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

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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>

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Durio Zibethinus L Plantation Intelligent Web-Based Irrigation System using fuzzy logic (DuWIMS) based on IoT

2025-07 , Zahari Awang Ahmad , Muhammad Imran Ahmad , Shuhaizar Daud

In response to the challenges posed by the recent pandemic on traditional farming in Malaysia, this paper proposes DuWIMS, an innovative smart irrigation system that integrates a modern web interface with an IoT-based system. Utilizing an array of sensors for real-time data collection, the system enhances irrigation efficiency through a dual-mode operation: manual remote control and automated irrigation via a fuzzy logic model. This system, tested on a durian farm in Kedah, Malaysia, significantly reduced irrigation time from two hours to approximately 50 minutes for 65 trees and decreased water usage by about 25%. Furthermore, durian trees irrigated with this system exhibited improved growth compared to those under traditional irrigation methods. Operational efficiency was bolstered with remote irrigation control and reliable performance, as evidenced by no system downtime over a three-month period. The deployment of the fuzzy logic model on a cloud server, serving as a backend API, also proved cost-effective by negating the need for on-site computing units. These findings underscore the potential of DuWIMS to revolutionize agricultural practices, particularly in challenging times, by optimizing resource usage and promoting sustainable farming.

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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.

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IoT Enabled Mushroom Farm Automation with Machine Learning

2024-06-03 , Shafie Omar , Wan Mohd Faizal Wan Nik , Muhammad Imran Ahmad , Tan Shie Chow , Mohd Nazri Abu Bakar , Shahrul Fazly Man@Sulaiman , Fadhilnor Abdullah , Vikneshwara Ram Suppiah

Mushroom farming has gained prominence due to its significant contribution to the global market. One major challenge for mushroom cultivation is maintaining optimal environmental conditions, specifically temperature and humidity. Traditional farming methods, prevalent in many parts of the world, lack precise control over these parameters, often leading to poor yield. This paper presents an innovative approach combining the Internet of Things (IoT) and Machine Learning (ML) for mushroom farm automation. The proposed system employs the ESP8266 microcontroller with specific agricultural sensors for smart monitoring. To regulate the farm's environmental conditions, ML algorithms predict mushroom farm weather states: mild, normal, and hot. The ensemble ML model, comprising five classifiers – Decision Tree, Logistic Regression, K-nearest neighbor, Support Vector Machine, and Random Forest – delivers a commendable accuracy of 100% when combining predictions, surpassing the performance of individual classifiers. This integrated IoT and ML approach promises to revolutionize real-time automation and cultivation practices in the mushroom industry.

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Comparison between machine learning classifier based on face recognition

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.

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Design and development of an irrigation monitoring and control system based on blynk internet of things and thingspeak

2025-04 , Fahmy Rinanda Saputri , Ricardo Linelson , Muhammad Salehuddin , Danial Md Nor , Muhammad Imran Ahmad

The scarcity of water resources exacerbated by climate change poses a major challenge for sustainable agriculture. This study presents an Internet of Things (IoT)-enabled irrigation system designed for real-time monitoring and precise water control. Using the Blynk platform and ThingSpeak for data management, the system integrates sensors for soil moisture, temperature, and humidity with a NodeMCU module to optimize irrigation practices. Initial results demonstrate the system’s effectiveness in improving water use efficiency and supporting sustainable agricultural practices, providing a low-cost, accessible solution for small and medium-scale farmers.

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Publication

Optimizing ant colony system algorithm with rule-based data classification for smart aquaculture

2024-01-01 , Munif M.M. , Husna Jamal Abdul Nasir , Muhammad Imran Ahmad

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.

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Improving subset linear discriminant analysis algorithm using overlapping clustering

2023-08 , Thulfiqar H. Mandeel , Muhammad Imran Ahmad , Noor Aldeen A. Khalid , Mustafa Hamid Hassan , Mohammed Hayder Kadhim

In recent years, there have been many proposals to improve the performance of traditional linear discriminant analysis (LDA). One of these is subset-improving linear discriminant analysis (S-LDA), which is based on clustering the whole set of classes into subsets and performing the LDA locally on these subsets. However, this algorithm suffers from an improper mapping of the classes to the corresponding subset during the testing procedure due to the inevitable discrepancy between the images used for training and those for testing. This discrepancy is caused either by spatial distortion or noise. The wrong mapping is severe when the number of data samples is small which is a common scenario in biometric datasets. In this paper, overlapping clustering is proposed for class clustering, to overcome the aforementioned problem. The proposed algorithm outperforms the S-LDA by 35.4% in mapping accuracy when using the PolyU palmprint database, 36.96%, resp. 33.92% when using left resp. Right palm images from the IIT Delhi Touchless Palmprint Database.

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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.

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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.

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