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Improved Boosted Decision Tree Algorithms by Adaptive Apriori and Post-Pruning for Predicting Obstructive Sleep Apnea by Sim, Doreen Ying Ying, Teh, Chee Siong, Ahmad Izuanuddin, Ismail

“…The improved version of Boosted Decision Tree algorithm, named as Boosted Adaptive Apriori post-Pruned Decision Tree (Boosted AApoP-DT), was developed by referring to Adaptive Apriori (AA) properties and by using post-pruning technique. …”
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Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

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Genetic algorithms for VLSI micro-Cell layout area optimization based on binary tree by Hasliza, A. Rahim@Samsuddin, Ab Al-Hadi, Ab Rahman, R. Badlishah, Ahmad, 'Aini Syuhada, Md Zain, M.I., Ahmad, Wan Nur Suryani Firuz, Wan Arrifin

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A genetic algorithm approach to VLSI macro cell non-slicing floorplans using binary tree by Rahim At Samsuddin, H. A., Ab. Rahman, A. A. H., Andaljayalakshmi, G., Ahmad, R. B.

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Adaptive rapidly-exploring-random-tree-star (Rrt*) -Smart: algorithm characteristics and behavior analysis in complex environments by Jauwairia Nasir, Fahad Islam, Yasar Ayaz

“…Navigation experiments using the real Pioneer 3-AT Mobile Robot provide a proof of the concept.…”
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A genetic algorithm approach to VLSI macro cell non-slicing floorplans using binary tree by Hasliza, A. Rahim@Samsuddin, Ab Rahman, A. A H, Andaljayalakshmi, G., R. Badlishah, Ahmad, Wan Nur Suryani Firuz, Wan Ariffin

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A genetic algorithm approach to VLSI macro cell non-slicing floorplans using binary tree by Hasliza, A. Rahim@Samsuddin, Ab Al-Hadi, Ab Rahman, Andaljayalakshmi, G., R. Badlishah, Ahmad, Wan Nur Suryani Firuz, Wan Arrifin

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Disparity map algorithm using hierarchical of bitwise pixel differences and segment-tree from stereo image by Zainal Azali, Muhammad Nazmi

“…The adaptability of the algorithm is demonstrated through a 3D surface reconstruction using a final disparity map. …”
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Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles by Amirruddin, Amiratul Diyana, Muharam, Farrah Melissa, Ismail, Mohd Hasmadi, Tan, Ngai Paing, Ismail, Mohd Firdaus

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A hybrid interpretable deep structure based on adaptive neuro‑fuzzy inference system, decision tree, and K‑means for intrusion detection by Jia, Lu, Yin Chai, Wang, Chee Siong, Teh, Xinjin, Li, Liping, Zhao, Fengrui, Wei

“…Meanwhile, for improving the efciency of training and predicting, Pearson Correlation analysis, standard deviation, and a new adaptive K-means are used to select attributes and make fuzzy interval decisions. …”
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Developing an intelligent system to acquire meeting knowledge in problem-based learning environments by Chiang, A., Baba, M.S.

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Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization by Hussain Mohammad Yousef Abu Dalbouh

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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Investigating the Application of Artificial Intelligence for Earthquake Prediction in Terengganu by Suzlyana Marhain binti Masrizan

Detection and classification of conflict flows in SDN using machine learning algorithms by Mutaz Hamed Hussien Khairi, Sharifah Hafizah Syed Ariffin, Nurul Mu'azzah Abdul Latiff, Kamaludin Mohamad Yusof, Mohamed Khalafalla Hassan, Fahad Taha Al-Dhief, Mosab Hamda, Suleman Khan, Muzaffar Hamzah

“…Using a range flows from 1000 to 100000 with an increment of 10000 flows per step in two network topologies namely, Fat Tree and Simple Tree Topologies, that were created using the Mininet simulator and connected to the Ryu controller, the performance of the proposed algorithms was evaluated for efficiency and effectiveness across a variety of evaluation metrics. …”
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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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Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy by Rahman, Sam Matiur, Ali, Md. Asraf, Altwijri, Omar, Alqahtani, Mahdi, Ahmed, Nasim, Ahamed, Nizam Uddin

“…Therefore, we aimed to classify six classes of freshly excised tissues from a set of electrical impedance measurement variables using five ensemble-based machine learning (ML) algorithms, namely, the random forest (RF), extremely randomized trees (ERT), decision tree (DT), gradient boosting tree (GBT) and AdaBoost (Adaptive Boosting) (ADB) algorithms, which can be subcategorized as bagging and boosting methods. …”
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A Proposed False Report Identification Algorithm for a Mobile Application in the IoT Environment by Rajoo, S, Magalingam, P, Idris, NB, Samy, GN, Maarop, N, Shanmugam, B, Perumal, S

“…The algorithm is designed and developed in R Studio and we built a framework to show how the algorithms can be adapted into a reporting service application. …”

E4ML: Educational Tool for Machine Learning by Sainin, Mohd Shamrie, Siraj, Fadzilah

“…There are various types of machine learning algorithms with certain processes taken by the algorithm.In teaching of the machine learning algorithms, such processes need to be explained especially to the beginner in introductory level.This paper discusses the development the tool that addresses the process by certain algorithm to produce a hypothesis or output based on given data.This tool can also be used in teaching and learning purposes.The explanation of processes by the algorithms is demonstrated through simple simulation.The source of the algorithms was adapted from Mitchell book [1] that cover popular algorithms in machine learning for teaching and learning such as Concept Learning, Decision Tree, Bayesian Learning, Neural Networks, and Instance based Learning.The tool also used several classes of Weka (Waikato Environment for Knowledge Analysis) as a basis for the design and implementation of the new tool that focuses on explaining the processes taken by certain algorithm.…”
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An Educational Tool Aimed at Learning Metaheuristics by Kader, Md. Abdul, Jamaluddin, Jamal A., Kamal Z., Zamli

“…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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