Nordiana Shariffudin
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Nordiana Shariffudin
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Nordiana, Shariffudin
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Shariffudin, N.
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Scopus Author ID
36968747300

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Development of Real Time Night Vision Camera Monitoring Robot Integrating DTMF and GPS System

2020-09-21 , Sulong M.M.S. , Mohd Zamri Hasan , Nordiana Shariffudin , Busari M.A. , Muhammad Naufal Mansor

In general, spy robots were employed mostly in military field to patrol over the country border and also can be assigned for a rescue and search mission. Even though this technology has a rapid growth recently, the major problem is what is there in Malaysia security sector, there are many lacks in this technology if compared to other countries. Furthermore, most robots use the RF technology which means the person can only monitor or control the robot within a limited range. Even though the patrol robot can be operated from a long range, there is a circumstance that it can be hard to be located or tracked. Moreover, the ordinary camera can't deliver a better performance under dark circumstances. In view of confinements that have been featured previously, this project plans to develop a mobile patrol robot with wireless night vision camera that can be controlled by using DTMF and GPS system that can be used in military field. There are several parts to be in implement in this project as following; software simulation & hardware development of robot. As example the DTMF technology, GPS system and wireless night vision camera as well are implemented in this project so the working principle of the robot features needs to be simulated and programmed into the robot using Arduino and Proteus software. Only then if the simulation is succeeded the progress is then proceeded to the hardware development of the robot. Later the successful simulation is integrated into the robot to make the robot can be fully operated. To conclude, this robot is supposed to be able to easily be tracked by GPS system and monitored from long range using DTMF while performing such monitoring or guarding duty at the country border or other public areas.

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Automatic Recognition System of Iron Deficiency Anaemia in Human RBC using Machine Learning Techniques

2023-01-01 , Siti Nurul Aqmariah Mohd Kanafiah , Jusman Y. , Shazmin Aniza Abdul Shukor , Ibrahim W.N.A.B.W. , Nordin S.A. , Tohit E.R.B.M. , Ali H.B. , Nordiana Shariffudin , Siti Marhainis Othman

Iron Deficiency Anaemia (IDA) is the most common blood disorder. According to WHO, 30% of women aged 15-49 years, 37% of pregnant women, as well as 40% of children aged 6-59 months are anaemic globally. Anaemia can cause premature birth and affect mental, physical, and cognitive development, which in turn will lead to birth weight problems and stunted birth. The process of detecting IDA is usually captured based on a thin blood smear utilizing microscopic observation. Nevertheless, this process can be time-consuming. Moreover, it is challenging to identify the difference between IDA and normal red blood cells (RBCs) because the size is similar based on the observation of the human eye. It will cause difficulty in giving drug treatment to patients. A computeraided diagnosis (CAD) method was created to automatically distinguish between IDA and normal RBCs. The processes started with image acquisition, image processing, and recognition. Additionally, a Graphical User Interface (GUI) is also used to display images. In conclusion, recognition was done using the Multilayer Perceptron (MLP) method. The findings indicate that the proposed automated system is effective at distinguishing between IDA as well as normal RBCs, having an accuracy of 97.58% with regard to training and 98% regarding validation utilizing Levenberg-Marquardt (LM) trained MLP.

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Intelligent Classification Procedure for Plasmodium Knowlesi Malaria Species

2022-01-01 , Siti Nurul Aqmariah Mohd Kanafiah , Mohd Yusoff Mashor , Mohamed Z. , Jusman Y. , Hasimah Ali , Nordiana Shariffudin , Siti Marhainis Othman

Plasmodium knowlesi (PK) is the fifth most prevalent malarial parasite species that causes serious health problems. Generally, PK present in a thin blood smear is observed using a microscope to differentiate between trophozoites (PKT), schizonts (PKS), gametocytes (PKG), and white blood cells (WBCs). This process is time-consuming and strenuous for the human eye. This study developed an intelligent classification procedure for PK using image processing and classification methods. The processes involved starting from image acquisition, and contrast enhancement based on Combination Local and Global Statistical Data (CLGSD), and local contrast stretching (LCS). Subsequently, a segmentation procedure was developed to segment the malaria images into two regions, namely malarial parasites and background regions. The proposed 16 feature sets were extracted, which consisted of the size of the object, size ratio of the object per infected RBC, and seven moments for each object shape based on size and perimeter. Finally, to validate the procedure performance, the proposed procedure was tested using 800 malarial parasites and WBC images. The results showed that the proposed procedure can classify three stages of PK, namely PKT, PKS, and PKG, as well as WBCs with an accuracy of 99.56% for training and 98.84% for validation, using a multi-layer perceptron (MLP) trained using the Levernberg-Marquardt (LM) algorithm.

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