Mohamed Mydin Hj M.Abdul Kader
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Preferred name
Mohamed Mydin Hj M.Abdul Kader
Official Name
Mohamed Mydin , Hj M.Abdul Kader
Alternative Name
Kader, M. M.A.
Abdul Kader, M. Mohamed Mydin
Mohamed, M. M.A.K.
Abdul Kader, Mohamed Mydin M.
Kader, M. M.M.A.
Main Affiliation
Scopus Author ID
57202264868
Researcher ID
CYH-3060-2022

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  • Publication
    Environmental lighting towards growth effect monitoring system of plant factory using ANN
    (Semarak Ilmu Publishing, 2025-01)
    Mohamed Mydin Hj M.Abdul Kader
    ;
    Muhammad Naufal Mansor
    ;
    Wan Azani Wan Mustafa
    ;
    Ahmad Anas Nagoor Gunny
    Malaysia is currently driven to become another most developed country in the world. Among other priority sector is Food Sustainability. Along the process, our vegetable supply-demand keeps increasing by year. Compared to traditional systems, closed systems or its other name called hydroponic is getting more important for plant production, with artificial light which has many potential advantages, including better quality transplants, shorter production time and less resource use. To gain full profit from it, the quality of vegetables needs to be controlled efficiently. Climate conditions, especially temperature and light intensity, have a significant impact on vegetable growth and yield, as well as nutritional quality. Plant growth and development are influenced by a variety of environmental factors, the most important one is light intensity. Among the problems to be tackled in this research are plant growth manual observation, light intensity variation and abundance of growth-related data to be evaluated manually. Therefore, to solve these problems, the specific type of vegetable used here is lettuce. The proposed methods are, observation of plant growth conducted automatically round the clock in intervals of 15 minutes for the whole month (estimated mature period of lettuce), using images captured. At the same time, the proposed light intensity which is red & white to the ratio of 2:1 (optimum ratio recommended by previous researchers) will be used. The issue of data to be evaluated manually will be solved using Artificial Neural Network (ANN) architecture, in specific Deep Learning. Concisely, the results & analysis shows the research is successfully developed for plant growth monitoring by using artificial neural network which, reached 80% to 90% accuracy in the training and validation session that made the architecture sufficient for determining the growth of the said vegetable. This is indeed foreseen, will highly assist the farmer in better monitoring the growth rate of the plant.
  • Publication
    Methylene blue dye adsorption by using deep eutectic solvent pre-treated wood chips
    ( 2022-11-18)
    Muhammad Masrur Jamil
    ;
    Ahmad Anas Nagoor Gunny
    ;
    Mohd Sharizan Md Sarip
    ;
    Mohd Faizal Ab Jalil
    ;
    Mohamed Mydin Hj M.Abdul Kader
    ;
    Mohamed Syazwan Osman
    ;
    Zulkarnain Mohamed Idris
    Adsorption of Methylene Blue using biomass or bioproduct of industries has received major attention by the researcher and industries. In this study, wood chips treated using deep eutectic solvents (DES) was used for adsorption of methylene blue. The surface and pores of this wood chips were analyzed by using scanning electron microscope (SEM). One Factor At a Time (OFAT) method was carried out to study the effect of contact time and biomass dosage on adsorption efficiency. Evaluation of pretreated wood chips biosorption, isotherms and kinetics were also studied.. Wood chips treated with DES changes the surface area and the pores of wood chips and improved the adsorption process efficiency. Freundlich isotherm and pseudo-second show correlation coefficient of R2, 0.9591 and 0.9999 respectively. Wood chips treated using deep eutectic solvents demonstrate a great potential as a cheap biosorbent for the removal of methylene blue by looking at the isotherm and kinetic studies.
      3  49
  • Publication
    Automotive Mechanical Vehicle Starter
    (IOP Publishing, 2021-12-01)
    Mohamed Mydin Hj M.Abdul Kader
    ;
    Zol Bahri Razali
    ;
    Wan Azani Wan Mustafa
    ;
    Syahrul Affandi Saidi
    ;
    Ahmad Anas Nagoor Gunny
    ;
    Setumin S.
    ;
    Osman M.K.
    ;
    Idris M.
    ;
    Akbar M.F.
    ;
    Muhammad Anas Ahmad Sarbini
    ;
    Nor Syamina Sharifful Mizam
    This research is used to crank start automotive vehicle. There are many different system used in order to start-up vehicles using electric starter, in the time of battery low-power or totally drained. The purpose of this research is to help the driver to get out of this difficulty. Nowadays there are many people that have experienced such a bad moment, where they are stranded at road side due to malfunction starter in their car because of battery problem. Most of the vehicle electric starter failure is because of battery corrosion or battery undercharged. The importance of this research is to solve this problem. Starter is a vital part of the vehicle, without it no automotive vehicles able to operate. These starters will rotate an internal-combustion engine to initiate the engine's operation under its own power. Starters also can be malfunction too due to corroded electrical connections or an undercharged battery. This system can be used to solve this problem. This system used human energy by using mechanical parts in order to produce electrical power. In order to produce electrical current, workforce will be applied by rotating the wheel that already linked by belt and from that rotations will trigger a magnetic force and it will produce an electrical current and supply it into battery. This system is divided into two development; hardware development and software development. The hardware development involved, mechanical device which is used and electrical device such as monitor. For software development, Fritzing is used to construct circuit.
      26  33
  • Publication
    Urban farming growth monitoring system using Artificial Neural Network (ANN) and Internet of Things (IOT)
    (Semarak Ilmu Publishing, 2025-01)
    Mohamed Mydin Hj M.Abdul Kader
    ;
    Muhammad Naufal Mansor
    ;
    Wan Azani Wan Mustafa
    ;
    Ahmad Anas Nagoor Gunny
    As an introduction to this project, the growth-related traits, such as above-ground biomass and leaf area, are critical indicators to characterize the growth of indoor lettuce plants. Currently, non-destructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. It is also one of the problem statements in this project. Based on this project the next problem is manual control of nutrients may cause quality issues to the lettuce plant. If the nutrient supply is too much or less, it will disturb the growth of the lettuce plant either the lettuce plant is dead or stunted. This project is about urban farming growth monitoring system using Artificial Neural Network (ANN) and Internet of Things (IoT). In this project, a method for monitoring the growth of indoor lettuce plants was proposed by using digital images and an ANN using Deep Learning Architecture. DLA is mostly developed by the software of MATLAB or Python to insert and run the coding. DLA is mostly used for image detection, pattern recognition, and natural language processing through the graph for Neural Network. Next, the Internet of Things (IoT) is a medium to store images of indoor lettuce plant growth into the Cloud (Google Drive). Furthermore, it takes indoor lettuce plant images as the input, an ANN was trained to learn the relationship between images and the corresponding growth- related traits with other fixed parameters. The pH level parameters were controlled by other fixed parameters to take the images of indoor lettuce plant growth. The parameters used in this project are temperature and humidity. This helps to compare the results of Artificial Neural Network (ANN), widely adopted methods were also used. Concisely, this project is expected to develop the Deep Learning Architecture using an Artificial Neural Network (ANN) with digital images as a robust tool for the monitoring of the growth of indoor lettuce plants every 30 minutes per day. Generally, focused on an urban farming growth monitoring system using Artificial Neural Network (ANN) and the Internet of Things (IoT).
      3  1
  • Publication
    Deep Eutectic Solvent-Assisted Synthesis of Nanocrystalline Cellulose Adsorbent for Silver Nitrate Removal
    ( 2023-01-01)
    Ahmad Anas Nagoor Gunny
    ;
    Aminuddin N.H.
    ;
    Azalina Mohamed Nasir
    ;
    Hashim R.H.R.
    ;
    Jalil M.F.A.
    ;
    Pakir M.A.A.S.
    ;
    Mohamed Mydin Hj M.Abdul Kader
    ;
    Rahman A.
    This study introduces application of deep eutectic solvent-assisted of nanocrystalline cellulose adsorbent for silver nitrate removal. The study aims to offer new green and low energy method in producing nano crystalline cellulose (NCC). NCC from microcrystalline cellulose (MCC) was extracted by using synthesized acidic DES. NCC fabricated was found to have a capability to act as a biosorbent for removal of silver nitrate (AgNO3). The adsorption kinetics and isotherm of silver nitrate on NCC were studied by varies initial concentration of silver nitrate and contact time. The experimental data were analyzed by using Langmuir and Freundlich models of adsorption. The adsorption isotherm data were fitted well to Freundlich adsorption isotherm. The biosorption kinetics data obtained at different time have been analyzed using pseudo-first order and pseudo-second order equation. The experimental data fitted well to pseudo-second order kinetics model. Application of novel and green acidic DES for production of NCC from lignocellulose will contribute to a new paradigm in a green adsorption study.
      2  34
  • Publication
    Renewable Energy Driven Exhaust Fan for Use in Laboratory via IOT
    (Institute of Electrical and Electronics Engineers Inc., 2021-01-01)
    Mohamed Mydin Hj M.Abdul Kader
    ;
    Wan Azani Wan Mustafa
    ;
    Akbar M.F.
    ;
    Syahrul Affandi Saidi
    ;
    Ahmad Anas Nagoor Gunny
    ;
    Zol Bahri Razali
    ;
    Osman M.K.
    ;
    Setumin S.
    ;
    Idris M.
    ;
    Mahendran Gunaseakaran
    ;
    Nor Syamina Sharifful Mizam
    This paper discussed on the hardware product of renewable energy driven exhaust fan for use in laboratory via IOT. Ventilation is generally deployed in buildings for maintaining user's safety and health. This renewable energy driven exhaust fan is the most considered system in improving the energy saving while sustaining user's safety and health. If we can renew and reuse the energy we waste, it would help in some way to the problem of scarcity of energy, which is major threat of present world. Initial capital cost of solar systems is still quite high when it comes to generate power for domestic. By using the concept of wind turbines wind-generated electricity can be used for battery charging and for connection with the power grid. Hence this research proposes a prototype of Renewable Energy Driven Exhaust Fan for use in laboratory via IOT. This research presents a prototype of regenerating power by an exhaust fan. The generated power can be either used directly or can be stored in a battery. This exhaust fan also can be controlled and monitored via IOT. The objectives of this research are, to design and develop an exhaust fan that can be driven by renewable energy, to design and develop an exhaust fan that can be controlled by IoT and to collect data and analyze the power consumptions and power saving. Methods used in this research is to use power from battery to operate the Fan 1. Than this kinetic energy produced by Fan 1 is used to drive Fan 2 and Fan 3 which are now actually a pair of generators with the help of charging circuit to directly recharge the battery which at first used to power up Fan 1. Analysis is then carried out to evaluate the theory, which actually agreed to the initial theory as presented
      11  63
  • Publication
    Development of a Multi-Fan System (MFS) in a Plant Factory with Artificial Light
    ( 2022-01-01)
    Mohamed Mydin Hj M.Abdul Kader
    ;
    Zol Bahri Razali
    ;
    Wan Azani Wan Mustafa
    ;
    Muhammad Naufal Mansor
    ;
    Ahmad Anas Nagoor Gunny
    ;
    Akbar M.F.
    ;
    Osman M.K.
    ;
    Setumin S.
    ;
    Idris M.
    ;
    Bin Ramli M.A.
    ;
    Sharifful Mizam N.S.
    A plant factory is a factory that grows plants indoors. These indoor farms could be the key to solve food shortages in the world. Plant factories are operated in indoor spaces under controlled cultivation conditions such as light, temperature and humidity. Then, a multi-fan system (MFS) for single culture beds. The MFS had four fans which were installed on both the front and back sides of culture beds to generate airflow from two opposite horizontal directions by using the Internet of Things (IoT) via the access and connection of smartphone devices. The fans that push the air into the culture bed were air inlets while those that pull the air out of the culture bed were air outlets. The main problem is in plant factories with artificial light, a heat that is usually used to control the environmental parameters and the air velocity is generally lower than the optimum range required for plant growth. Compare to a plant factory without using a multi-fan, it no circulation of air in the container to ensure continuous gas exchange. This reduction in gas exchange can impact calcium uptake by the plants. The gas exchange makes the tip burn. Tip burn can have a significant impact on the salability of a lettuce crop. Based on the limitations that have been highlighted previously, this research has been carried out by using multi-fan and without multi-fan. To get the data that need to be compared. Then, to improve the airflow in a plant factory with artificial light and prevent tip burn occur on the lettuce itself. In a nutshell, this prototype is expected to help plant factories reduce tip burn symptoms on leaf lettuce and the airflow can improve the growth of indoor cultured lettuce.
      2  1
  • Publication
    Antioxidant and xanthine oxidase inhibitory activity of sesbania grandiflora extract
    ( 2024-07)
    Ahmad Anas Nagoor Gunny
    ;
    Ahmad Iqrak Azri
    ;
    Mohd Hishamuddin Che Mat
    ;
    Syaheerah Syazana Syalabiah Salim
    ;
    Mohamed Mydin Hj M.Abdul Kader
    Xanthine oxidase is a crucial enzyme in the regulation of uric acid, with elevated levels leading to gout and hyperuricemia. To combat these conditions, a synergistic approach using antioxidants and xanthine oxidase inhibitors presents a promising treatment strategy. Given the side effects of synthetic inhibitors, this study explores the natural xanthine oxidase inhibitory and antioxidant properties of Sesbania grandiflora leaves extract. The analysis revealed a significant free radical inhibition rate of 61.59 ± 0.90% at a concentration of 100 μg/mL. The SC₅₀ value for the leaves extract was 16.59 ± 0.67 μg/mL, compared to 7.76 ± 0.23 μg/mL for gallic acid, a positive control. The leaves extract also demonstrated notable efficiency in inhibiting uric acid formation, with an IC₅₀ value of 0.36 ± 0.341 μg/mL, although this was higher than the 0.01 ± 0.005 μg/mL value for allopurinol. A strong correlation (r = 0.9958) was found between the antioxidant activity and xanthine oxidase inhibition of the extract, underscoring its effectiveness. This study is the first to report the combined effects of xanthine oxidase inhibition and antioxidant activity in Sesbania grandiflora, highlighting its potential as a natural treatment for gout and related diseases.
      19  2