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An improved algorithm for iris classification by using support vector machine and binary random machine learning by Kamarulzalis, Ahmad Haadzal

“…In machine learning, there are three type of learning branch that can used in classification procedures for data mining. …”
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Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves by Lia, Kamelia

“…The proposed method integrates colour and texture feature-based image analysis with machine learning algorithms for classification. The process begins with acquiring image data, which is categorized into four classes: nitrogen (N) deficiency, phosphorus (P) deficiency, potassium (K) deficiency, and normal. …”
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Wearable based-sensor fall detection system using machine learning algorithm by Ishak, Anis Nadia, Habaebi, Mohamed Hadi, Yusoff, Siti Hajar, Islam, Md. Rafiqul

“…Then, a Machine Learning Algorithm (MLA) is used to train and test the data before a classifier is used to classify the new incoming dataset. …”
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Sentiment Analysis of Sexual Harassment in Malaysia on Twitter Using Machine Learning Algorithms by Nurellezia, Suleiman

“…The transformed data is then modelled using machine learning algorithms such as Naïve Bayes classifier and Support Vector Machine to predict the overall sentiment of tweets, in which the finding depicted an overall positive sentiment surrounding the issue. …”
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Development Of Machine Learning User Interface For Pump Diagnostics by Lee, Zhao Yang

“…The result from the SVM algorithms will be used as database for the machine learning in Microsoft Azure. …”
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Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization by Nanyonga Aziida, Sorayya Malek, Firdaus Aziz, Khairul Shafiq Ibrahim, Sazzli Kasim

“…Feature selection methods such as Boruta, Random Forest (RF), Elastic Net (EN), Recursive Feature Elimination (RFE), learning vector quantization (LVQ), Genetic Algorithm (GA), Cluster Dendrogram (CD), Support Vector Machine (SVM) and Logistic Regression (LR) were combined with RF, SVM, LR, and EN classifiers for 30-day mortality prediction. …”
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Predictive analytics for the sentiment of malaysian place of interest using machine learning models by Qiryn Adriana, Kharul Zaman

“…The data was then divided into training and testing sets, and was trained using three different supervised learning algorithms, namely Support Vector Machine, Random Forest, and Naive Bayes. …”
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The formulation of a transfer learning pipeline for the classification of the wafer defects by Lim, Shi Xuen

“…However, limitations such as robustness and difficulty in setting up the parameters required for image processing algorithm encourages the investigation in using Deep learning classification in detecting the wafer defects. …”
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High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor by Abdul Rashid, Raghdah Rasyidah, Shaharudin, Shazlyn Milleana, Sulaiman, Nurul Ainina Filza, Zainuddin, Nurul Hila, Mahdin, Hairulnizam, Mohd Najib, Summayah Aimi, Hidayat, Rahmat

“…These factors include identifying relevant atmospheric features contributing to rainfall, addressing missing data, and developing a significant model to predict daily rainfall intensity using appropriate machine-learning techniques. The Principal Component Analysis (PCA) technique was employed to choose relevant environmental variables as input for the machine learning model, and various imputation methods were utilized to manage missing data, such as mean imputation and the KNN algorithm. …”
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Exploration of COVID‑19 data in Malaysia through mapper graph by Carey Ling, Yu Fan, Piau, Phang, Liew, Siaw Hong, Vivek Jason, Jayaraj, Benchawan, Wiwatanapataphee

“…To keep with the expanding quantity and complexity of data while employing minimal assumptions, a topological data analysis tool known as the Mapper algorithm is used to explore Malaysia’s daily confirmed cases, deaths, and vaccination data from the onset of the pandemic to June 2022 via data visualization and clustering. …”
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Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network by Zafar, R., Kamel, N., Naufal, M., Malik, A.S., Dass, S.C., Ahmad, R.F., Abdullah, J.M., Reza, F.

“…General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). …”
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Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh by Sai , Chong Yeh

“…Supervised and unsupervised machine learning algorithms particularly the Support Vector Machine (SVM) and Density Based Spatial Clustering of Application with Noise (DBSCAN) are used in this study. …”
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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

“…The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. …”
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Malware Detection using Deep Learning (DL) by Chowdhury Sajadul Islam, Madihah Mohd Saudi, Nur Hafiza Zakaria, Muji Setiyo

Fast shot boundary detection based on separable moments and support vector machine by Idan, Zinah N., Abdulhussain, Sadiq H., Mahmmod, Basheera M., Al-Utaibi, Khaled A., Syed Abdul Rahman Al-, Syed Abdul Rahman Al-Hadad, Sait, Sadiq M.

“…As a result, the computational cost is reduced in the subsequent stages. Finally, machine learning statistics based on the support vector machine is implemented to detect the cut transitions. …”
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Sentiment analysis on the place of interest in Malaysia by Qiryn Adriana, Khairul Zaman, Wan Nur Syahidah, Wan Yusoff, Qistina Batrisyia, Azman Shah

“…Pre-processing techniques and Natural Language Processing (NLP) methods were applied to handle missing values and prepare the text data for analysis. The dataset was then split into training and testing sets, and three supervised learning algorithms which are Support Vector Machine, Random Forest, and Naive Bayes were employed to evaluate the sentiment analysis models. …”
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Diabetes risk prediction system and data visualization / Azizah Mohamad Imran and Hawa Mohd Ekhsan by Mohamad Imran, Azizah, Mohd Ekhsan, Hawa

“…To determine Diabetes, the prediction model used and compared different machine learning algorithms such as Logistic Regression (LR) and Support Vector Machine (SVM). …”
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Predicting the onset of acute coronary syndrome events and in-hospital mortality using machine learning approaches / Song Cheen by Song , Cheen

“…Understanding this association is crucial given the increasing prevalence of air pollution in many regions, particularly in Malaysia, which is affected by air pollution. This study used a comprehensive methodology to investigate the relationship between air pollution and ACS patient outcomes utilizing machine learning (ML) algorithms, including: 1) Linear Regression, 2) Logistic Regression, 3) Support Vector Machine (SVM), 4) Random Forest (RF), 5) XGBoost, 6) Naïve Bayes (NB), and 7) Stacked Ensemble ML utilizing data from the National Cardiovascular Disease Database (NCVD) Malaysia registry and air quality data from the Department of Environment (DOE) Malaysia. …”
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EEG-Based Person Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique by Liew, Siaw Hong

“…The IncFRNN algorithm is able to control the size of training pool using predefined window size threshold. …”
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Bayesian network of influence of sociodemographic variables on dengue related knowledge, attitude, and practices in selected areas in Selangor, Malaysia by Ajibola, Lamidi-Sarumoh Alaba

“…The data collected was used to learn the structure of BN via some known algorithms using R programming language. …”
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