International Journal of Information Technology and Computer Science
Scope Information not localized - No SJR COMMENTS
This journal does not ask for article publishing processing (APC) or submission fees. Starting on August 1, 2025, authors who submit paper to this journal will need to subscribe to it when their paper is accepted. This is to make sure that the journal grows quickly and is of excellent quality. The authors can select between the print and electronic versions.
8 to 19 pages
It should be noted that we typically receive over 3,000 submissions per year, and it is difficult to locate so many reviewers. Cooperation between authors and editors is crucial; authors have the right to suggest reviewers.
Determination of Artificial Neural Network Structure with Autoregressive Form of ARIMA and Genetic Algorithm to Forecast Monthly Paddy Prices in Thailand
By Ronnachai Chuentawat Siriporn Loetyingyot
DOI: https://doi.org/10.5815/ijisa.2019.03.03, Pub. Date: 8 Mar. 2019
This research aims to study a development of a forecasting model to predict a monthly paddy price in Thailand with 2 datasets. Each of datasets is the univariate time series that is a monthly data, since Jan 1997 to Dec 2017. To generate a forecasting model, we present a forecasting model by using the Artificial Neural Network technique and determine its structure with Autoregressive form of the ARIMA model and Genetic Algorithm, it’s called AR-GA-ANN model. To generate the AR-GA-ANN model, we set 1 to 3 hidden layers for testing, determining the number of input nodes by an Autoregressive form of the ARIMA model and determine the number of neurons in hidden layer by Genetic Algorithm. Finally, we evaluate a performance of our AR-GA-ANN model by error measurement with Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) and compare errors with the ARIMA model. The result found that all of AR-GA-ANN models have lower RMSE and MAPE than the ARIMA model and the AR-GA-ANN with 1 hidden layer has lowest RMSE and MAPE in both datasets.
[...] Read more.Spatial Temporal Dynamics and Risk Zonation of Dengue Fever, Dengue Hemorrhagic Fever, and Dengue Shock Syndrome in Thailand
By Phaisarn Jeefoo
DOI: https://doi.org/10.5815/ijmecs.2012.09.08, Pub. Date: 8 Sep. 2012
This study employed geographic information systems (GIS) to analyze the spatial factors related to dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS) epidemics. Chachoengsao province, Thailand, was chosen as the study area. This study examines the diffusion pattern of disease. Clinical data including gender and age of patients with disease were analyzed. The hotspot zonation of disease was carried out during the outbreaks for years 2001 and 2007 by using local spatial autocorrelation statistics (LSAS) and kernel-density estimation (KDE) methods. The mean center locations and movement patterns of the disease were found. A risk zone map was generated for the incidence. Data for spatio-temporal analysis and risk zonation of DF/DHF/DSS were employed for years 2000 to 2007. Results found that the age distribution of the cases was different from the general population’s age distribution. Taking into account that the quite high incidence of DF/DHF/DSS cases was in the age group of 13-24 years old and the percentage rate of incidence was 42.9%, a DF/DHF/DSS virus transmission out of village is suspected. An epidemic period of 20 weeks, starting on 1st May and ending on 31st September, was analyzed. Approximately 25% of the cases occurred between Weeks 6-8. A pattern was found using mean centers of the data in critical months, especially during rainy season. Finally, it can be identified that from the total number of villages affected (821), the highest risk zone covered 7 villages (0.85%); the moderate risk zone comprised 39 villages (4.75%); for the low risk zone 22 villages (2.68%) were found; the very low risk zone consisted of 120 villages (14.62%); and no case occurred in 633 villages (77.10%). The zones most at risk were shown in districts Mueang Chachoengsao, Bang Pakong, and Phanom Sarakham. This research presents useful information relating to the DF/DHF/DSS. To analyze the dynamic pattern of DF/DHF/DSS outbreaks, all cases were positioned in space and time by addressing the respective villages. Not only is it applicable in an epidemic, but this methodology is general and can be applied in other application fields such as dengue outbreak or other diseases during natural disasters.
[...] Read more.
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