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Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data by Ong, Song Quan, Pradeep Isawasan, Ahmad Mohiddin Mohd Ngesom, Hanipah Shahar, As’malia Md Lasim, Gomesh Nair

“…Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. …”
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Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates by Ehteram M., Graf R., Ahmed A.N., El-Shafie A.

Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms by Murti M.A., Junior R., Ahmed A.N., Elshafie A.

New Approach for E-Commerce Stock Prices Prediction : Combination of Machine Learning and Technical Analysis by Kelvin Lee, Yong Ming, Mohamad, Jais, Pick Soon, Lee

“…The signals emitted by the technical indicators are used as the features for two machine learning algorithms in predicting the stocks movements. …”
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Optimization of turning parameters using ant colony optimization by Mohamad Nazri, Semoin

“…The project objectives are to develop Ant Colony Optimization (ACO) algorithm for CNC turning process and to optimize turning parameters for minimized production cost per unit. …”
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A comparative analysis of machine learning algorithms for diabetes prediction by Alansari, Waseem Abdulmahdi, Masnizah Mohd

“…The methodology involves data collection, pre-processing, and training the algorithms using k-fold cross-validation. The results indicate that pre-processing steps and dataset characteristics significantly impact algorithm performance. …”
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Machine learning approach for automated optical inspection of electronic components by Lim, Siew Kee

“…The factor that affecting the confidence level of the supervised machine learning algorithm is discussed. …”
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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 attributes are the number of households, area, state, strata, race, highest certificate, marital status, gender, housing, income, total expenditure, and category as attributes class. The model development employs five machine learning algorithms namely decision tree, Naive Bayes, Neural network, Support Vector Machines, Nearest Neighbour. …”
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Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China by Sattar Hanoon M., Najah Ahmed A., Razzaq A., Oudah A.Y., Alkhayyat A., Feng Huang Y., kumar P., El-Shafie A.

“…In this study, different supervised and unsupervised machine learning algorithms are proposed: artificial neural network (ANN), AutoRegressive Integrated Moving Aveage (ARIMA) and support vector machine (SVM). …”

Fine-scale predictive modeling of Aedes mosquito abundance and dengue risk indicators using machine learning algorithms with microclimatic variables by Che Dom, Nazri, Mohd Hardy Abdullah, Nur Athen, Dapari, Rahmat, Salleh, Siti Aekbal

“…Integrating time-lagged microclimatic variables into machine learning frameworks enhances the predictive accuracy of dengue risk indicators at a fine spatial scale. …”
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Operating a reservoir system based on the shark machine learning algorithm by Allawi M.F., Jaafar O., Mohamad Hamzah F., Ehteram M., Hossain M.S., El-Shafie A.

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

“…The results indicate that random forest regressor is the best machine learning algorithms and can be used for heritage property valuation.…”
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The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction by Adnan R.M., Kisi O., Mostafa R.R., Ahmed A.N., El-Shafie A.

Comparison of supervised machine learning algorithms for malware detection / Mohd Faris Mohd Fuzi ... [et al.] by Mohd Fuzi, Mohd Faris, Mohd Shahirudin, Syamir, Abd Halim, Iman Hazwam, Jamaluddin, Muhammad Nabil Fikri

“…The results indicated that the Decision Tree and Random Forest algorithms provided the best detection accuracy at 96%, followed by the K-NN algorithm at 95%. …”
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Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction by Zuriani, Mustaffa

“…Results showed that the eABC-LSSVM possess lower prediction error rate as compared to eight hybridization models of LSSVM and Evolutionary Computation (EC) algorithms. In addition, the proposed algorithm is compared to single prediction techniques, namely, Support Vector Machines (SVM) and Back Propagation Neural Network (BPNN). …”
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Optimization of multi-holes drilling toolpath using tiki-taka algorithm by Norazlina, Abdul Rahman

“…The research methodology involves problem modeling, algorithm development, and validation. The TSP concept formulated the MDMT problem, representing holes as cities and tools as a salesman in finding the shortest path to develop a mathematical model or fitness function. …”
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Polymorphic malware detection based on dynamic analysis and supervised machine learning / Nur Syuhada Selamat by Selamat, Nur Syuhada

“…The benefit of this work indicated that the implementation of a feature selection technique plays an important role in machine learning algorithms to increase the performance of detection.…”
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Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms by Essam Y., Huang Y.F., Ng J.L., Birima A.H., Ahmed A.N., El-Shafie A.

Waste management using machine learning and deep learning algorithms by Sami, Khan Nasik, Amin, Zian Md Afique, Hassan, Raini

“…For our research we did the comparisons between three Machine Learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Decision Tree, and one Deep Learning algorithm called Convolutional Neural Network (CNN), to find the optimal algorithm that best fits for the waste classification solution. …”
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Flexible job shop scheduling using priority heuristics and genetic algorithm by Farashahi, Hamid Ghaani

“…In the next method, a genetic algorithm has been developed. It has been shown that proposed genetic algorithm with a reinforced initial population (GA2) has better efficiency compared to a proposed genetic algorithm with fully random initial population (GA0). …”
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