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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57075005500

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A framework design of web-based knowledge management system with NoSQL database

2023 , Rhui Jaan Ong , Rafikha Aliana A Raof , Suhizaz Sudin , Kar Yan Choong

In today's dynamic and information-rich environment, effective knowledge management has emerged as a critical determinant for organizations seeking to take advantage of intellectual resources. Nevertheless, it is unrealistic to expect experts who lack a background in Information and Communications Technology (ICT) to possess an advanced level of technological proficiency or linguistic analytical skills. This study provides a comprehensive framework targeted at developing a Web-based Knowledge Management System (WKMS) coupled with chatbot application that is precisely tuned to meet the complex challenges of organizing, disseminating, and utilizing knowledge related to agriculture sector. This paper elaborates on a framework from both functional and technical perspectives, including the identification of knowledge origins, the establishment of mechanisms for capturing and sharing knowledge, and the facilitation of collaborative knowledge generation. This study employing a methodological approach and adopts a three-tier architecture for framework construction, coupled with text processing for data preparation. The findings emphasize that a successful WKMS should prioritize not only technology infrastructure but also strategies for building a culture favorable to information exchange. This paradigm lays the groundwork for enhanced discovery, innovation, and decision-making in a variety of professional fields by fusing theoretical insights and pragmatic considerations.

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An introduction to double stain normalization technique for brain tumour histopathological images

2024 , Fahmi Akmal Dzulkifli , Mohd Yusoff Mashor , Rafikha Aliana A Raof , Hasnan Jaafar

Stain normalization is an image pre-processing method extensively used to standardize multiple variances of staining intensity in histopathology image analysis. Staining variations may occur for several reasons, such as unstandardized protocols while preparing the specimens, using dyes from different manufacturers, and varying parameters set while capturing the digital images. In this study, a double stain normalization technique based on immunohistochemical staining is developed to improve the performance of the conventional Reinhard’s algorithm. The proposed approach began with preparing a target image by applying the contrast-limited adaptive histogram equalization (CLAHE) technique to the targeted cells. Later, the colour distribution of the input image will be matched to the colour distribution of the target image through the linear transformation process. In this study, the power-law transformation was applied to address the over-enhancement and contrast degradation issues in the conventional method. Five quality metrics comprised of entropy, tenengrad criterion (TEN), mean square error (MSE), structural similarity index (SSIM) and correlation coefficient were used to measure the performance of the proposed system. The experimental results demonstrate that the proposed method outperformed all conventional techniques. The proposed method achieved the highest average values of 5.59, 3854.11 and 94.65 for entropy, TEN, and MSE analyses.

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Feature Targeted Image Enhancement for Acute Myeloid Leukemia

2023-01-01 , Rahman R.A. , Mashor M.Y. , Rafikha Aliana A Raof , Hassan R. , Nazahah Mustafa , Kanafiah S.N.A.B.M. , Rahman K.S.B.A. , Zulkeflee R.H.

Image enhancement is one of the pre-processing steps in various computer vision applications. The current image enhancement algorithm typically applies uniform enhancements across the entire image where this approach often falls short of accurately highlighting or enhancing the specific features due to the influence of the background color. Therefore, this paper proposes a feature-targeted image enhancement technique. Feature-targeted image enhancement (FTIE) algorithm is the improvement over the conventional technique. This method will only enhance the targeted feature instead of the entire image. Therefore, the targeted feature will be enhanced accurately without the influence of the background image. The FTIE method was done by extracting the target feature from the original images and then applying the enhancement method to that region only. Based on the 80 acute myeloid leukemia images, the proposed method showed a promising result, where the comparative analysis shows that the image produced from the proposed method surpasses other conventional methods in terms of structural similarity index (0.995), universal image quality index (0.996), peak signal-to-noise ratio (30.803), mean absolute error (0.002), correlation coefficient (0.997) and contrast enhancement-based image quality (1.743) values.

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Erratum: Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction (PLoS ONE (2020) 15:8 (e0229367) DOI: 10.1371/journal.pone.0229367)

2021-05-01 , Vijayasarveswari Veeraperumal , Allan Melvin Andrew , Muzammil Jusoh , R Badlishah Ahmad , Thennarasan Sabapathy , Rafikha Aliana A Raof , Mohd Najib Mohd Yasin , Sabira Khatun , Hasliza A Rahim @ Samsuddin

The authors are listed out of order. Please view the correct author order, affiliations, and citation here: V. Vijayasarveswari1, A.M. Andrew1, M. Jusoh1, R.B. Ahmad1, T. Sabapathy1, R.A.A. Raof1, M.N.M. Yasin1, S. Khatun2, H.A. Rahim1 1 Advanced Communication Engineering (ACE) Centre of Excellence, Universiti Malaysia Perlis, Kangar, Perlis, West Malaysia, 2 Faculty of Electrical & Electronic Engineering, Universiti Malaysia Pahang, Pekan, Pahang Vijayasarveswari V, Andrew AM, Jusoh M, Ahmad RB, Sabapathy T, Raof RAA, et al. (2020) Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction. PLoS ONE 15(8): e0229367. https://doi.org/10.1371/journal.pone.0229367 There are errors in the Funding statement. The correct Funding statement is as follows: The study was supported by Fundamental Research Grant Scheme (FRGS), Ministry of Education Malaysia under grant number: FRGS/1/2019/TK04/UNIMAP/02/3. No additional external funding was received for this study.

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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.

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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.

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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.

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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.

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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.

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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.

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