scholarly journals Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network

2022 ◽  
Vol 11 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Titi Purwandari ◽  
Solichatus Zahroh ◽  
Yuyun Hidayat ◽  
Sukonob Sukonob ◽  
Mustafa Mamat ◽  
...  
Keyword(s):  
Neural Network ◽  
Health Workers ◽  
Movement Control ◽  
Percentage Error ◽  
Average Percentage ◽  
First Case ◽  
The Neural Network ◽  
The Government ◽  
Mlp Model ◽  
Death Cases

COVID-19 has spread to more than a hundred countries worldwide since the first case reported in late 2019 in Wuhan, China. As one of the countries affected by the spread of COVID-19 cases, the local government of Malaysia has issued several policies to reduce the spread of this outbreak. One of the measures taken by the Malaysian government, namely the Movement Control Order, has been carried out since March 18, 2020. In order to provide precise information to the government so that it can take the appropriate measures, many researchers have attempted to predict and create the model for these cases to identify the number of cases each day and the peak of this pandemic. Therefore, hospitals and health workers can anticipate a surge in COVID-19 patients. In this research, confirmed, recovered, and death cases prediction was performed using the neural network as one of the machine learning methods with high accuracy. The neural network model used is the Multi-Layer Perceptron, Neural Network Auto-Regressive, and Extreme Learning Machine. The three models calculated the average percentage error (APE) values for 7 days and obtained APE values for most cases less than 10%; only 1 case in the last day of one method had an APE value of approximately 11%. Furthermore, based on the best model, then the forecast is made for the next 7 days. In conclusion, this study identified that the MLP model is the best model for 7-step ahead forecasting for confirmed, recovered, and death cases in Malaysia. However, according to the result of testing data, the ELM performs better than the MLP model.

A method for predicting the capacity of a lithium-ion battery based on deep neural network

2021 ◽  
Author(s):  
JamesChan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Lithium Ion ◽  
Chemical Conversion ◽  
Absolute Error ◽  
Production Costs ◽  
Percentage Error ◽  
Average Percentage ◽  
True Value ◽  
Battery Capacity

This paper proposes a solution to predict the capacity of the lithium-ion battery's capacity division process using deep learning methods. This solution extracts the physical observation records of part of the process steps from the chemical conversion and volumetric processes as features, and trains a Deep Neural Network (DNN) to achieve accurate prediction of battery capacity. According to the test, the average percentage absolute error (Mean Absolute Percentage Error, MAPE) of the battery capacity predicted by this model is only 0.78% compared with the true value. Combining this model with the production line can greatly reduce production time and energy consumption, and reduce battery production costs.

Predicting the French Stock Market Using Social Media Analysis

2015 ◽  
Vol 7 (2) ◽  
pp. 70-84
Author(s):  
Vincent Martin ◽  
Emmanuel Bruno ◽  
Elisabeth Murisasco
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Percentage Error ◽  
Social Media Analysis ◽  
Single Output ◽  
The Neural Network ◽  
Simple Neural Network

In this article, the authors try to predict the next-day CAC40 index. They apply the idea of Johan Bollen et al. from (Bollen, Mao, & Zeng, 2011) on the French stock market and they conduct their experiment using French tweets. Two analyses are applied on tweets: sentiment analysis and subjectivity analysis. Results of these analyses are then used to train a simple neural network. The input features are the sentiment, the subjectivity and the CAC40 closing value at day-1 and day-0. The single output value is the predicted CAC40 closing value at day+1. The authors propose an architecture using the JEE framework resulting in a better scalability and an easier industrialization. The main experiments are conducted over 5 months of data. The authors train their neural network on the first of the data and they test predictions on the remaining quarter. Their best run gives a direction accuracy of 80% and a mean absolute percentage error (MAPE) of 2.97%. In another experiment, the authors retrain the neural network each day which decreases the MAPE to 1.14%.

Using neural network model to estimate the rental price of residential properties

2019 ◽  
Vol 24 (2) ◽  
pp. 217-230
Author(s):  
Olalekan Shamsideen Oshodi ◽  
Wellington Didibhuku Thwala ◽  
Tawakalitu Bisola Odubiyi ◽  
Rotimi Boluwatife Abidoye ◽  
Clinton Ohis Aigbavboa
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Predictive Accuracy ◽  
Content Type ◽  
The Neural Network ◽  
Residential Properties ◽  
African Context ◽  
Neural Network Algorithm ◽  
The Government

Purpose Estimation of the rental price of a residential property is important to real estate investors, financial institutions, buyers and the government. These estimates provide information for assessing the economic viability and the tax accruable, respectively. The purpose of this study is to develop a neural network model for estimating the rental prices of residential properties in Cape Town, South Africa. Design/methodology/approach Data were collected on 14 property attributes and the rental prices were collected from relevant sources. The neural network algorithm was used for model estimation and validation. The data relating to 286 residential properties were collected in 2018. Findings The results show that the predictive accuracy of the developed neural network model is 78.95 per cent. Based on the sensitivity analysis of the model, it was revealed that balcony and floor area have the most significant impact on the rental price of residential properties. However, parking type and swimming pool had the least impact on rental price. Also, the availability of garden and proximity of police station had a low impact on rental price when compared to balcony. Practical implications In the light of these results, the developed neural network model could be used to estimate rental price for taxation. Also, the significant variables identified need to be included in the designs of new residential homes and this would ensure optimal returns to the investors. Originality/value A number of studies have shown that crime influences the value of residential properties. However, to the best of the authors’ knowledge, there is limited research investigating this relationship within the South African context.

Photovoltaics Energy Prediction Under Complex Conditions for a Predictive Energy Management System

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
Martin Schmelas ◽  
Thomas Feldmann ◽  
Jesus da Costa Fernandes ◽  
Elmar Bollin
Keyword(s):  
Neural Network ◽  
Physical Model ◽  
Energy Management ◽  
Management System ◽  
Percentage Error ◽  
Positive Energy ◽  
Energy Management System ◽  
Energy Prediction ◽  
The Neural Network ◽  
Accurate Forecast

Solar energy converted and fed to the utility grid by photovoltaic modules has increased significantly over the last few years. This trend is expected to continue. Photovoltaics (PV) energy forecasts are thus becoming more and more important. In this paper, the PV energy forecasts are used for a predictive energy management system (PEMS) in a positive energy building. The publication focuses on the development and comparison of different models for daily PV energy prediction taking into account complex shading, caused for example by trees. Three different forecast methods are compared. These are a physical model with local shading measurements, a multilayer perceptron neural network (MLP), and a combination of the physical model and the neural network. The results show that the combination of the physical model and the neural network provides the most accurate forecast values and can improve adaptability. From April to December, the mean percentage error (MPE) of the MLP with physical information is 11.6%. From December to March, the accuracy of the PV predictions decreases to an MPE of 78.8%. This is caused by poorer irradiation forecasts, but mainly by snow coverage of the PV modules.

scholarly journals Prediction of Density and Speed of Sound of Binary Ionic Liquid and Ketone Mixtures Using Artificial Neural Network

2019 ◽  
Vol 120 ◽  
pp. 01003
Author(s):  
Redden Rose Rivera ◽  
Allan Soriano
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ionic Liquid ◽  
Ionic Liquids ◽  
Speed Of Sound ◽  
Negative Impact ◽  
Green Energy ◽  
Percentage Error ◽  
Average Percentage ◽  
Artificial Neural

The applications of ionic liquids solve a lot of major problems regarding green energy production and environment. Ionic liquids are solvents used as alternative to unfriendly traditional and hazardous solvents which reduces the negative impact to environment to a great extent. This study produced models to predict two of the basic physical properties of binary ionic liquid and ketone mixtures: density and speed of sound. The artificial neural network algorithm was used to predict these properties by varying the temperature, mole fraction, atom count in cation, methyl group count in cation, atom count in anion, hydrogen atom count in anion of ionic liquid and atom count in ketone. Total experimental data points of 2517 for density and 947 for speed of sound were used to train the algorithm and to test the network obtained. The optimum neural network structure determined for density and speed of sound of binary ionic liquid and ketone mixtures were 7-9-9-1 and 7-7-4-1 respectively; overall average percentage error of 2.45% and 2.17% respectively; and mean absolute error of 28.21 kg/m3 and 33.91 m/s respectively. The said algorithm was found applicable for the prediction of density and speed of sound of binary ionic liquid and ketone mixtures.

scholarly journals Situational Analysis for COVID-19: Estimating Transmission Dynamics in Malaysia using an SIR-Type Model with Neural Network Approach

2021 ◽  
Vol 50 (8) ◽  
pp. 2469-2478
Author(s):  
Mohammad Subhi Jamiluddin ◽  
Mohd Hafiz Mohd ◽  
Noor Atinah Ahmad ◽  
Kamarul Imran Musa
Keyword(s):  
Neural Network ◽  
Movement Control ◽  
Type Model ◽  
Situational Analysis ◽  
Neural Network Approach ◽  
Public Places ◽  
Major Health ◽  
The Government ◽  
Control Order ◽  
Network Approaches

COVID-19 is a major health threat across the globe, which causes severe acute respiratory syndrome, and it is highly contagious with significant morbidity and mortality. In this paper, we examine the feasibility and implications of several phases of Movement Control Order (MCO) and some non-pharmaceutical intervention (NPI) strategies implemented by Malaysian government in the year 2020 using a mathematical model with SIR-neural network approaches. It is observed that this model is able to mimic the trend of infection trajectories of COVID-19 pandemic and, Malaysia had succeeded to flatten the infection curve at the end of the Conditional MCO (CMCO) period. However, the signs of ‘flattening’ with R0 of less than one had been taken as a signal to ease up on some restrictions enforced before. Though the government has made compulsory the use of face masks in public places to control the spread of COVID-19, we observe a contrasting finding from our model with regards to the impacts of wearing mask policies in Malaysia on R0 and the infection curve. Additionally, other events such as the Sabah State Election at the end of third quarter of 2020 has also imposed a dramatic COVID-19 burden on the society and the healthcare systems.

scholarly journals The Impact of COVID-19 Pandemic on Higher Education and Research: Learning from Experience and the Way Forward

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Ahmad Bashawir Abdul Ghani ◽  
Nor Idayu Mahat ◽  
Mohd Faizal Omar ◽  
Shahbani Abu Bakar
Keyword(s):  
Negative Impact ◽  
Movement Control ◽  
Holistic Approach ◽  
Learning And Teaching ◽  
Work From Home ◽  
First Case ◽  
Face To Face Learning ◽  
The Government ◽  
The University ◽  
The Impact

The COVID-19 pandemic took its toll on many countries in early 2020 after the first case was reported in China at the end of 2019. Malaysia was not spared either and the Government was forced to take a bold yet drastic measure in implementing the Movement Control Order (MCO) in earnest on 18 March 2020. The measure, akin to a lockdown, practically forced all forms of socio-economics and socio-educational activities to come to an abrupt stop. Schools, institutions of higher learning and training centers were directed to close its doors to students. Universiti Utara Malaysia (UUM) had to abruptly implement contingency plans in the wake of the negative impact brought about by the pandemic. Almost all academic activities had to be reorganized when majority of the students opted to return to the safety of their home environment, and the staff were required to work from home in compliant with the MCO. This development necessitated the University to introduce the remote learning mode in place of the traditional face to face learning and teaching (T&L). Various other strategies and measures were also introduced by the University which required reprioritization of tasks and determining possible risks that could impede normal daily operations. UUM opted for a holistic approach to address the impending concerns and to ensure the continuity of the education process and to address the wellbeing of its staff who are forced to work from home.

scholarly journals Use of Empirical Mode Decomposition in Improving Neural Network Forecasting of Paddy Price

MATEMATIKA ◽  
2019 ◽  
Vol 35 (4) ◽  
pp. 53-64
Author(s):  
Siti Nabilah Syuhada Abdullah ◽  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Forecast Errors ◽  
Ann Model ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Artificial Neural Network Ann

Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices fromFebruary 1999 up toMay 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.

scholarly journals An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya

2021 ◽  
Vol 22 (3) ◽  
pp. 1245
Author(s):  
Adi Kurniawan ◽  
Anisa Harumwidiah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Percentage Error ◽  
Average Percentage ◽  
Daily Average ◽  
Artificial Neural ◽  
Average Percentage Error ◽  
The City

The estimation of the daily average global solar radiation is important since it increases the cost efficiency of solar power plant, especially in developing countries. Therefore, this study aims at developing a multi layer perceptron artificial neural network (ANN) to estimate the solar radiation in the city of Surabaya. To guide the study, seven (7) available meteorological parameters and the number of the month was applied as the input of network. The ANN was trained using five-years data of 2011-2015. Furthermore, the model was validated by calculating the mean average percentage error (MAPE) of the estimation for the years of 2016-2019. The results confirm that the aforementioned model is feasible to generate the estimation of daily average global solar radiation in Surabaya, indicated by MAPE of less than 15% for all testing years.

scholarly journals Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

2022 ◽  
Vol 24 (3) ◽  
pp. 1-26
Author(s):  
Nagaraj V. Dharwadkar ◽  
Anagha R. Pakhare ◽  
Vinothkumar Veeramani ◽  
Wen-Ren Yang ◽  
Rajinder Kumar Mallayya Math
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Production Line ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Multiple Production ◽  
Wavelet Regression ◽  
The Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

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