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Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management by Latif S.D., Ahmed A.N.

Footwear quality evaluation using decision tree and logistic regression models by Tan, Swee Choon

“…The analysis showed that Decision Tree with Gini algorithm (three branches) in the first method prevails against the other methods with misclassification rate of 0.1307. …”
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Hardware development of autonomous mobile robot based on actuating lidar by Mohd Romlay, Muhammad Rabani, Mohd Ibrahim, Azhar, Toha, Siti Fauziah, Rashid, Muhammad Mahbubur, Ahmad, Muhammad Syahmi

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Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review by Yafouz A., Ahmed A.N., Zaini N., El-Shafie A.

The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Recommendation System Model For Decision Making in the E-Commerce Application by Mohammed Mahmudur Rahman

Application of Machine Learning for Daily Forecasting Dam Water Levels by Almubaidin, Ahmed, Winston C.A.A., El-Shajie A.

“…Despite being the most common approach for defining hydrologic processes and implementing physical system changes, the physics-based model has some practical limitations. …”

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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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.

Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach by Mustakim, Nurul Ain

“…It is organized into three phases: preliminary investigation, implementation and analysis, and validation. The descriptive analysis examines purchasing behavior through correlation and regression analyses, while the predictive model uses decision trees (J48, Random Tree, REPTree), rule-based algorithms (JRip, OneR, PART), and clustering (K-Means) to identify patterns and predict trends. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Oral Dictionary by Md. Radzi, Ashylla

“…Process activities in Oral Dictionary application will then follow the incremental model which allows system to be reworked in response to change request. Since the output speed is critical, binary search tree algorithm is chosen as best data structure to be implemented in this application to improve the searching time performance. …”
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Machine learning techniques for flood forecasting by Hadi F.A.A., Sidek L.M., Salih G.H.A., Basri H., Sammen S.Sh., Dom N.M., Ali Z.M., Ahmed A.N.

“…2030). ML algorithms were Logistic Regression, K-Nearest neighbors, Support Vector Classifier, Naive Bayes, Decision tree, Random Forest, and Artificial Neural Network. …”

Evaluation of XML documents queries based on native XML database by Lazim, Raghad Yaseen

“…Furthermore, it is usually very difficult after insertion to change the relational schema due to XML schema changes. …”
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Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques by Mas Ira Syafila, Mohd Hilmi Tan, Mohd Faizal, Jamlos, Ahmad Fairuz, Omar, Kamarulzaman, Kamarudin, Mohd Aminudin, Jamlos

“…Four different ML algorithms are tested for prediction of G. boninense infection: K-Nearest Neighbour (kNN), Naïve Bayes (NB), Support Vector Machine (SVM) and Decision Tree (DT) are tested which depicted DT algorithm achieves a satisfactory overall performance with high accuracy up to 93.1% and F1-score of 92.6% compared to other algorithms. …”
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