Applied Predictive Analytics
The Applied Predictive Analytics research group focuses on enhancing predictive analytics techniques to better support decision-making across various sectors, including healthcare, finance, and environmental management. Our team is composed of professionals skilled in data science, machine learning, and statistical analysis, working together to create reliable predictive models and algorithms.
Our efforts are divided into three main areas: Predictive Modeling, Big Data Analytics, and Real-Time Decision Systems. In Predictive Modeling, we develop models that forecast trends and outcomes, such as disease progression in healthcare or maintenance needs in manufacturing. Big Data Analytics involves managing and extracting insights from large datasets, improving data handling and processing capabilities. Lastly, Real-Time Decision Systems focuses on integrating analytics directly into operational workflows, enabling swift responses to changing conditions.
We collaborate with both industrial partners and academic institutions to ensure our research is applicable and impactful. Through continuous engagement, we strive to contribute valuable advancements to the field of predictive analytics. This approach helps us stay practical and relevant, ensuring our research directly contributes to improvements in various industries.
Leader:
Ir. Dr. Mazlina Mamat
Members:
- Associate Professor Dr. Jamal Dargham,
- Associate Professor
- Ir. Dr. Farrah Wong Hock Tze,
- Dr. Rosalyn R. Porle
Recent Publications:
- Harrison, H., Mamat, M., Wong, F., Yew, H. T. (2024). A novel and refined contactless user feedback system for immediate on-site response collection. International Journal of Advanced Computer Science and Applications (IJACSA), 15(7), 241 - 248.
- Yew, H. T., Mamat, M., Dargham, J. A., Chung, S. K., Moung, E. G. (Eds.). (2024). Internet of Things and Artificial Intelligence for Smart Environments. Springer, Singapore.
- Mamat, M., Mustakim, R., Johari, N. (2024). Artificial intelligence: Offline, online, and reinforcement learning approaches in time series air pollutant index prediction, Internet of Things and Artificial Intelligence for Smart Environments. Springer, F2949, 83-101.
- Mustakim, R., Mamat, M., Wong, F., Mohamad Dasuki, S. N. A. S., Johari, N. (2024). Artificial intelligence in time series prediction, classification, and sequence-to-sequence problems. Internet of Things and Artificial Intelligence for Smart Environments. Springer, F2949, 103-118.
- Johari, N., Mamat, M., Yew, H. T., Kiring, A. (2023). Effect of distance and direction on distress keyword recognition using ensembled bagged trees with a ceiling-mounted omnidirectional microphone. International Journal of Advanced Computer Science and Applications (IJACSA), 14(6), 283-290.
- Mustakim, R., Mamat, M., & Yew, H. T. (2022). Towards on-site implementation of multi-step air pollutant index prediction in Malaysia industrial area: Comparing the NARX neural network and support vector regression. Atmosphere, 13(11), 1787.
Collaboration:
Associate Professor Dr. Wan Mimi Wan Zaki,
Predictive Analytic Research,
Department of Electrical, Electronic and Systems Engineering,
Universiti Kebangsaan Malaysia.
Our efforts are divided into three main areas: Predictive Modeling, Big Data Analytics, and Real-Time Decision Systems. In Predictive Modeling, we develop models that forecast trends and outcomes, such as disease progression in healthcare or maintenance needs in manufacturing. Big Data Analytics involves managing and extracting insights from large datasets, improving data handling and processing capabilities. Lastly, Real-Time Decision Systems focuses on integrating analytics directly into operational workflows, enabling swift responses to changing conditions.
We collaborate with both industrial partners and academic institutions to ensure our research is applicable and impactful. Through continuous engagement, we strive to contribute valuable advancements to the field of predictive analytics. This approach helps us stay practical and relevant, ensuring our research directly contributes to improvements in various industries.
Leader:
Ir. Dr. Mazlina Mamat
Members:
- Associate Professor Dr. Jamal Dargham,
- Associate Professor
- Ir. Dr. Farrah Wong Hock Tze,
- Dr. Rosalyn R. Porle
Recent Publications:
- Harrison, H., Mamat, M., Wong, F., Yew, H. T. (2024). A novel and refined contactless user feedback system for immediate on-site response collection. International Journal of Advanced Computer Science and Applications (IJACSA), 15(7), 241 - 248.
- Yew, H. T., Mamat, M., Dargham, J. A., Chung, S. K., Moung, E. G. (Eds.). (2024). Internet of Things and Artificial Intelligence for Smart Environments. Springer, Singapore.
- Mamat, M., Mustakim, R., Johari, N. (2024). Artificial intelligence: Offline, online, and reinforcement learning approaches in time series air pollutant index prediction, Internet of Things and Artificial Intelligence for Smart Environments. Springer, F2949, 83-101.
- Mustakim, R., Mamat, M., Wong, F., Mohamad Dasuki, S. N. A. S., Johari, N. (2024). Artificial intelligence in time series prediction, classification, and sequence-to-sequence problems. Internet of Things and Artificial Intelligence for Smart Environments. Springer, F2949, 103-118.
- Johari, N., Mamat, M., Yew, H. T., Kiring, A. (2023). Effect of distance and direction on distress keyword recognition using ensembled bagged trees with a ceiling-mounted omnidirectional microphone. International Journal of Advanced Computer Science and Applications (IJACSA), 14(6), 283-290.
- Mustakim, R., Mamat, M., & Yew, H. T. (2022). Towards on-site implementation of multi-step air pollutant index prediction in Malaysia industrial area: Comparing the NARX neural network and support vector regression. Atmosphere, 13(11), 1787.
Collaboration:
Associate Professor Dr. Wan Mimi Wan Zaki,
Predictive Analytic Research,
Department of Electrical, Electronic and Systems Engineering,
Universiti Kebangsaan Malaysia.
