Norfadila Mahrom
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Preferred name
Norfadila Mahrom
Official Name
Norfadila, Mahrom
Alternative Name
Mahrom, Norfadila
Mahrom, N.
Mahrom, Norfadilla
Main Affiliation
Scopus Author ID
57200080959
Researcher ID
DDY-3868-2022

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Review of big data application in smart manufacturing

2023-07-19 , Ahmad Faizal Annuar , Ab-Samat H. , Norfadila Mahrom

This paper reviews the application of big data in smart manufacturing. Currently, the application of big data into the manufacturing operation is still a premature undertaking, with cost, compatibility, and lack of expertise hindering decision-making for many organizations. The selected papers discuss a variety of situations in which big data analytics and its applications have been used to enhance decision-making in the manufacturing operations, productions, scheduling, quality assurance, maintenance, and sustainability with significant data analytics considerations, obstacles to extensive data analytics adoption, machine learning, and use of sensors for data extraction. This article presents the discussions in the manufacturing industry surrounding the use of big data analytics.

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Analysis of Dielectric Properties on Agricultural Waste for Microwave Communication Application

2017-12-11 , Nurul Ain Zulkifli , Wee Fwen Hoon , Norfadila Mahrom , Been Seok Yew , Yeng Seng Lee , Siti Zuraidah Ibrahim , Anusha Leemsuthep Am Phan

This paper presents the analysis of dielectric properties of agricultural waste for microwave communication application such as microwave absorber and antenna. The residues products - rice straw, rice husk, banana leaves and sugar cane bagasse were studied in the range between 1-20GHz. Firstly, the 2 types of resins namely Epoxy der 331 and Polyamine clear hardener were mixed with the agricultural waste materials to produce the small size of agricultural waste sample. Then, the sample were measured using PNA network analyzer. The permittivity and tangent loss of different agricultural waste samples have been measured using dielectric probe technique. Besides, other objectives of this paper is to replace the conventional printed circuit board (PCB) using FR4, Taconic, and Roger material with the agricultural waste material. Besides that, the different percentage of filer for each agricultural waste materials were also investigated to specify the best material to be used as the substrate board and as the resonant material. the result shows the average of dielectric constants and the average of the tangent loss of agricultural waste materials.

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Design and Implementation of FPGA-based Single Computing Engine of VLC Image Transfer

2023-10-06 , Ismail S.N. , Mohd Rashidi Che Beson , Salih M.H. , Norfadila Mahrom

This paper has proposed a single computing engine based on VLC technology for use in real-time to secured image of transmitter and receiver systems implemented on an FPGA in real time. It is proposed that a single computing engine system consist of the following components be implemented: UART control, FIFO buffer, VGA controller, and 128-bits AES algorithm decryption and encryption. An Altera DE1-SoC board is used to implement the design, coded in VHDL, and implemented in Quartz prime 15.1 FPGA using a software platform system architecture. The single computing engine communication via VLC system hardware provides the highest security benefit with excellent image quality and unnoticeable local area communication security features. It has been demonstrated through implementation results that the single computing engine can operate at a maximum clock frequency of Fmax 170.97 MHz and achieve a throughput of 1.367 Mbps with the design single computing engine.

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Shared cache partitioning based on performance gain estimations

2020 , Norfadila Mahrom , M J Liebelt

In multiprocessor systems, dynamic cache distribution has been used to increase system performance by effectively partitioning the cache resources. However, different performance metrics used at runtime used to dynamically decide the partition sizes can give different impacts on performance, as well as varying impacts on the hardware cost of the system. In this paper, we propose an Adaptive CPI-based Cache Partitioning (ACCP) scheme to provide better utilisation of the shared cache resources among the competing applications in the system. ACCP uses performance gain estimations of the cache, without incurring significant hardware overhead. It aims to allow all applications in the system to run at approximately the same speed by accelerating the slowest application without significantly decelerating the others. We evaluated the ACCP on a quad-core system on which it achieved on average 23% reduction in miss rate, compared to an unpartitioned shared cache. ACCP also yields a similar IPC throughput improvement to a well-known UCP scheme, and better performance compared to the CPI by Muralidhara et al. Overall, the throughput of the system is improved at minimal complexity without yielding significant additional hardware cost. Hence, ACCP shows better overall performance in managing the hardware overhead compared to the UCP scheme.

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Development of statistically modelled feature selection method for microwave breast cancer detection

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.

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Android-based timetable manager for university students using rule-based algorithm

2023 , Lim Yan Yi , Norfadila Mahrom , Calvin Lim

Currently, there are several drawbacks in the existing university timetable system, including students do not know the class hour of subjects that they are going to register during the registration period. This situation has resulting in clashing in the class hour of the registered subjects. In addition, some students are not aware of this situation until it is too late from them to drop and register other subjects. This particularly happened to those who has more than two registered subjects clash and resulting in overlook at the rest of clashed subjects. Besides, students find it is inconvenient to view the timetable through the current timetable system. Thus, the development of an Android based mobile application that able to notify students if the class hour of the subjects they registered are overlapping is presented in this paper. The application also able to manage the student timetable by reporting details such as venue, day and the time of clashed subjects. Then, the application will suggest student to select elective subjects or subjects that students wish to retake to prevent clashing in class hour. The software development of the application is based on the Agile model and using rule-based algorithm. The application is developed in Android Studio by using Java as the programming language and all data is stored in Firebase Realtime Database. In conclusion, this paper presents results of the application that has successfully designed and developed.

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Enhancement and segmentation of Ziehl Neelson sputum slide images using contrast enhancement and Otsu threshold technique

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.

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Tuberculosis Classification Using Deep Learning and FPGA Inferencing

2023-02-01 , Fazrul Faiz Zakaria , Asral Bahari Jambek , Norfadila Mahrom , Rafikha Aliana A Raof , Mohd Nazri Mohd Warip , Al Eh Kan P.L. , Mustapa M.

Among the top 10 leading causes of mortality, tuberculosis (TB) is a chronic lung illness caused by a bacterial infection. Due to its efficiency and performance, using deep learning technology with FPGA as an accelerator has become a standard application in this work. However, considering the vast amount of data collected for medical diagnosis, the average inference speed is inadequate. In this scenario, the FPGA speeds the deep learning inference process enabling the real-time deployment of TB classification with low latency. This paper summarizes the findings of model deployment across various computing devices in inferencing deep learning technology with FPGA. The study includes model performance evaluation, throughput, and latency comparison with different batch sizes to the extent of expected delay for real-world deployment. The result concludes that FPGA is the most suitable to act as a deep learning inference accelerator with a high throughput-to-latency ratio and fast parallel inference. The FPGA inferencing demonstrated an increment of 21.8% in throughput while maintaining a 31% lower latency than GPU inferencing and 6x more energy efficiency. The proposed inferencing also delivered over 90% accuracy and selectivity to detect and localize the TB.

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