Rafikha Aliana A Raof
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Rafikha Aliana A Raof
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Rafikha Aliana, A Raof
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Raof, Rafikha Aliana A.
Raof, R. A.A.
Raof, Rafikha Alaina A.
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Scopus Author ID
57075005500

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  • Publication
    Reliable Early Breast Cancer Detection using Artificial Neural Network for Small Data Set
    ( 2021-03-01)
    Vijayasarveswari V.
    ;
    Muzammil Jusoh
    ;
    Thennarasan Sabapathy
    ;
    Rafikha Aliana A Raof
    ;
    Khatun S.
    ;
    Iszaidy I.
    This paper proposes a breast cancer detection module using Artificial Neural Network for small data set. The developed system consists of hardware and software. Hardware included UWB transceiver and a pair of home-made directional sensor/antenna. The software included a Graphical User Interface (GUI) and k-fold based feed-forward back propagation Neural Network module to detect the tumor existence, size and location along with soft interface between software and hardware. Forward scattering technique is used by placing two sensors diagonally opposite sides of a breast phantom. UWB pulses are transmitted from one side of phantom and received from other side, controlled by the software interface in PC environment. Firstly feed forward backpropagation neural network (FFBNN) is developed. Then, k-fold is combined with developed FFBNN for testing purpose. Four data sets are created where contains 125, 95, 65 and 30 data samples in 1st,2nd,3rd and 4th data set respectively. Collected received signals were then fed into the NN module for training, testing and validation. The process is done for all data sets separately. The system exhibits detection efficiency of tumor existence, location (x, y, z), and size were approximately 87.72%, 87.24%, 83.93% and 80.51% for 1st, 2nd, 3rd and 4th data set respectively. The proposed module is very practical with low-cost and user friendly. The developed breast cancer detection module can be used for large data samples as well as for minimum data samples.
  • Publication
    Time Series Analysis for Vegetable Price Forecasting in E-Commerce Platform: A Review
    ( 2021-06-11)
    Choong K.Y.
    ;
    Rafikha Aliana A Raof
    ;
    Suhizaz Sudin
    ;
    Ong R.J.
    Vegetables industry plays an important role especially in providing the abundant fresh agricultural products. Forecasting the vegetable price is vital in agriculture sector for effective decision making. In Malaysia, the problems faced by the farmers are not only their age, but also their competitive skill where the wholesale market and the hypermarket/supermarket are prioritized by the consumers in Malaysia for the fresh vegetables and fruits. This review article helps to recognize the current problems faced by the agricultural sector of Malaysia and study the relationship between the agriculture and E-Commerce. Recent researchers have mentioned the growth of the E-Agribusiness and the authors found the potential of an Agricultural E-Commerce platform with price forecasting model in solving the current national issue. This research reviews the existing agricultural E-Commerce platforms in worldwide and try to compare with the local one. After the reviews have been done, the authors bring up an idea in constructing the time analysis model in hybrid approach for veggies price forecasting in an agricultural E-Commerce platform which can be used by the government in deriving their policies.
  • Publication
    Development of an automated intelligent diagnostic system for tuberculosis detection based on sputum specimen
    ( 2014)
    Rafikha Aliana A Raof
    Tuberculosis (TB) is a highly infectious disease. TB diagnosis is usually done manually by microbiologist through microscopic examination of sputum specimen of TB patients for pulmonary TB diseases. However, this practice is time consuming and labour-intensive. Hence, it results in fatigue and work overload to the microbiologists, thus reduces the diagnostic performance. This research involved in the development of automated intelligent diagnosis system for tuberculosis detection based on Ziehl-Neelsen sputum specimen. The system developed is also equipped with automatic capturing system for capturing sputum slide images automatically using 40X lens. Besides that, this study also suggested the combination of image processing technique with artificial neural network in creating a new procedure for diagnosing process of Ziehl-Neelsen sputum specimen. Image enhancement technique based on white balance and partial contrast method has been proposed. A new procedure for segmentation technique was also proposed based on the combination of kmeans clustering, 3 × 3 median filter and automated seed based region growing algorithm. The study also includes feature extraction where features such as size, colour and shape were chosen in classifying TB bacilli with the aid of artificial neural network. This research proposed to use HMLP network with MRPE algorithm for detection and classification of TB bacilli. The system is supposed to reduce the problems arise during the diagnosis of tuberculosis disease such as avoidance of eye fatigue to the microbiologist due to observing through the microscope eyepiece for a long period of time. It has been shown that the classification for sputum slide specimen for TB diagnosis produces good results with classification accuracy of more than 94%. These findings suggest the potential use of this software in diagnosing pulmonary TB disease. The conducted research has provided the platform for automated intelligent system to diagnose tuberculosis disease.
  • Publication
    Enhancement and segmentation of Ziehl Neelson sputum slide images using contrast enhancement and Otsu threshold technique
    (Semarak Ilmu Publishing, 2023)
    Ainul Kamilah Mohd Yusoff
    ;
    Rafikha Aliana A Raof
    ;
    Norfadila Mahrom
    ;
    Siti Suraya Md Noor
    ;
    Fazrul Faiz Zakaria
    ;
    Phak Len Al Eh Kan
    Image processing is the most effective method for enhancement and segmentation of tuberculosis bacilli in sputum smear samples. Improper straining can result in poor screening results such as over-staining, under-staining, and blurred images. The goal is to find an image enhancement and segmentation technique that will prepare the image for feature extraction. There are still some shortcomings with existing method when it is implemented on Ziehl Neelsen images. In normal images, TB bacilli can be identified easily, but in blur and images with dark background, TB bacilli are sometimes hidden behind the sputum cells. Hence, the basic method of contrast enhancement is not enough to improve the contrast of TB bacilli as the object of interest within the image. In this study, the combination of local and partial contrast enhancement is proposed as the best method for image enhancement. Image segmentation can be accomplished using Otsu thresholding technique. Otsu's method is presented as most suitable image processing techniques in this paper. The goal of the Otsu Threshold is to find a threshold value that distinguishes the object of interest from the background. Experiment shows that the combination of local and partial contrast enhancement followed by Otsu’s method achieve an average segmentation accuracy of 98.93% when applied on 50 images of sputum smear.
  • Publication
    A proposed framework for improving the detection and classification of Ki67 expression in Astrocytoma histopathological images
    (IEEE, 2024-03)
    Fahmi Akmal Dzulkifli
    ;
    Mohd Yusoff Mashor
    ;
    Rafikha Aliana A Raof
    ;
    Hasnan Jaafar
    Detecting and classifying the Ki67 expression is a challenging process. The inconsistency in staining intensity and the variations in image quality are the main factors that may reduce the performance of an automated system. Therefore, this study proposes a framework that objectively improves detecting and classifying Ki67 expression in astrocytoma histopathological images. The proposed framework began with implementing the double stain normalization procedure to reduce the colour-staining intensity variations. Then, the system automatically enhanced the morphological features of the Ki67 expression. The following step was to segment the enhanced images by using the U-Net network model. The last step of the proposed framework was to localize and classify the Ki67 expression based on the modified YOLOv3 model. In conclusion, the proposed YOLOv3 model produced a high detection result with a mean average precision of 0.80 for detecting Ki67-positive cells and 0.87 for detecting Ki67-negative cells.
  • Publication
    Data Security Implementation using Data Encryption Standard Method for Student Values at the Faculty of Medicine, University of North Sumatra
    ( 2021-03-01)
    Ikhwan A.
    ;
    Rafikha Aliana A Raof
    ;
    Phaklen Ehkan
    ;
    Yasmin Mohd Yacob
    ;
    Syaifuddin M.
    It is undeniable that advances in technology and the rapid spread of information are also accompanied by an increase of crime in the IT field, very valuable information is sought by criminals in the IT field in order to be misused so that they gain enormous profits. One of the information is student value data, so a system is needed that applies a cryptographic algorithm that can secure the information in this case the method used is securing student value data using the Data Encryption Standard (DES) algorithm. DES is asymmetrical cryptographic algorithm and is also classified as a cipher block with a 64-bit key size. DES converts plaintext into a ciphertext of the same size, 64 bits using a 56-bit internal key. By building a system to implement the DES algorithm in securing data values, it is hoped that it can help the Faculty of Medicine, University of North Sumatra in protecting the confidentiality of data values from irresponsible parties.
      3  12
  • Publication
    A Review of Chatbot development for Dynamic Web-based Knowledge Management System (KMS) in Small Scale Agriculture
    ( 2021-03-01)
    Ong R.J.
    ;
    Rafikha Aliana A Raof
    ;
    Suhizaz Sudin
    ;
    Choong K.Y.
    Market data indicates that the average age of Malaysian farmers to be 50 years old and that the majorities are in the B40 group. Malaysia have so much of land with a small population of over 30 million compared with neighbours country and yet still need to import over 50 billion in food commodities annually to feed the nation. Small-scale farmers are having issues in communicating with each other and usually lack of Standard Operating Procedure (SOP) compare to industrial farming. An information sharing platform is prominent to disseminate information and knowledge between farmers especially for most of the young farmers who having issues when they newly start to involve in agriculture field. This paper is about to design and develop a framework of dynamic web-based knowledge management system with Chatbot application in order to utilize the information sharing platform to disseminate knowledge and build networks among small-scale farmers and related experts. Thus, information sharing and working together with a related expert will effectively improve both the quality and quantity of the product and also against the diseases on the spot.
      3  32
  • Publication
    Real Time Detection of Object Blob Localization Application using 1-D Connected Pixel and Windowing Method on FPGA
    ( 2021-03-01)
    Lam Chee Yuen
    ;
    Phaklen Ehkan
    ;
    Rafikha Aliana A Raof
    ;
    Jungjit S.
    Blob detection and localization is a common process used in the machine vision. Current existing blob detection method is using 2-dimensional kernel matrix which is higher in time consumption and also memory space. This study has proposed a dedicated digital architecture consist of two modules to detect binary image blob using only 1-dimensional image pixel. First module is used to detect connected pixel in a row of pixel, and second module is used to perform windowing to justify blob location. This study has been successfully implemented and tested on Altera DE2 FPGA board. The proposed architecture only takes 24 clock cycles to deliver blob location and related features. The tested architecture only utilizes 1597 logic element, or 4.81% of the FPGA total resources.
      1
  • Publication
    Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction
    ( 2020-08-01)
    Vijayasarveswari V.
    ;
    Allan Melvin Andrew
    ;
    Muzammil Jusoh
    ;
    Thennarasan Sabapathy
    ;
    Rafikha Aliana A Raof
    ;
    Mohd Najib Mohd Yasin
    ;
    R Badlishah Ahmad
    ;
    Khatun S.
    ;
    Hasliza A Rahim @ Samsuddin
    Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multistage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multistage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.
      2  39
  • Publication
    Development of statistically modelled feature selection method for microwave breast cancer detection
    (Semarak Ilmu Publishing, 2025)
    Vijayasarveswari Veeraperumal
    ;
    Norfadila Mahrom
    ;
    Rafikha Aliana A Raof
    ;
    Phak Len Al Eh Kan
    ;
    Muhammad Amiruddin Ab Razak
    ;
    Bavanraj Punniya Silan
    ;
    Yusnita Rahayu
    ;
    Mohd Wafi Nasrudin
    ;
    Ahmad Ashraf Abdul Halim
    ;
    Nuraminah Ramli
    Microwave technology is very promising tool for breast cancer detection. Microwave transmits and receives UWB signals. UWB signals carries information of the breast cancer. UWB signals need to be pre-processed in order to remove irrelevant and redundant features. Feature extraction and feature selection methods are mostly used to remove the unwanted features. In this paper, a statistically modelled feature selection (SMFS) method is proposed for microwave breast cancer detection. Initially, performance of different feature extraction and feature selection method are analysed using Anova test (p-value) and machine learning (SVM, DT, PNN, NB) accuracy. The best feature extraction and feature selection methods are combined and tested. Based on the performance of feature extraction and feature selection method, Combined Neighbour Component Analysis (feature selection) and Statistical features (feature extraction) are combined and tested. This method is able to achieve up to 85%. The result proves two stage methods are able to improve the accuracy compared to single stage method. Therefore, SMFS is able to detect breast cancer efficiently.
      1  4