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

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  • 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.
  • 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.
  • Publication
    Biological sequence alignments: A review of hardware accelerators and a new PE computing strategy
    ( 2014)
    Mohd Nazrin Md Isa
    ;
    Muhammad Imran Ahmad
    ;
    Sohiful Anuar Zainol Murad
    ;
    Rizalafande Che Ismail
    ;
    Khaled Benkrid
    One of the most challenging tasks in sequence alignment is its repetitive and time-consuming alignment matrix computations. In addition, performing sequence alignment in hardware, i.e. FPGA requires more hardware resources as the number of processing elements is replicated to increase performance throughput. This paper first reviews the existing FPGA-based biological sequence alignment core architectures and then proposed an efficient scheduling strategy, the so-called overlap computation and configuration (OCC) towards realizing optimized biological sequence alignment core architecture targeting for pairwise sequence alignment. In this research work, double buffering-based core architecture have been proposed and implemented on Xilinx Virtex-5 FPGA. Results have shown that this approach gained more than 10K times speed-up as compared to the GPP solution.
  • Publication
    Optimizing ant colony system algorithm with rule-based data classification for smart aquaculture
    ( 2024)
    Mohd Mizan Munif
    ;
    Husna Jamal Abdul Nasir
    ;
    Muhammad Imran Ahmad
    <span>Aquaculture is one of many industries where the use of artificial intelligence (AI) techniques has increased dramatically in recent years. Internet of things (IoT), AI, and big data are just a few of the technologies being used in smart aquaculture to increase productivity, efficiency, and system sustainability of aquaculture systems. Data classification, which involves finding patterns and relationships in huge datasets, is one of the most important tasks in smart aquaculture. The ant colony system (ACS) has been used to solve a number of optimization issues, including data classification. To provide a more practical and successful solution, this study proposes an improved ACS algorithm for rule-based data classification in smart aquaculture. The proposed algorithm combines the advantages of ACS and rule-based classification to optimize the number of rules and accuracy. The experimental results showed that the proposed algorithm outperformed the traditional AntMiner algorithm in terms of the number of rules and accuracy. The improved pheromone update technique could potentially increase data classification accuracy and convergence in smart aquaculture systems.</span>
  • Publication
    Aquaculture monitoring system using multi-layer perceptron neural network and adaptive neuro fuzzy inference system
    ( 2024-01-01)
    Abu Hassan Abdullah
    ;
    Sukhairi Sudin
    ;
    Saad F.S.A.
    ;
    Muhamad Khairul Ali Hassan
    ;
    Muhammad Imran Ahmad
    ;
    bin Abdul Khalid K.A.
    The water quality is the most important parameter for aquatic species health and growth. The condition is very critical and is essential to monitor continuously. Poor water quality will affect health, growth and ability of the animal to survive. These also affected their harvesting yields based on the amount and size of the animal. The main water parameters such dissolved oxygen (DO), pH, temperature, salinity and turbidity are monitored and control for good water quality. The data were acquired by the developed instrument and send wirelessly through GPRS/GSM module to cloud-based database. The data were retrieved and the water quality is predicted using fuzzy logic and multi-layer perceptron. MATLAB software was used for the model which is developed based on Mamdani fuzzy interface system. The membership functions of fuzzy were generated, as well as the simulation and analysis of the water quality system. Results show that the performance of fuzzy method can improve system performance in monitoring the water quality. This system also provides alert signals to farmers based on specific limit value for the water quality parameters. This will help the breeders to make certain adjustment to ensure suitable water quality for the aquaculture system.
  • 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.
  • 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.
  • Publication
    AI Assisted and IOT Based Fertilizer Mixing System
    (Universiti Malaysia Perlis, 2024-06-03)
    Wan Mohd Faizal Wan Nik
    ;
    Shahrul Fazly Man@Sulaiman
    ;
    Muhammad Imran Ahmad
    ;
    Shafie Omar
    ;
    Tan Shie Chow
    ;
    Mohd Nazri Abu Bakar
    ;
    Fadhilnor Abdullah
    ;
    Muhammad Khamil Akbar
    Agriculture techniques, particularly fertilizer mixing, have significant impacts on crop productivity. Introducing IoT technology to agriculture can enhance productivity, and machine learning offers a mechanism to gain insights from data, making agricultural practices more efficient. This research aims to design an AI-assisted and IoT-based fertilizer mixing system for greenhouses. This system utilizes sensor data and AI algorithms, specifically the Support Vector Machine (SVM), to optimize fertilizer application. Results from the SVM classifier showed a 100% accuracy rate for temperature and humidity, 65% accuracy for phosphorus, 86% for nitrogen, and 100% for potassium. These findings demonstrate the potential of the proposed system to improve fertilizer efficiency while reducing labor and resource waste.