Mohd Nazrin Md Isa
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Mohd Nazrin Md Isa
Official Name
Mohd Nazrin , Md Isa
Alternative Name
Md Isa, Mohd N.
Isa, M. Nazrin Md
Md Isa, M. N.
Isa, Nazrin
Isa, M. Nazrin M.
Main Affiliation
Scopus Author ID
56426995200
Researcher ID
N-1250-2017

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  • Publication
    Machine vision for laser defect in PV solar modules
    ( 2016)
    R. Shuaimi
    ;
    Rizalafande Che Ismail
    ;
    Mohd Nazrin Md Isa
    ;
    Sohiful Anuar Zainol Murad
    This paper presents a new methodology in inspection on laser scribe defect of PV thin film solar modules. The work focuses on the application of machine vision as an inspection tools which has successfully integrated in other manufacturing environment as pattern recognition utility. Compared to manual inspection by human, machine vision system could offer better measurement accuracy as scribe defects are extremely hard to detect due to their small sizes and complexity of the detection process. Studies were made to identify machine vision system screening capabilities to define different scribe defect by their inspection criteria. Current result with paper and broad samples indicates that the propose system can be used effectively to replace human evaluators that currently employs in manufacturing quality control. © 2016 IEEE.
  • Publication
    Electrical conductivity (EC) sensing system for paddy plant using the internet of things (IoT) connectivity
    ( 2020-01-08)
    Othaman N.N.C.
    ;
    Mohd Nazrin Md Isa
    ;
    Sohiful Anuar Zainol Murad
    ;
    Azizi Harun
    ;
    Shaiful Nizam Mohyar
    This paper presents the design and development of an IoT-based electrical conductivity system for measuring paddy soil nutrients. Relationship between electrical conductivity (EC) and the influence of soil temperature in precision farming will be discussed. In this work, the EC algorithm was modelled and verified using MATLAB and realized on Node MCU (ESP8266) microcontroller. Results showed that the measured data from the developed system is closed to the calibration solution conductivity that is 1.413mS/cm and 12.88mS/cm. It is also noted that the recorded electrical conductivity value increases with temperature.
  • Publication
    Enhancing fractal image compression speed using peer adjacent mapping with sum of absolute difference for computed radiography images
    ( 2020)
    N. A. Z. Rahman
    ;
    Rizalafande Che Ismail
    ;
    Ahmad Husni Mohd Shapri
    ;
    Mohd Nazrin Md Isa
    The encoding phase in full-search fractal image compression (FIC) is time-intensive as a sequential search must be performed through a massive domain pool to find the best-matched domain for each block of ranges. In this paper, a peer adjacent with the sum of absolute difference (SAD) mapping has been suggested to enhance the FIC speed while retaining the reconstructed image quality. The SAD similarity measure applied in searching the most matching domain between domain pool for a range before transformation in order to shorten the mapping process. Therefore, instead of performing a complete search in the next level, one requires to only search a close neighbourhood of the region computed from the previous search. The efficiency of the proposed method is evaluated using standard test image, SMPTE test pattern and standard computed radiography digital images from JSRT database, from which the peak signal-to-noise ratio (PSNR), compression time and compression ratio are calculated. The experimental results validate the effectiveness of the proposed method.
  • Publication
    A Novel Double Co-Transformation for a Simple and Memory Efficient Logarithmic Number System
    ( 2020-07-01)
    Basir M.S.S.M.
    ;
    Rizalafande Che Ismail
    ;
    Mohd Nazrin Md Isa
    To date, co-transformation architecture is typically used in resolving the singularity issue in the logarithmic number system (LNS). The co-transformation was first introduced by Coleman, by using a rule of sign(r1) ≠ sign(r2) which translate the singularity into an argument that can be stored in two identical look-up tables (LUTs) with size of 2k. Recently, a portable 32-bit chipset preferred a small LUT, hitherto a co-transformation architecture is rearranged. This paper presents a novel double co-transformation, by means of first-order co-transformation architecture that covers-0.5 < r < 0 region is extended to r >-1 to replace the triumvirate F, D and E tables occupy by the interpolator. The accuracy settings at the co-transformation is compromised with the worst case error of 0.5 ulp. The outcome revealed a double co-transformation with Lagrange interpolator shows a decline in the total bit by 13% compared to European Logarithmic Microprocessor (ELM). With a simple architecture, the proposed double co-transformation is a promise for a fast LNS system.
  • Publication
    Comparison between machine learning classifier based on face recognition
    (IEEE, 2023)
    Ibrahim Mahmood Rashid Al-Bakri
    ;
    Muhammad Imran Ahmad
    ;
    Mohd Nazrin Md Isa
    ;
    Mustafa Zuhaer Nayef Al-Dabagh
    With face recognition, machine learning is one of the computer sciences fields that is getting bigger the quickest. The goal of this study is to give a basic overview of machine learning and the algorithmic paradigms it provides. The study gives a detailed explanation of the basic ideas behind machine learning and the math that turns these ideas into methodologies that can be used in the real world, and discusses and compares the performance of various face recognition methods. Machine learning, a field of AI, has emerged as an important part of the digitizing approaches that have attracted a lot of interest. The purpose of this work is to provide a high-level overview of several of the most widely utilized and commonly used algorithmic techniques for machine learning currently available. The goal of this work is to help readers make educated decisions about the best algorithm for machine learning they should employ for a given task by highlighting the benefits and drawbacks of each method from an implementation point of view.
  • Publication
    Palmprint features matching based on KAZE feature detection
    ( 2021-06-11)
    Khalid N.A.A.
    ;
    Ahmad M.I.
    ;
    Mandeel T.H.
    ;
    Mohd Nazrin Md Isa
    Palmprint is very popular biometric recognition system that is able to guarantee high accuracy. It has attracted increasing amount of attention because palmprints are abundant of many characteristics, such as the principle lines, ridges, minute points and textures for the use of images with low resolution. In this paper we propose palmprint feature detection based on KAZE technique. Palmprint texture has many important points for discrimination process. Selecting the best number of point using KAZE is very important for classification process in order to avoid overlapping features in different class. The experimental work has been done using polyU palmprint database in order to evaluate the best number of features.
      1  14
  • Publication
    "Look & Blink" two step verification security log in system
    ( 2021-03-01)
    Kai Qi L.
    ;
    Razaidi Hussin
    ;
    Afzan Kamarudin
    ;
    Rizalafande Che Ismail
    ;
    Mohd Nazrin Md Isa
    ;
    Naziri S.Z.M.
    A new artificial intelligence security design which is face recognition with eye blinking login system is proposed. It aims to strengthen the security account for each user using artificial intelligence technology and increase speed and user convenience for security during login. The face of a person cannot be copied and it can replace the username of the user, while eye blinking detection is another step for double verification replacing the password of a user. It is a two-step verification process that can be applied to all sorts of account login field so that this technology can replace the old school username with a password security system. The recognition system used a real-time where it is reducing the number of hackers in the field as it is impossible to hack a person's real-time face. The proposed system has been tested and analyzed the functionality by accessing the personal account in the university's portal.
      2  38
  • Publication
    Signal propagation modelling for vehicle-to-infrastructure communication under the influence of metal obstruction
    ( 2021-12)
    Jamie Siregar Cynthia Turner
    ;
    Shahriman Abu Bakar
    ;
    Azizi Harun
    ;
    Mohd Sani Mohamad Hashim
    ;
    Zuradzman Mohamad Razlan
    ;
    D L Ndzi
    ;
    Rizalafande Che Ismail
    ;
    Sohiful Anuar Zainol Murad
    ;
    Mohd Nazrin Md Isa
    ;
    Shaiful Nizam Mohyar
    ;
    Mohammad Fadzli Ramli
    ;
    M K N Zulkifli
    Connected car has become one of emerging technology in the automotive industries today. This development preludes a rise in vehicular communication studies that primarily targets radio channel modelling on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication mode. Considering vehicular obstruction, vast channel propagation studies have focused more on V2V mode while others consider the typical urban scenarios consisting of high traffic volumes of moving vehicles. Due to challenging propagation mechanisms and high complexity in such areas, radio propagation models applied in simulators assume an obstacle-free environment rather than considering the least effect imposed by metal obstruction on communication signal. Besides, there are limited studies pertaining to metal obstruction that considers several under-explored environments such as actual parking lots, junctions and other road infrastructure support. As such, this paper demonstrates signal attenuation analysis caused by the presence of metal objects in low density over obstacle-free environment on actual parking lot via V2I mode. Two scenarios such as LOS and NLOS conditions consisting of obstacle-free, cars and buses as static metal objects are evaluated. The aim of this research is to characterize signal strength caused by metal blockage on radio wave propagation predicated on the presence of vehicles as a subject of obstruction in comparison to obstacle-free vehicular environment. The validity of data is shown through received signal strength indicator (RSSI) and approximation analysis (RMSE) to demonstrate the efficiency of obtained measurements. The results demonstrated that Log-normal shadowing model yields the best fit to low-density metal obstruction scenario with smallest RMSE of 4.78 under bus obstruction whereas 5.72 under car obstruction.
      5  22
  • Publication
    e-PADI: an iot-based paddy productivity monitoring and advisory system
    ( 2019)
    M.A.F. Ismail
    ;
    Mohd Nazrin Md Isa
    ;
    S. N. Mohyar
    ;
    Muhammad Imran Ahmad
    ;
    M. N. M. Ismail
    ;
    Rizalafande Che Ismail
    ;
    A. Harun
    ;
    Sohiful Anuar Zainol Murad
    Rice is source of food calories and protein.  This second most widely grown cereal crop is the staple food for more than half the world’s population especially in developing countries.  The ability of global rice production to meet population demands (now estimated at more than 5 billion and projected to rise to 8.9 billion by 2050) remains in uncertainty in the near future unless challenges in rice production are properly addressed [1-3]. This paper proposed an IoT (Internet of things)-based paddy productivity monitoring and advisory system Using Dash7 Wireless Network Protocol. All collected data will be stored in a database management system to enable users to retrieve data either from tablets, smartphones or computers. The heart of the system is the ATmega328p microcontroller that will control the payload of the wireless network of dash7 and read data from sensor nodes. Results show all data from sensor nodes in representation of graph for analysis purpose.
      36  7
  • Publication
    Image processing for paddy disease detection using K-means clustering and GLCM algorithm
    ( 2021-12)
    A. F. A. Ahmad Effendi
    ;
    Mohd Nazrin Md Isa
    ;
    Muhammad Imran Ahmad
    ;
    Mohd Fikri Che Husin
    ;
    Siti Zarina Md Naziri
    The traditional human-based visual quality inspection approach in agriculture is unreliable and uneven due to various variables, including human errors. In addition to the lengthy processing durations, the traditional method necessitates plant disease diagnostic experts. On the other hand, existing image processing approaches in agriculture produce low-quality output images despite having a faster computation time. As a result, a more comprehensive set of image processing algorithms was used to improve plant disease detection. This research aims to develop an efficient method for detecting leaf diseases using image processing techniques. In this work, identifying paddy diseases based on their leaves involved a number of image-processing stages, including image pre-processing, image segmentation, feature extraction, and eventually paddy leaf disease classification. The proposed work targeted the segmentation step, whereby an input image is segmented using the K-Means clustering with image scaling and colour conversion technique in the pre-processing stage. In addition, the Gray Level Co-occurrence Matrix technique (GLCM) is used to extract the features of the segmented images, which are used to compare the images for classification. The experiment is implemented in MATLAB software and PC hardware to process infected paddy leaf images. Results have shown that K-Means Clustering and GLCM are capable without using the hybrid algorithm on each image processing phase and are suitable for paddy disease detection.
      1  74