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Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for predicting Obstructive Sleep Apnea by Doreen Ying Ying, Sim, Chee Siong, Teh, Ahmad Izuanuddin, Ismail

“…The Pruned-Associative-Rule-Mined Decision Trees (PARM-DT) developed by adopting pre-pruning techniques on tree depth, minimum leaf and/or parent node size observations and maximum number of tree splits, based on Apriori and/or Adaptive Apriori (AA) frameworks, is boosted to achieve better predictive accuracies. …”
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Phylogenetic tree classification system using machine learning algorithm by Tan, Jia Kae

“…A study is conducted to develop an automated phylogenetic tree image classification system by using machine learning algorithm. …”
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Developing emergency application for LRT passengers with decision tree algorithm (RailAlert!) / Ezrren Natasha Baddru’l-Sham … [et al.] by Baddru’l-Sham, Ezrren Natasha, Abdul Rahid @ Abdul Rashid, Nurul Najwa, Ab Malek, Ezzad, Pahruradzi, Salman, Subano, Muhammad Rizzuan Ariff

“…With geolocation and decision tree algorithms, this project aims to design and develop an emergency application for LRT passengers. …”
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Developing emergency application for LRT passengers with decision tree algorithm (RailAlert!) / Ezrren Natasha Baddru’l-Sham … [et al.] by Baddru’l-Sham, Ezrren Natasha, Abdul Rahid @ Abdul Rashid, Nurul Najwa, Ab Malek, Ezzad, Pahruradzi, Salman, Subano, Muhammad Rizzuan Ariff

“…With geolocation and decision tree algorithms, this project aims to design and develop an emergency application for LRT passengers. …”
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Cancer Prediction Based On Data Mining Using Decision Tree Algorithm by Loh, Kin Ming

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An optimized attack tree model for security test case planning and generation by Omotunde, Habeeb, Ibrahim, Rosziati, Ahmed, Maryam

“…Given the huge risks web applications face due to incessant cyberattacks, a proactive risk strategy such as threat modeling is adopted. It involves the use of attack trees for identifying software vulnerabilities at the earliest phase of software development which is critical to successfully protect these applications. …”
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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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Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking by Boroujeni, Farsad Zamani

“…Over the last decade, many algorithms have been developed to address this problem. …”
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Image Based Oil Palm Tree Crowns Detection by Muhammad Afif Zakwan, Zaili

“…This system adopting some features from existing work to identify the ideas and methods that indicate the exact position and detecting the oil palm tree crowns on the acquired images. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Prediction of employee promotion using hybrid sampling method with machine learning architecture / Shahidan Shafie, Soek Peng Ooi and Khai Wah Khaw by Shafie, Shahidan, Soek, Peng Ooi, Khai, Wah Khaw

“…In this study, there are eight machine learning algorithms have been used, such as Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, Naïve Bayes, Adaptive Boosting Classifier, and Extreme Gradient Boost. …”
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Monitoring Trends of Land Use and Land Cover Changes in Rajang River Basin by Oad, V.K., Ul Mustafa, M.R., Takaijudin, H.B., Nabi, G., Hussain, M.

“…Supervised classification with the MaximumLikelihood-Algorithm technique was adopted for monitoring the LULC changes using Geographic Information System (GIS) and ERDAS Imagine tools. …”
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Determination of tree stem volume : A case study of Cinnamomum by Noraini Abdullah

“…Modelling of trees has attracted scientific research in various fields and disciplines since trees and forests play very important roles in the global system. …”
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Testing the use of machine learning for heritage property valuation / Junainah Mohamad, Nur Shahirah Ja’afar and Suriatini Ismail by Mohamad, Junainah, Ja’afar, Nur Shahirah, Ismail, Suriatini

“…Several machine learning algorithms have been developed and tested, including random forest regressor, decision tree regressor, lasso, ridge and, linear regression. …”
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Performance evaluation of intrusion detection system using selected features and machine learning classifiers by Raja Mahmood, Raja Azlina, Abdi, AmirHossien, Hussin, Masnida

“…These evolutionary-based algorithms are known to be effective in solving optimization problems. …”
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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. …”
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Development of a modified adaptive protection scheme using machine learning technique for fault classification in renewable energy penetrated transmission line by Olufemi, Osaji Emmanuel

“…The Random Tree standalone ML-AP relay model presented the best performing models from the ML-APS relay model with the best average performance for the correctly classified fault types of 97.61 % at 5 % significance level above other ML algorithms. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Classification models for higher learning scholarship award decisions by Wirawati Dewi Ahmad, Azuraliza Abu Bakar

“…A dataset of successful and unsuccessful applicants was taken and processed as training data and testing data used in the modelling process. Five algorithms were employed to develop a classification model in determining the award of the scholarship, namely J48, SVM, NB, ANN and RT algorithms. …”
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Anomaly detection in network traffic using machine learning by Amir Muhammad Hafiz, Othman, Mohd Faizal, Ab Razak, Mohd Izham, Mohd Jaya, Nurul Azma, Abdullah, Alanda, Alde

“…The study objective is to analyze the effectiveness and reliability of implementing machine learning techniques in identifying anomalies in network traffic. Five (5) algorithms, which are Adaptive Boosting (AdaBoost), K-Nearest Neighbor (KNN), Random Forest (RF), Multi-Layer Perceptron (MLP), and Decision Trees (ID3) are systematically evaluated using the dataset CICIDS2017, a comprehensive and widely adopted benchmark for network traffic detection research. …”
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