Erdy Sulino Mohd Muslim Tan
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Preferred name
Erdy Sulino Mohd Muslim Tan
Official Name
Erdy Sulino, Mohd Muslim Tan
Alternative Name
Mohd Muslim Tan, Erdy Sulino
Mohd Muslim Tan, E. S.
Tan, Erdy S.M.
Main Affiliation
Scopus Author ID
57210106471
Researcher ID
GEQ-7073-2022

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IoT health monitoring device of Oxygen Saturation (SpO2) and heart rate level

2020 , O.Y. Tham , M.A. Markom , Asyraf Hakimi Abu Bakar , Erdy Sulino Mohd Muslim Tan , A.M. Markom

People are normally suffering from body sickness such as heart disease, high blood pressure and diabetes when getting older. Thus, the health of elderly should be monitored to prepare for any emergency cases. This research presents a realtime monitoring system for elderly that is able to measure heart rate and Peripheral Capillary Oxygen Saturation Level (SpO2). The monitoring system is constructed using MAX30100 as front-end sensor and Node MCU (ESP8266) is used as microcontroller to collect and transfer the data to Cloud. Five healthy subjects have been chosen properly and their SpO2 and heart rate level are collected. All data undergone a few processes for validation such as segmentation and filtering. For SpO2, the data are computed to IR/RED variables. Then, the IR/RED are processed to get SpO2 ratio using empirically derived calibration curves in order to produce normal and abnormal results. For heart rate, a correlation test is conducted between the experimental reading with the reference reading. For the monitoring system, both SpO2 and heart rate data are combined to obtain the final classification of normal and abnormal. The result of the correlation test shows strong correlation value (rs=0.993). The percentage error is calculated between the developed system with a commercial oximeter which is resulted with less 3% and 1.03 % for SpO2 and heart rate, respectively. Based on the validation results, the monitoring system of SpO2 and heart rate is ready to be used. Also, the IoT system allows many authenticated users to monitor the patient condition.

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Fuzzy logic based prediction of micronutrients demand for harumanis mango growth cycles

2021-12-01 , Wan Mohd Nooriman Wan Yahya , Abu Hassan Abdullah , Norasmadi Abdul Rahim , Erdy Sulino Mohd Muslim Tan

Harumanis is a famous green eating mango cultivar that has been commercially cultivated in Malaysia's state of Perlis. A variety of nutrients are found in soil, all of which are necessary for plant growth. Micronutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K) are essential for Harumanis mango (Mangifera Indica) to growth. The importance of soil fertility in achieving high plant productivity and quality cannot be overstated. It should be used in a moderate amount and in a balanced manner. Predicting appropriate nutrients and the right timing to satisfy the tree's demands is critical. The aim of this study is to create a fuzzy logic-based system to analyse the results of soil tests for nitrogen (N), phosphorus (P), and potassium (K) in the Harumanis mango orchard. The interpreted data are used to estimate N-P-K nutrient levels and indicate the optimal fertilizer solution and application timing for each Harumanis growth stages. The system utilizes Fuzzy Logic Control (FLC) to predict the nutrients demand for Harumanis mango growth. Results shows the system able to calculate and predict values of required N-P-K fertilizer for optimal growth. Thus, assist farmers in predicting the proper amount of N-P-K to apply to Harumanis mango soil.

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EEG signal processing using deep learning for motor imagery tasks: Leveraging signal images

2025 , Saidatul Ardeenawatie Awang , Abdul Hamid Adom , Erdy Sulino Mohd Muslim Tan , Husna Najeha Amran , Arni Munira Markom

A novel approach to processing electroencephalography (EEG) signals has emerged, leveraging the utilization of signal images. The application of deep learning techniques in bypassing complex signal and image processing tasks has generated significant interest in this field. However, challenges remain in signal image processing, particularly in handling significant features and image sizes. This study presents a comprehensive investigation of EEG motor imagery signal processing, focusing on the classification of three tasks: eating, drinking, and seeking assistance. Fast Fourier Transform (FFT) is employed to extract signal image features, which are subsequently utilized in a deep learning framework. EEG data were collected from five subjects, and four transfer functions of deep learning models, namely VGG16, VGG19, ResNet50, and ResNet101, were employed for training and classification purposes. The performance of the four models was meticulously evaluated and compared. Notably, VGG16 exhibited superior performance in accurately classifying the EEG motor imagery tasks, achieving an impressive accuracy of 90%, sensitivity of 84%, and specificity of 92%. In conclusion, this study underscores the efficacy of EEG signal image processing through deep learning-based classification techniques. The findings highlight the potential of utilizing signal images in EEG analysis for motor imagery tasks, thereby contributing to the advancement of brain-computer interface technology and enhancing our understanding of neural dynamics.

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Nutrient Requirements and Growth Response of Harumanis Mango (Mangiferaindica L.) during Vegetative Shoot Growth Stages: A Mitscherlich Law Analysis

2023-01-01 , Erdy Sulino Mohd Muslim Tan , Markom M.A.B. , Allan Melvin Andrew , Abu Hassan Abdullah , Norasmadi Abdul Rahim , Fathinul Syahir Ahmad Sa'ad , Imaduddin Helmi Wan Nordin , Abidin M.A.Z. , Jamil S.H.F.S.A. , Yogesh C.

This study investigates the nutrient requirements of Harumanis mango (Mangifera indica L) during different vegetative shoot growth stages by analyzing the soil nutrient test value-relative growth relationships. The research utilizes the Mitscherlich Law to model the response of mango yield in relation to varying nutrient levels. The data came from experimental plots, and the results show the asymptotic behavior of mango yield for three essential nutrients: nitrogen (N), phosphorus (P), and potassium (K). For vegetative shoot growth1, the asymptotic yield was estimated at 665.5 with a decline rate of -3.39 concerning N, -2.17 concerning P, and -1.35 concerning K. The coefficient of determination (R2) was 0.934, indicating a high goodness of fit for the model. Similar trends were observed for vegetative shoot growth2 and 3, where the asymptotic yields and nutrient decline rates varied accordingly. This study provides crucial insights into Harumanis mango nutrient needs across growth stages, aiding orchard management for sustainable yields. Applying the Mitscherlich Law enhances our understanding of how nutrients affect mango growth. These findings support targeted fertilization, boosting productivity and orchard efficiency. Future research can explore more growth factors and long-term nutrient impacts.

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Analysis of Soil Nutrient (NPK) Test Value - Relative yield Relationship for Harumanis Mango using Modification Arcsine-Log Calibration Curve.

2023-01-01 , Erdy Sulino Mohd Muslim Tan , Markom M.A.B. , Allan Melvin Andrew , Abu Hassan Abdullah , Norasmadi Abdul Rahim , Fathinul Syahir Ahmad Sa'ad , Imaduddin Helmi Wan Nordin , Abidin M.A.Z. , Jamil S.H.F.S.A. , Yogesh C.K.

The cultivation of Harumanis mango (Mangifera indica) is of significant agricultural importance, especially in tropical regions like Malaysia, where it is renowned for its exceptional taste and quality. Maximizing mango yield and maintaining fruit quality are vital aspects of successful cultivation, relying on optimal soil nutrient management, particularly nitrogen (N), phosphorus (P), and potassium (K). In this research, the soil nutrient test value - relative yield relationship for Harumanis mango is investigated using a modification arcsine-log calibration curve. Traditional linear calibration curves may not fully capture the nonlinearities observed in crop responses, potentially leading to inaccurate nutrient requirements for optimal yield. By employing the innovative modification arcsine-log calibration curve, a more precise and robust relationship between soil nutrient test values and relative mango yield is established. Soil samples are collected from mango orchards, and NPK levels are measured using standardized laboratory techniques, alongside corresponding relative mango yields. This study advances precision agriculture by offering precise soil nutrient recommendations for mango farmers. Utilizing calibrated curves improves mango yield, minimizes nutrient waste, and encourages sustainable farming. In conclusion, the modified arcsine-log calibration curve reveals vital insights for optimal Harumanis mango production, benefiting the industry and sustainability.

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A Review: Deep Learning Classification Performance of Normal and COVID-19 Chest X-ray Images

2021-11-25 , Marni Azira Markom , Taha S.M. , Abdul Hamid Adom , Abdul Syafiq Abdull Sukor , Aimi Salihah Abdul Nasir , Haniza Yazid , Erdy Sulino Mohd Muslim Tan , Arni Munira Markom

COVID19 chest X-ray has been used as supplementary tools to support COVID19 severity level diagnosis. However, there are challenges that required to face by researchers around the world in order to implement these chest X-ray samples to be very helpful to detect the disease. Here, this paper presents a review of COVID19 chest X-ray classification using deep learning approach. This study is conducted to discuss the source of images and deep learning models as well as its performances. At the end of this paper, the challenges and future work on COVID19 chest X-ray are discussed and proposed.

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