Farah Hanan Mohd Faudzi
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Farah Hanan Mohd Faudzi
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Farah Hanan, Mohd Faudzi
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Mohd Faudzi, Farah Hanan
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Publication

Fusion wind and solar generation prototype design with Neural Network

2021-08-27 , Mahmoud Mustafa Yaseen Mohammed Al Asbahi , Muhammad Naufal Mansor , Mohd Rizal Manan , Mohd Azri Abd Aziz , Roejhan Md Kawi , Farah Hanan Mohd Faudzi

Wind and solar power are the most common renewable resources of energy and their usage for power generation is quickly growing all over the world. However, both wind and solar power are difficult to predict manually due to every time changes in weather condition; therefore, power output of wind and solar is associated with some uncertainty. A reliable wind-solar day ahead load prediction with neural network was proposed to support a small microgrids system. All the system performance measurement such as sensitivity, specificity and accuracy give higher than 90%.

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Leukemia Blood Cells Detection using Neural Network Classifier

2023-12-01 , Muhammad Naufal Mansor , Mohd Zamri Hasan , Wan Azani Wan Mustafa , Farah Hanan Mohd Faudzi , Syahrul Affandi Saidi , Mohd Aminudin Jamlos , Noor Anida Abu Talib , Ahmad Kadri Junoh

Image segmentation is an image processing operation performed on the image in order to partition the image into some images based on the information contained in the original image. Image segmentation plays an important role in many medical imaging applications, image segmentation facilitates the anatomy process in a particular body of human body. Classification and clustering are the methods used un data mining for analyzing the data sets and divide them on the basis of some particular classification rules. There are many image segmentation tools that used for medical purpose, so it is necessary to define and/or to improve the image segmentation methods in order to get the best method. In this study, the image of leukemia and red blood cells will be used as samples to determine the best algorithm in image segmentation. The procedure for doing segmentation itself is clustering image, edge detection on image, and image classification. The clustering is to extract important information from an image. The edge detection is to determine the existence of edges of lines in image in order to investigate and localize the desired edge features. Moreover, the classification analyzes the properties of some images and organizes the information into certain categories. In this study, the Neural Network and K-Nearest Neighbor are used for image classification by paired with Local Binary Pattern and Principal Component Analysis. The results revealed that the best method of proven in classifying images is from Local Binary Pattern feature extraction with the average accuracy of 94%.

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Publication

Fusion wind and solar generation forecasting via neural network

2021-08-27 , Mahmoud Mustafa Yaseen Mohammed Al Asbahi , Muhammad Naufal Mansor , Mohd Rizal Manan , Mohd Azri Abd Aziz , Roejhan Md Kawi , Farah Hanan Mohd Faudzi

Wind and solar power are the most common renewable resources of energy and their usage for power generation is quickly growing all over the world. However, both wind and solar power are difficult to predict manually due to every time changes in weather condition; therefore. power output of wind and solar is associated with some uncertainty. A reliable wind-solar day ahead load prediction proposed in this paperwork to support a small microgrids system. The system is a combination of hardware of solar panel, wind turbine, hybrid charge controller, current sensor, voltage sensor circuit, battery, Arduino Mega and personal computer that is install with MATLAB along with artificial neural network model for load forecast. The prediction model is known as Feedforward back propagation (FFBP) artificial neural network (ANN), this method utilizes a learning relationship between wind-solar power output and predicted weather. The FFBP model trained ANN to recognize similar pattern and to predict the output power based on train and tested data and the results achieved 99.5 accuracy, 6.25% MAPE and 1.2 % MAD.

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