Azuraliza Abu Bakar

Publications

3 Comparative Study - Three Artificial Intelligence Techniques for Rain Domain in Precipitation Forecast

Authors: Zalinda Othman, Abdul Razak Hamdan, Azuraliza Abu Bakar, Nabilah Filzah Mohd Radzuan, Andi Putra

Abstract:

Precipitation forecast is important in avoid incident of natural disaster which can cause loss in involved area. This review paper involves three techniques from artificial intelligence namely logistic regression, decisions tree, and random forest which used in making precipitation forecast. These combination techniques through VAR model in finding advantages and strength for every technique in forecast process. Data contains variables from rain domain. Adaptation of artificial intelligence techniques involved on rain domain enables the process to be easier and systematic for precipitation forecast.

Keywords: Logistic Regression, decisions tree, random forest, VAR model

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2 Representing Data without Lost Compression Properties in Time Series: A Review

Authors: Zalinda Othman, Abdul Razak Hamdan, Azuraliza Abu Bakar, Nabilah Filzah Mohd Radzuan

Abstract:

Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties.

Keywords: Uncertainty, Weather Prediction, compression properties, uncertain time series, mining technique

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1 Anomaly Based On Frequent-Outlier for Outbreak Detection in Public Health Surveillance

Authors: zalizah Awang Long, Abdul Razak Hamdan, Azuraliza Abu Bakar

Abstract:

Public health surveillance system focuses on outbreak detection and data sources used. Variation or aberration in the frequency distribution of health data, compared to historical data is often used to detect outbreaks. It is important that new techniques be developed to improve the detection rate, thereby reducing wastage of resources in public health. Thus, the objective is to developed technique by applying frequent mining and outlier mining techniques in outbreak detection. 14 datasets from the UCI were tested on the proposed technique. The performance of the effectiveness for each technique was measured by t-test. The overall performance shows that DTK can be used to detect outlier within frequent dataset. In conclusion the outbreak detection technique using anomaly-based on frequent-outlier technique can be used to identify the outlier within frequent dataset.

Keywords: Public Health, Surveillance, outbreak, anomaly, outlier detection, frequent-outlier

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Abstracts

3 Optimizing Load Shedding Schedule Problem Based on Harmony Search

Authors: Azuraliza Abu Bakar, Almahd Alshereef, Ahmed Alkilany, Hammad Said

Abstract:

From time to time, electrical power grid is directed by the National Electricity Operator to conduct load shedding, which involves hours' power outages on the area of this study, Southern Electrical Grid of Libya (SEGL). Load shedding is conducted in order to alleviate pressure on the National Electricity Grid at times of peak demand. This approach has chosen a set of categories to study load-shedding problem considering the effect of the demand priorities on the operation of the power system during emergencies. Classification of category region for load shedding problem is solved by a new algorithm (the harmony algorithm) based on the "random generation list of category region", which is a possible solution with a proximity degree to the optimum. The obtained results prove additional enhancements compared to other heuristic approaches. The case studies are carried out on SEGL.

Keywords: Optimization, classification, harmony algorithm, load shedding

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2 Comparative Study od Three Artificial Intelligence Techniques for Rain Domain in Precipitation Forecast

Authors: Zalinda Othman, Abdul Razak Hamdan, Azuraliza Abu Bakar, Nabilah Filzah Mohd Radzuan, Andi Putra

Abstract:

Precipitation forecast is important to avoid natural disaster incident which can cause losses in the involved area. This paper reviews three techniques logistic regression, decision tree, and random forest which are used in making precipitation forecast. These combination techniques through the vector auto-regression (VAR) model help in finding the advantages and strengths of each technique in the forecast process. The data-set contains variables of the rain’s domain. Adaptation of artificial intelligence techniques involved in rain domain enables the forecast process to be easier and systematic for precipitation forecast.

Keywords: Logistic Regression, decisions tree, random forest, VAR model

Procedia PDF Downloads 240
1 Representation Data without Lost Compression Properties in Time Series: A Review

Authors: Zalinda Othman, Abdul Razak Hamdan, Azuraliza Abu Bakar, Nabilah Filzah Mohd Radzuan

Abstract:

Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties.

Keywords: Uncertainty, Weather Prediction, compression properties, uncertain time series, mining technique

Procedia PDF Downloads 284