Search Results - (( variable educational tree algorithm ) OR ( java adaptation optimization algorithm ))

Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

Prediction of electronic cigarette and vape use among Malaysian: decision tree analysis by Kartiwi, Mira, Ab Rahman, Jamalludin, Nik Mohamed, Mohamad Haniki, Draman, Samsul, Ab Rahman, Norny Syafinaz

“…Results: By using the ID3 algorithm, it is possible to consider the relationship among variables and to identify the most informative variables for predicting the classification of the instance. …”
Get full text
Get full text
Get full text

Characteristics of electronic cigarette and vape users in Malaysia: Lessons from decision tree analysis by Kartiwi, Mira, Nik Mohamed, Mohamad Haniki, Ab Rahman, Jamalludin, Draman, Samsul, Ab Rahman, Norny Syafinaz

“…Several predictor variables included in this study were: seven demographics variables (i.e., age, gender, race, residence, marital, occupation and education) and twenty variables on the perception of ECV use. …”
Get full text
Get full text
Get full text

Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance by Alsariera Y.A., Baashar Y., Alkawsi G., Mustafa A., Alkahtani A.A., Ali N.

Predictive modelling of student academic performance using machine learning approaches : a case study in universiti islam pahang sultan ahmad shah by Nurul Habibah, Abdul Rahman

“…Recently, predictive analytics research has grown in popularity in higher education because it provides helpful information to educators and potentially assists them in enhancing student achievement. …”
Get full text
Get full text

Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

“…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
Get full text
Get full text
Get full text
Get full text

Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud by Shariff, S. Sarifah Radiah, Hud, Hady

Get full text
Get full text

Artificial Intelligence (AI) to predict dental student academic performance based on pre-university results by Ahmad Amin, Afifah Munirah, Abdullah, Adilah Syahirah, Lestari, Widya, Sukotjo, Cortino, Utomo, Chandra Prasetyo, Ismail, Azlini

“…Logistic Regression (LR) is the most effective algorithm for forecasting student success in Year 1 with accuracy 0.88 and Decision Tree (DT) in Year 3 with accuracy 0.9. …”
Get full text
Get full text

Tangible interaction learning model to enhance learning activity processes among children with dyslexia by Jamalai@Jamali, Siti Nurliana

Get full text
Get full text
Get full text

Ensemble learning for multidimensional poverty classification by Azuraliza Abu Bakar, Rusnita Hamdan, Nor Samsiah Sani

“…Analysis of this study showed that Per Capita Income, State, Ethnic, Strata, Religion, Occupation and Education were found to be the most important variables in the classification of poverty at a rate of 99% accuracy confidence using Random Forest algorithm.…”
Get full text
Get full text
Get full text

An application of predicting student performance using kernel k-means and smooth support vector machine by Sajadin, Sembiring

“…In this study, psychometric factors used as predictor variables, thereare Interest, Study Behavior, Engaged Time, Believe, and Family Support.The rulemodel developed using Kernel K-means Clustering and Smooth Support Vector MachineClassification.Both of these techniquesbased on kernel methodsand relativelynew algorithms of data mining techniques, recently received increasingly popularity in machine learning community. …”
Get full text
Get full text

Enhancing understanding of programming concepts through physical games by Raja Yusof, Raja Jamilah, Habib, Ahsan

Get full text
Get full text
Get full text

Performance Prediction of Compulsory Subjects and Recommendation of Subject Options for China’s New College Entrance Examination by Long, Wang

“…Four machine learning algorithms: Naïve Bayes (NB), Decision Tree (DT), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs) were evaluated using accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (MCC). …”
Get full text
Get full text
Get full text

Factors with retirement behaviour among retirees and pre-retirees identified with a machine learning method / Muhammad Aizat Zainal Alam by Muhammad Aizat , Zainal Alam

“…This study uses 3,067 responses which are then be coupled with a machine learning methodology (ranging from Naïve Bayesian, Generalised Linear Model, Logistic Regression, Artificial Neural Network, Decision Tree, Random Forest, and Gradient Boosted Trees) via RapidMiner Studio to expand the understanding of how categories of wealth and expenditures can affect retirement behaviour, given the increasingly important role of machine learning algorithms within the context of behavioural economics where it has been demonstrated to describe patterns and relationships in behavioural data better than standard statistical analysis. …”
Get full text
Get full text
Get full text