Mohd Aminudin Jamlos
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Preferred name
Mohd Aminudin Jamlos
Official Name
Mohd Aminudin , Jamlos
Alternative Name
Aminuddin Jamlos, Mohd
Jamlos, M. A.
Jamlos, Mohd Aminuddin
Jamlos, Mohd A.
Jamlos, Mohd
Main Affiliation
Scopus Author ID
36010739800
57210119953
Researcher ID
AGU-7505-2022

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  • Publication
    Zero Index Metamaterial of Simulated Split Ring Resonator Element
    ( 2022-01-01)
    Mohd Aminudin Jamlos
    ;
    Othman N.A.
    ;
    Wan Azani Wan Mustafa
    ;
    Hasliza A Rahim @ Samsuddin
    ;
    Alfilh R.H.C.
    ;
    Mohamad Nur Khairul Hafizi Rohani
    Split Ring Resonator of Zero Index metamaterial element has been proposed. The Split Ring Resonator consists of four loops; a more modest loop inside a bigger one, with openings, consolidated into each loop at the far edges, and an expansion of the rectangular loop to realize the gap (split) which permits control of the capacitance. The split ring is designed and simulated using sophisticated simulation software to have accurate simulation results. Two waveguide ports of terminals have been used within the assigned unit cell boundary for the simulated purpose. A parametric study has taken place for the width and length of the split ring resonator to find the optimized design to have zero index at the desired frequency of 2.7 GHz. The optimized dimensions of the split ring resonator are 7.29 mm and 6.0 mm for width and length respectively. The split ring resonator successfully recorded zero index (phase) at the desired frequency of 2.7 GHz for low-frequency applications specifically for GHz ranges.
  • Publication
    IoT-based Machine Learning Comparative Models of Stream Water Parameters Forecasting for Freshwater Lobster
    ( 2024-05-01)
    Bakhit A.A.
    ;
    Sabli N.S.M.
    ;
    Jamlos M.F.
    ;
    Mohd Aminudin Jamlos
    ;
    Ramli N.H.
    ;
    Nordin M.A.H.
    ;
    Alhaj N.A.
    ;
    Ali E.
    Water quality parameters such as dissolved oxygen, pH, and mineral content are important factors for aquaculture. Predictive analytics can predict water conditions in aquaculture and significantly reduce the mortality probability of aquaculture products. This paper applied stream predictive analytics to the freshwater lobster farming dataset where its real-time data supplied by End Node Unit (ENU) which integrated with dissolved oxygen (DO), potential hydrogen (pH), electrical conductivity (EC), and total dissolved solids (TDS). The real-time data of ENU in Structured Query Language (SQL) is normally displayed for remote monitoring and the analytics will only be done after in different processing platform called batch analytics. Instead of batch, this paper demonstrates capability of stream analytics where the real-time data query from ENU streaming through Structured Query Language (SQL) right into R Studio and Autoregressive Integrated Moving Average (ARIMA) predictions executed on the query table simultaneously on the same processing platform. Previously, ARIMA, Neural Network Autoregressive (NNETAR), and Naïve Bayes, were run and evaluated in R Studio to identify the best algorithm for stream analytics. Prediction procedure in R studio start with importing real-time data stored in SQL database and stream into R Studio using command of “dbGetQuery(con,sql)”. These three models evaluated the performance of freshwater lobster water conditions, dissolved oxygen (DO), potential hydrogen (pH), electrical conductivity (EC), and total dissolved solids (TDS). The data was collected for six months, and 70% was used as training data and 30% as test data. Compared to NNETAR and Naïve Bayes, ARIMA fits the entire data set well for 7 days; the ARIMA model exhibited lower absolute errors for pH and electrical conductivity, with errors ranging from 0.04 to 1.7 across days, while the NNETAR model had generally lower errors for TDS, with errors ranging from 0.3 to 0.7; however, the Naïve Bayes model's performance varied, with the lowest error for DO on day (5) 0.15 but higher errors for other parameters and days, including the highest error for electrical conductivity on day (6) 6.2. In conclusion, the average absolute errors for DO, pH, EC, and TDS are 0.163, 0.064, 0.705, and 0.498, respectively. Our findings underscore the efficacy of ARIMA for comprehensive water quality via stream prediction while highlighting the nuanced strengths and weaknesses of each model in forecasting specific parameters. This study contributes to the aquaculture literature by providing a nuanced comparative analysis of predictive models tailored to freshwater lobster farming, emphasizing the imperative role of stream predictive modelling. It enables real-time monitoring of water quality parameters, ensuring prompt interventions to maintain optimal conditions, thereby minimizing risks, enhancing aquaculture productivity, and ultimately contributing to sustainable and efficient freshwater lobster farming practices.
  • Publication
    Parametric Study on The Rice Bran Protein Extraction Process Using Water as a Solvent
    ( 2022-01-01)
    Kanapathy M.
    ;
    Mohd Sharizan Md Sarip
    ;
    Syahrul Affandi Saidi
    ;
    Mohd Aminudin Jamlos
    ;
    Aris N.I.A.
    Rice bran is a by-product resulting from the milling process that is frequently underutilized as cattle food or disposed through open-burning despite of its high nutritional and nutraceutical properties. Thus, this research aims in recognizing and exploring rice bran and its extraction methods that could further cultivate in the industry. This study focuses on the rice bran extraction process using water assisted with ultrasonication. The relationship between the operational parameters such as the temperature, extraction time and sample-to-solvent ratio to the protein yield were studied. The rice bran protein was subjected to the surface functional group analysis using Fourier-transform infrared spectroscopy (FTIR). As a conclusion, the extraction temperature of 60°C, sample to solvent ratio of 10 % and extraction time of 25 mins were chosen as the best conditions for the protein extraction. The extraction of the protein from rice bran is highly profitable due to its nutritional and nutraceutical properties as well as it is readily available at low cost.
  • Publication
    Pap Smear Images Classification Using Machine Learning: A Literature Matrix
    ( 2022-12-01)
    Alias N.A.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    ;
    Alquran H.
    ;
    Hanafi H.F.
    ;
    Ismail S.
    ;
    Rahman K.S.A.
    Cervical cancer is regularly diagnosed in women all over the world. This cancer is the seventh most frequent cancer globally and the fourth most prevalent cancer among women. Automated and higher accuracy of cervical cancer classification methods are needed for the early diagnosis of cancer. In addition, this study has proved that routine Pap smears could enhance clinical outcomes by facilitating the early diagnosis of cervical cancer. Liquid-based cytology (LBC)/Pap smears for advanced cervical screening is a highly effective precancerous cell detection technology based on cell image analysis, where cells are classed as normal or abnormal. Computer-aided systems in medical imaging have benefited greatly from extraordinary developments in artificial intelligence (AI) technology. However, resource and computational cost constraints prevent the widespread use of AI-based automation-assisted cervical cancer screening systems. Hence, this paper reviewed the related studies that have been done by previous researchers related to the automation of cervical cancer classification based on machine learning. The objective of this study is to systematically review and analyses the current research on the classification of the cervical using machine learning. The literature that has been reviewed is indexed by Scopus and Web of Science. As a result, for the published paper access until October 2022, this study assessed past approaches for cervical cell classification based on machine learning applications.
  • Publication
    Multiple Partial Discharge Signal Classification Using Artificial Neural Network Technique in XLPE Power Cable
    ( 2023-02-01)
    Halim M.I.A.
    ;
    Razaly N.Z.M.
    ;
    Mohamad Nur Khairul Hafizi Rohani
    ;
    Norfadilah Rosle
    ;
    Auni W.N.
    ;
    Afifah Shuhada Rosmi
    ;
    Muhammad Zaid Aihsan
    ;
    Mohd Aminudin Jamlos
    ;
    Mas’ud A.A.
    According to partial discharge (PD) damage in the electrodes that are not entirely bridging, the presence of PD in the high voltage (HV) power cable might lead to insulation failure. PD defects can damage cross-linked polyethylene (XLPE) cables directly, which is one of the most critical electrical issues in the industry. Poor workmanship during cable jointing, aging, or exposure to the surrounding environment is the most common cause of PD in HV cable systems. As a result, the location of the PD signals that occur cannot be classified without identifying the multiple PD signals present in the cable system. In this study, the artificial neural network (ANN) based feedforward back propagation classification technique is used as a diagnostic tool thru MATLAB software in which the PD signal was approached to determine the accuracy of the location PD signal. In addition, statistical feature extraction was added to compare the accuracy of classification with the standard method. The three-point technique is also an approach used to locate PD signals in a single line 11 kV XLPE underground power cable. The results show that the statistical feature extraction had been successful classify the PD signal location with the accuracy of 80% compared to without statistical feature extraction. The distance between PD signals and the PD source affected the result of the three-point technique which proved that a lower error means a near distance between them.
  • Publication
    Inductance and Conductance Characteristic Effect Towards H-Shape Metamaterial Design Performances for Light/THz Application
    ( 2022-01-01)
    Mohd Aminudin Jamlos
    ;
    Marzuki M.K.
    ;
    Wan Azani Wan Mustafa
    ;
    Hasliza A Rahim @ Samsuddin
    ;
    Alkhayyat A.
    ;
    Rosmi A.S.
    Unit cell is a meta-atom structured to form a metamaterial. The size of the unit cell is related to the frequency of waves. The structure size is smaller than the wavelength of the incident waves. Since light frequency used is Terahertz, the size of unit cell is in nanometer. Unit cell geometrical shape is design using copper and it placed on the Rogers substrate. It designed based on the desired of the researcher. There is no specific design assigned to specific application. The objective in this paper is to exhibit the negative refractive index or negative index material that is capable to bend the light wave. H Shape design has been used widely as a design to manipulate the electromagnetic waves but none of them used for higher frequency such as light frequency. The negative value of material properties obtained from the simulation of the metamaterial at three different part which are at phase=0, real value and imaginary value. CST Microwave Studio used as a simulation software. The results show all the negative value of material properties obtained at different frequency range. However, the negative value of material properties at phase=0 and imaginary part is obtained at same frequency range. While for real part, the negative value for all material properties occurred at different frequency range. This H-Shape design is suitable to manipulate the lights radiation waves.
  • Publication
    5.8 GHz Circular Polarized Microstrip Feeding Antenna for Solar Panel Application
    ( 2020-12-18)
    Mohd Aminudin Jamlos
    ;
    Khairi M.
    ;
    Ariffah S.N.
    ;
    Wan Azani Wan Mustafa
    ;
    Syed Zulkarnain Syed Idrus
    ;
    Muhammad A.
    Circular polarized microstrip antenna have been proposed to establish connection among distributed solar farms. The base station antenna of each solar farm permits more precise on the targeting the radio signal and usually is placed at the open area or at a height place so that the radio waves to be transmitted will not be interrupted. For this paper, circularly polarized microstrip patch antenna (CPMSA) is designs and being reviewed. The patch antenna is based on low-cost, but lossy, and the substrate is made of Rogers RT 5880 (lossy). It consists of a rectangular radiator patch, which is fed by microstrip transmission line. In order to realized circularly polarized antenna, the patch has undergone some design modification to achieve circular rotation. Some technique is proposed to achieve CP antenna. The results indicate that the antenna that uses micro strip feed line technique yields 8.55 dB directive gain, return loss and axial ratio at -24.4 dB and 2.05 dB respectivel. The resonance frequency of 5.8 GHz is being selected since it is suitable used for point to point communication among distributed solar farms that located far from each others.
  • Publication
    Monitoring system to classify cervical cancer stages based on pap smear nucleus analysis
    ( 2020-12-15)
    Halim A.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    Nowadays, in the hospital, cervical cancer is in the higher rank (number two) of the most popular cancer among ladies in the world. This type of cancer develops in the woman's cervix, which the womb is the entrance. The nucleus of the normal cell is in a smaller size compared to the abnormal nucleus. The abnormal nucleus has a bigger size, which sometimes, the size cannot be identified accurately by seeing with bare eyes to classify the stages of cervical cancer. As the solution, to detect and classifying the cells using methods through Pap smear images technique for handling the paper objectives with the better performance required. This method may improve the accuracy of the detection and the classification which to show better performance with the balance data and samples. Based on all of the results classified, the five methods were compared such as Wolf method, Nick method, Niblack method, Bradley method, and Bernsen method has been determined. Then, the Bradley method showed the best result of the cervical cancer threshold which has been chosen in this project. Furthermore, method modification has been made as to the new method of detection for the nucleus successfully proposed as stated in the project objective. After that, the analysis of the specificity, accuracy, PSNR, sensitivity, and F-Measure determined. All of the results of data analysis showed that the proposed method has a high percentage of the accuracy in total average, in which the project system performance of nucleus detection is good. Nevertheless, the analysis of the nucleus feature in terms of size by obtaining the area and the perimeter of the foreground (nucleus) made as the classification of cells were classed into three classes has been successfully made as to the second project objective. The three classes of cells finally success to identify, those are the 'Abnormal Cell', 'Intermediate Cell', and 'Normal Cell'. This study could perhaps encourage researchers throughout the field in seeing the researched risk associated with some of the methods and to provide a solid base for design and implementing new algorithms or implementing new ones.
  • Publication
    Design of a Low-cost IoT-based Biofloc Water Quality Monitoring System
    ( 2024-02-01)
    Bakhit A.A.
    ;
    Jamlos M.F.
    ;
    Nordin M.A.H.
    ;
    Mohd Aminudin Jamlos
    ;
    Mamat R.
    ;
    Mohd Al-Hafiz Mohd Nawi
    ;
    Nugroho A.
    This paper proposes an IoT-based BFT water monitoring system that can measure water parameters such as pH, DO, TDS, and EC. The collected data is displayed remotely via the BLYNK cloud and Node-RED via an MQTT broker. Moreover, a mobile application monitors all water parameters in real-time, notifying users when a parameter exceeds the ideal value. This study suggests that the proposed system based on IoT is an excellent option for a cost-effective BFT system.
  • Publication
    Pap Smear Image Analysis Based on Nucleus Segmentation and Deep Learning – A Recent Review
    ( 2023-02-01)
    Alias N.A.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    ;
    Ismail S.
    ;
    Alquran H.
    ;
    Mohamad Nur Khairul Hafizi Rohani
    Cervical cancer refers to a dangerous and common illness that impacts women worldwide. Moreover, this cancer affects over 300,000 people each year, with one woman diagnosed every minute. It affects over 0.5 million women annually, leading to over 0.3 million deaths. Recently, considerable literature has grown around developing technologies to detect cervical cancer cells in women. Previously, a cervical cancer diagnosis was made manually, which may result in a false positive or negative. Automated detection of cervical cancer and analysis method of the Papanicolaou (Pap) smear images are still debated among researchers. Thus, this paper reviewed several studies related to the detection method of Pap smear images focusing on Nuclei Segmentation and Deep Learning (DL) from the publication year of 2020, 2021, and 2022. Training, validation, and testing stages have all been the subject of study. However, there are still inadequacies in the current methodologies that have caused limitations to the proposed approaches by researchers. This study may inspire other researchers to view the proposed methods' potential and provide a decent foundation for developing and implementing new solutions.