). …”
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Hypertension Prediction in Adolescents Using Anthropometric Measurements: Do Machine Learning Models Perform Equally Well?
Published 2022“…However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combination with other biophysical and lifestyle variables. …”
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Bayesian Network of Traffic Accidents in Malaysia
Published 2019“…By using Hill Climb (HC) and Tabu algorithms, the structure of the data was learnt and their relationship is estimated through the conditional probability based on the Bayes theorem. …”
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Predictive modelling of student academic performance using machine learning approaches : a case study in universiti islam pahang sultan ahmad shah
Published 2024“…The predictive algorithm can also be added to the Learning Management System along with a dashboard so that it is easier to do analyses in the future.…”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection
Published 2018“…Real-Valued Negative Selection Algorithm with Variable-Sized Detectors (V-Detectors) is an offspring of AIS and demonstrated its potentials in the field of anomaly detection. …”
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A Stacked Ensemble Deep Learning Approach For Imbalanced Multi-class Water Quality Index Prediction
Published 2024journal::journal article -
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Improving Classification Accuracy of Scikit-learn Classifiers with Discrete Fuzzy Interval Values
Published 2020“…Understanding machine learning (ML) algorithm from scratch is time consuming. …”
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Multi-label risk diabetes complication prediction model using deep neural network with multi-channel weighted dropout
Published 2025“…Seven machine learning models—Artificial Neural Network (ANN), Random Forest (RF), Decision Tree (DTT), k-Nearest Neighbors (k-NN), Naïve Bayes (NB), Support Vector Machine (SVM), and Deep Neural Network (DNN)—were used for multi-label classification of the complications. …”
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