Nor Azura Husin

Modeling of dengue outbreak prediction in Malaysia: A comparison of Neural Network and Nonlinear Regression Model

Malaysia has a good dengue surveillance system but there have been insufficient findings on suitable model to predict future dengue outbreak. This study aims to design a neural network model (NNM) and nonlinear regression model (NLRM) using different architectures and parameters incorporating time series, location and rainfall data to define the best architecture for early prediction of dengue outbreak. Four architecture of NNM and NLRM were developed in this study. Architecture I involved only dengue cases data, Architecture II involved combination of dengue cases data and rainfall data, Architecture III involved proximity location dengue cases data, while Architecture IV involved the combination of all criterion. The parameters studied in this research were adjusted for optimal performance. These parameters are the learning rate, momentum rate and number of neurons in the hidden layer. The performance of overall architecture was analyzed and the result shows that the MSE for all architectures by using NNM is better compared by NLRM. Furthermore, the results also indicate that architecture IV performs significantly better than other architecture in predicting dengue outbreak and it is therefore proposed as a useful approach in the problem of time series prediction of dengue outbreak.

Sequential pattern mining on library transaction data

Application of data mining techniques in library data results interesting and useful patterns that can be used to improve services in university libraries. This paper presents results of the work in applying the sequential pattern mining algorithm namely AprioriAll on a library transaction dataset. Frequent sequential patterns containing book sequences borrowed by students are generated for minimum supports 0.3, 0.2, 0.15 and 0.1. These patterns can help library in providing book recommendation to students, conducting book procurement based on readers need, as well as managing books layout.

A hybrid model using genetic algorithm and neural network for predicting dengue outbreak

Prediction of dengue outbreak becomes crucial in Malaysia because this infectious disease remains one of the main health issues in the country. Malaysia has a good surveillance system but there have been insufficient findings on suitable model to predict future outbreaks. While there are previous studies on dengue prediction models in Malaysia, unfortunately some of these models still have constraints in finding good parameter with high accuracy. The aim of this paper is to design a more promising model for predicting dengue outbreak by using a hybrid model based on genetic algorithm for the determination of weight in neural network model. Several model architectures are designed and the parameters are adjusted to achieve optimal prediction performance. Sample data that covers dengue and rainfall data of five districts in Selangor collected from State Health Department of Selangor (SHD) and Malaysian Meteorological Department is used as a case study to evaluate the proposed model. However, due to incomplete collection of real data, a sample data with similar behavior was created for the purpose of preliminary experiment. The result shows that the hybrid model produces the better prediction compared to standalone models.

Comparison of Classification Techniques on Fused Optical and SAR Images for Shoreline Extraction: A Case Study at Northeast Coast of Peninsular Malaysia

Shoreline is a very important element to identify exact boundary at the coastal areas of a country. However, in order to identify land-water boundary for a large region using traditional ground survey technique is very time consuming. Alternatively, shoreline can be extracted by using satellite images that minimizes the mapping errors. The trend of extracting shoreline has been shifted from image processing to machine learning and data mining techniques. By using machine learning technique, the satellite images could be classified into land and water classes in order to extract shoreline. However, the result is meaningless if it has cloud and shadow on the water-land boundary. In this study, we compare the accuracy and Kappa Coefficient of six machine learning techniques namely Maximum Likelihood, Minimum Distance, Mahalanobis Distance, Parallelepiped, Neural Network and Support Vector Machines on three type of images; single optical multispectral, single SAR and fused image. A case study for this research is done alongside Tumpat beach, located at the Northeast Coast of Peninsular Malaysia. All the machine learning techniques have been tested on the three types of images. The experimental results show that classification using SVM on single multispectral image has the highest accuracy among all. However, the classified of fused image using SVM is considered much more accurate because it can cater the cloud and shadow problem. Additionally, the classification on 5 and 10 m fused images also tested and the result shows that with the increase of spatial resolution of fused image, the classification accuracy also increases.

Multi-layers Convolutional Neural Network for Twitter Sentiment Ordinal Scale Classification

Twitter sentiment analysis according to five points scales has attracted research interest due to its potential use in commercial and public social media application. A multi-point scale classification is a popular way used by many companies to evaluate the sentiment of product reviews (e.g. Alibaba, Amazon and eBay). Most of the classification approaches addressed this problem using traditional classification algorithm that requires expert knowledge to select the best features. Even though deep learning has been utilized, most of them employed a simple structure that not enough to capture the important features. In this paper, a complex structure of convolutional neural network (CNN) is proposed to classify the tweet into five-point scale and obtain a more several tweet representation. After a series of experiments with CNN including different hyperparameters and pooling strategies (Max and Average), we found that the best structure for our model is three convolutional layers, each one followed by average pooling layer. The proposed multi-layers convolutional neural network (MLCNN) model achieve the lowest Macro average mean absolute error (MAEM) and outperforms the state-of-the-art approach on tweet 2016 dataset for Ordinal classification. Experimental results show the ability of average pooling to preserve significant features that provide more expressiveness to ordinal scale.

Early self-diagnosis of dengue symptoms using fuzzy and data mining approach

Dengue fever (DF) is of huge public health problem that causes mortality and morbidity worldwide. Currently, dengue fever is the most crucial infectious disease in Malaysia. Delay in detection of dengue disease could lead to life threatening complications and increase fatality rate. Therefore, this research aimed to develop an accurate model that could better detect early signs and symptoms of dengue fever and a practical system for self-notification of the disease. Two techniques were applied to give early self-notification to the patients whether they are suspected to have dengue fever or not namely the fuzzy expert system and data mining technique. The rules of dengue diagnosis built based on an interview with doctor and those rules will be applied in an expert system using a fuzzy logic. However, before applying the extracted rules, the accuracy of rules was tested by data mining tool. The experimental results show that the fuzzy logic approach with data mining had improved the accuracy and produce a reliable result for self-diagnosis of dengue symptoms.Dengue fever (DF) is of huge public health problem that causes mortality and morbidity worldwide. Currently, dengue fever is the most crucial infectious disease in Malaysia. Delay in detection of dengue disease could lead to life threatening complications and increase fatality rate. Therefore, this research aimed to develop an accurate model that could better detect early signs and symptoms of dengue fever and a practical system for self-notification of the disease. Two techniques were applied to give early self-notification to the patients whether they are suspected to have dengue fever or not namely the fuzzy expert system and data mining technique. The rules of dengue diagnosis built based on an interview with doctor and those rules will be applied in an expert system using a fuzzy logic. However, before applying the extracted rules, the accuracy of rules was tested by data mining tool. The experimental results show that the fuzzy logic approach with data mining had improved the accuracy and produce a ...

Validation assessment of shoreline extraction on medium resolution satellite image

Monitoring coastal zones helps provide information about the conditions of the coastal zones, such as erosion or accretion. Moreover, monitoring the shorelines can help measure the severity of such conditions. Such measurement can be performed accurately by using Earth observation satellite images rather than by using traditional ground survey. To date, shorelines can be extracted from satellite images with a high degree of accuracy by using satellite image classification techniques based on machine learning to identify the land and water classes of the shorelines. In this study, the researchers validated the results of extracted shorelines of 11 classifiers using a reference shoreline provided by the local authority. Specifically, the validation assessment was performed to examine the difference between the extracted shorelines and the reference shorelines. The research findings showed that the SVM Linear was the most effective image classification technique, as evidenced from the lowest mean distance be...

Performance of Hybrid GANN in Comparison with Other Standalone Models on Dengue Outbreak Prediction

Early prediction of diseases especially dengue fever in the case of Malaysia, is very crucial to enable health authorities to develop response strategies and context preventive intervention programs such as awareness campaigns for the high risk population before an outbreak occurs. Some of the deficiencies in dengue epidemiology are insufficient awareness on the parameter as well as the combination among them. Most of the studies on dengue prediction use standalone models which face problem of finding the appropriate parameter since they need to apply try and error approach. The aim of this paper is to conduct experiments for determining the best network structure that has effective variable and fitting parameters in predicting the spread of the dengue outbreak. Four model structures were designed in order to attain optimum prediction performance. The best model structure was selected as predicting model to solve the time series prediction of dengue. The result showed that neighboring location of dengue cases was very effective in predicting the dengue outbreak and it is proven that the hybrid Genetic Algorithm and Neural Network (GANN) model significantly outperforms standalone models namely regression and Neural Network (NN).