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Laptop price prediction using decision tree algorithm / Nurnazifah Abd Mokti by Abd Mokti, Nurnazifah

“…This research project focuses on developing a laptop price prediction model using the decision tree algorithm based on laptop specifications. …”
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A Hybrid Adaptive Leadership GWO Optimization with Category Gradient Boosting on Decision Trees Algorithm for Credit Risk Control Classification by Suihai, Chen, Chih How, Bong, Po Chan, Chiu

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Enhancing loan approval decision-making: an interpretable machine learning approach using LightGBM for digital economy development / Teuku Rizky Noviandy, Ghalieb Mutig Idroes and... by Noviandy, Teuku Rizky, Idroes, Ghalieb Mutig, Hardi, Irsan

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AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites by Alsariera, Y.A., Adeyemo, V.E., Balogun, A.O., Alazzawi, A.K.

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Enhance efficiency of answering XML keyword query using incompact structure of MCCTree by Sazaly, Ummu Sulaim

“…If the result cannot be used directly by the ranking method, the algorithm has an ineffective process. Moreover, if the ineffective process requires re-examining the original tree, the efficiency of the process of the algorithm will be reduced. …”
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Household overspending model amongst B40, M40 and T20 using classification algorithm by Zulaiha Ali, Othman, Azuraliza, Abu Bakar, Nor Samsiah, Sani, Jamaludin, Sallim

“…The results show that the decision tree through J48 algorithm has produced the easiest rule to be interpreted. …”
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Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids by Chiaverini, Luca, Macdonald, David W., Hearn, Andrew J., Kaszta, Zaneta, Ash, Eric, Bothwell, Helen M., Can, Ozgun Emre, Channa, Phan, Clements, Gopalasamy Reuben *, Haidir, Iding Achmad, Kyaw, Pyae Phyoe, Moore, Jonathan H., Rasphone, Akchousanh, Tan, Cedric Kai Wei, Cushman, Samuel A.

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Diamond price prediction using random forest algorithm / Nur Amirah Mohd Azmi by Mohd Azmi, Nur Amirah

“…Traditional methods struggle to model these complexities effectively, necessitating adoption of advanced algorithms to improve accuracy. The aim of this project is to develop a Diamond Price Prediction System using Random Forest, designed to accurately predict diamond prices based on attributes. …”
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Car dealership web application by Yap, Jheng Khin

“…The transfer learning algorithm pre-trained the River adaptive random forest regressor and classifier by transferring the tree structures and weights from the Scikit-learn fitted random forest regressor and classifier, respectively. …”
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Poverty risk prediction based on socioeconomic factors using machine learning approach by Mohd Zawari, Nur Farhana Adibah

“…Information gain was used in the feature selection and four classification algorithms namely, Logistic Regression, Random Forest, Decision Tree, and Gradient Boosted, were implemented and tested with the incorporation of 10-fold cross-validation and splitting 70:30 in WEKA. …”
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Text-based emotion prediction system using machine learning approach by Ahmad Fakhri, Ab. Nasir, Eng, Seok Nee, Chun, Sern Choong, Ahmad Shahrizan, Abdul Ghani, Anwar, P. P. Abdul Majeed, Asrul, Adam, Mhd, Furqan

“…Text-based emotion prediction system to interpret and understand human emotions was successfully developed.…”
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Prediction of Fetal Health Status Using Machine Learning by Naidile S, Saragodu, Shreedhara N, Hegde, Harprith, Kaur

“…We integrated a range of machine learning algorithms, including logistic regression, support vector machines, decision trees, and random forests, to train and test our model. …”
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Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data by Oyebayo, Olaniran Ridwan

“…Random Forests (RF) are ensemble of trees methods widely used for data prediction, interpretation and variable selection purposes. …”
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An improved diabetes risk prediction framework : An Indonesian case study by Sutanto, Daniel Hartono

“…In the learning section,Support Vector Machine and Artificial Neural Network were selected as suitable classification algorithms,while Gradient Boosted Tree was employed to interpret the rule based on the black box classifiers.Testing the framework involved Pima Indian Dataset as public dataset and Semarang Hospital Dataset as private dataset (800 patients’ data).In validating the DRPF,four case studies investigated Subject Matter Expert (SME) groups based on the agreement level.The questionnaire consists of a DRPF component,implementation of DRPF,and viability of DRPF.DRPF components were validated by the SMEs,whereby the group ascertained five highest risk factors:HbA1c,systole/diastole,blood glucose,and creatinine and blood urea nitrogen that were assigned by attribute weighting.Results from the questionnaire revealed an average agreement level of 80%. …”
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Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation by Pande C.B., Srivastava A., Moharir K.N., Radwan N., Mohd Sidek L., Alshehri F., Pal S.C., Tolche A.D., Zhran M.

“…Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. …”

A Systematic Approach to Transform Machine Learning Students� Performance Prediction Model into Preventive Procedures by Khan I., Ahmad A.R., Jabeur N., Mahdi M.N.

“…J48 decision tree prevails other models by achieving accuracy and F-measure of nearly 0.90. …”

Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization by Razali, Mohd Norhisham, Ibrahim, Norizuandi, Hanapi, Rozita, Mohd Zamri, Norfarahzila, Abdul Manaf, Syaifulnizam

“…Subjective assessments, numerous assessment factors, and difficulties in interpreting predictive mechanisms add to the complexity of the task. …”
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Beyond Grades - Predicting Programme Learning Outcomes with Multi-Output Regression in Malaysian Higher Education by Muhammad Noorazizi Abd Ghani, Istas Fahrurrazi Nusyirwan