The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models’ performance discloses the most appropriate architecture for Malaysia’s specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus’s spread and minimize its effects on Malaysia’s population.
Universiti Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian Technical University. There is a great development of UTHM since its formation in 1993. Therefore, it is crucial to have accurate future electricity consumption forecasting for its future energy management and saving. Even though there are previous works of electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS), but most of their data are multivariate data. In this study, we have only univariate data of UTHM electricity consumption from January 2009 to December 2018 and wish to forecast 2019 consumption. The univariate data was converted to multivariate and ANFIS was chosen as it carries both advantages of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS). ANFIS yields the MAPE between actual and predicted electricity consumption of 0.4002% which is relatively low if compared to previous works of UTHM electricity forecasting using time series model (11.14%), and first-order fuzzy time series (5.74%), and multiple linear regression (10.62%).
Natural disaster brings massive destruction towards properties and human being and flood is one of them. In order for the government to take earlier action to reduce the damages, an accurate flood prediction is necessary. In Malaysia, Kelantan is categorized as a high flood risk area, thus this study focuses on Kelantan flood prediction. This study is to investigate the effect of decomposition for water level prediction by applying Artificial Neural Network (ANN) forecasting model. In this study, Empirical Mode Decomposition (EMD) is used as the decomposition method. The best Intrinsic Mode Function (IMF) for each input variable is selected using correlation-based selection method. The results showed that the performance of hybrid EMD and ANN is superior compared to other models, especially classic ANN model. The reason for this outcome is that through decomposition methods, ANN is able to capture more in-depth information of the Kelantan hydrological time series data. The resulting model provides new insights for government and hydrologist in Kelantan to have better prediction towards flood occurrence.
Forecasting is prediction of future values based on historical data. Electricity consumption forecasting is crucial for utility company to plan for future power system generation. Even though there are previous works of electricity consumption forecasting using Artificial Neural Network (ANN), but most of their data is multivariate data. In this study, we have only univariate data of electricity consumption from January 2009 to December 2018 and wish to do a prediction for a year ahead. On top of that, our data consist of autoregressive component, hence Nonlinear Autoregressive with External (Exogeneous) Input (NARX) Neural Network Time Series from Matlab R2018b was used. It gives the mean absolute percentage error (MAPE) between actual and predicted electricity consumption of 1.38%.
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