Nabilah Filzah Mohd Radzuan

Publications

2 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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1 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

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1280

Abstracts

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