Muhammad Imran Ahmad
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Preferred name
Muhammad Imran Ahmad
Official Name
Muhammad Imran, Ahmad
Alternative Name
Ahmad, Muhammad Imran
Ahmad, M. I.
Imran Ahmad, Muhammad
Ahmad, Muhamad Imran
Main Affiliation
Scopus Author ID
57214845678
Researcher ID
GBE-1471-2022

Research Output
Now showing 1 - 10 of 13
  • 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
    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
  • Publication
    Image data compression using fast Fourier transform (FFT) technique for wireless sensor network
    ( 2024-02-08)
    Haron M.H.
    ;
    Mohd Nazrin Md Isa
    ;
    Muhammad Imran Ahmad
    ;
    Arshad M.A.M.
    ;
    Asral Bahari Jambek
    ;
    Siti Zarina Md Naziri
    ;
    Hussin R.
    ;
    Harun A.
    ;
    Rizalafande Che Ismail
    ;
    Shaiful Nizam Mohyar
    Agricultural settings present unique challenges for the transmission of huge amounts of images over long-range wireless networks. It is challenging to remotely gather data for transmission over a wireless network in research areas due to a lack of basic amenities like internet connections, especially in distant agricultural areas. In this research, the Fast Fourier Transform (FFT) method was used in conjunction with the Discrete Cosine Transform (DCT) method of image compression to achieve a higher compression ratio. In order for a Wireless Sensor Network (WSN) to provide compressed image data to a wireless based station, a LoRaWAN network has been identified. Low-power LoRaWAN networks may regularly transmit compressed images from an agricultural region to a monitoring system up to 15 km away. Images of golden apple snails were collected for this study from a variety of sources. The procedure was coded in MATLAB so that it could be run with input images being judged by the created algorithm. The input images can be compressed with a range of compression ratios (CR) from 3.00 to 50.00, as shown by the simulation results. Compressed image quality is measured not only by the above-mentioned criteria, but also by Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). According to the numbers, the best achievable compression ratio is 49.04, with an MSE of 172.72 and a PSNR of 25.75 at its highest.
      29  4
  • Publication
    Image data compression using discrete cosine transform technique for wireless transmission
    ( 2021-12)
    Mona H. Haron
    ;
    Mohd Nazrin Md Isa
    ;
    Muhammad Imran Ahmad
    ;
    Rizalafande Che Ismail
    ;
    Norhawati Ahmad
    Telemetry data transfer over long-range wireless network for internet of things-based applications presently gaining popularity and this trend continuous in the era of Industrial Revolution (IR 4.0). However, transmitting larger amount of data such as images is a challenging task and requires further attention and research. Moreover, transmitting data over open agricultural area requires this capability to collect field data for further research and analysis. This work aims to propose a suitable image compression technique and recommends for the best compression ratio as to address the aforementioned issue. Discrete Cosine Transform (DCT) is a well-known lossy-based image compression technique, which has been explored along with another compression algorithm known as Fast Fourier Transform (FFT). Comparison between the two most widely used compression algorithms was analyzed and discussed. In this paper, golden apple snail images are acquired from various databases which include the mature snail, adult female laying eggs, snail pink eggs on stem and snails in the water. A MATLAB code is written to implement both algorithms with input images from the database is tested on the developed algorithm. Simulation results have shown that the input images can be compressed with a different value of compression ratio (CR) ranging from 3.00 to 50.00. Other than that, it is noted that the quality of the compression ratio is 49.04 with Mean Square Error (MSE) of 172.72 and Peak Signal to Noise Ratio (PSNR) of 25.75.
      23  1
  • Publication
    Development of Soil Electrical Conductivity (EC) Sensing System in Paddy Field
    ( 2021-03-01)
    Othaman N.N.C.
    ;
    Mohd Nazrin Md Isa
    ;
    Razaidi Hussin
    ;
    Rizalafande Che Ismail
    ;
    Siti Zarina Md Naziri
    ;
    Sohiful Anuar Zainol Murad
    ;
    Azizi Harun
    ;
    Muhammad Imran Ahmad
    The amount of fertilisers affects electrical conductivity (EC), and it is one of the major causes of the paddy yield decrease. The overuse of fertilisers can lead to environmental pollution and contamination. This study designed to develop soil electrical conductivity (EC) sensing system in the paddy field using the smart farming application. In this work, the study conducted in Kampung Ladang, Kuala Perlis, and the soil samples collected from a random location at two different depths from the paddy field. The EC value for the developed system was near the calibration solutions (12880µS and 150000µS) and directly proportional to the temperature. From the laboratory soil results, the EC values of the soils were higher with fertiliser. However, the EC values for 0-10cm soil depth were higher than 10-20cm soil depth. The soil EC is inversely proportional to the depth of soil and directly proportional to the amount of nutrients. It observed that the soil EC is linearly related to the amount of nutrients and temperature. The EC value decreases with the increase of soil depth displays a low amount of salts in the deep soil, while, increases with the increase of temperature indicates high current flow.
      1  38
  • Publication
    Face Recognition System Based on Fusion Features of Local Methods Using CCA
    ( 2020-03-01)
    Nayef Al-Dabagh M.Z.
    ;
    Muhammad Imran Ahmad
    ;
    Mohd Nazrin Md Isa
    ;
    Said Amirul Anwar Ab Hamid@Ab Majid
    Information fusion is a solution espoused for enhancing a pattern recognition system's performance. A single representation précises the information and presents a single cue on the data; thus, information fusion is said to be more prolific as every feature set depicts a different outlook on the actual dataset. This paper recommends a face recognition system by utilizing fusion features of two local descriptor approaches. Firstly, blending of two most effective local face features, namely Gabor transform features and Local Binary Pattern (LBP), renders significantly improved performance compared to either individually: they complement each other wherein small appearance details are captured by LBP, while Gabor includes encoding facial shape for a wider range of scales. Secondly, to the combined feature vector, applying of the Canonical Correlation Analysis method (CCA) is done in order to extract discriminant characteristics for recognition. Lastly, a support vector machine (SVM) is deployed for classification, and K-nearest neighbor (K-NN) is utilized for feature matching. The technique is assessed against many challenging face datasets such as Yale database, with encouraging outcomes.
      31  4
  • Publication
    Factors that affect soil electrical conductivity (EC) based system for smart farming application
    ( 2020-01-08)
    Othaman N.N.C.
    ;
    Mohd Nazrin Md Isa
    ;
    Rizalafande Che Ismail
    ;
    Muhammad Imran Ahmad
    ;
    Hui C.K.
    This paper presents the design and implementation of a soil electrical conductivity (EC) based system for a smart farming application using Arduino MEGA microcontroller. This work aims to establish the co-relationship between the measured EC values from the developed system with the amount of required NPK (nitrogen, phosphorus, potassium) fertilizer. Experimental results show that the soil EC is directly proportional to the nutrient concentration and inversely proportional to the depth of the soil. Besides, the soil EC values reflect the soil salinity (salt concentration) where, the higher the EC value, the higher the salt concentration in the soil and vice versa. It is also noted that the EC values and the total dissolved solids (TDS) could be used to estimate the amount of required NPK fertilizer.
      1  42