Sukhairi Sudin
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Preferred name
Sukhairi Sudin
Official Name
Sukhairi, Sudin
Alternative Name
Sudin, S.
Main Affiliation
Scopus Author ID
56572705200
Researcher ID
GFW-2221-2022

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Cycling performance prediction based on cadence analysis by using multiple regression

2021-12-01 , Sukhairi Sudin , Aziz Naim Abdul Aziz , Fathinul Syahir Ahmad Sa'ad , Nurul Syahirah Khalid , Ismail Ishaq Ibrahim

This project examined the influence of the cadence, speed, heart rate and power towards the cycling performance by using Garmin Edge 1000.Any change in cadence will affect the speed, heart rate and power of the novice cyclist and the changes pattern will be observed through mobile devices installed with Garmin Connect application. Every results will be recorded for the next task which analysis the collected data by using machine learning algorithm which is Regression analysis. Regression analysis is a statistical method for modelling the connection between one or more independent variables and a dependent (target) variable. Regression analysis is required to answer these types of prediction problems in machine learning. Regression is a supervised learning technique that aids in the discovery of variable correlations and allows for the prediction of a continuous output variable based on one or more predictor variables. A total of forty days' worth of events were captured in the dataset. Cadence act as dependent variable, (y) while speed, heart rate and power act as independent variable, (x) in prediction of the cycling performance. Simple linear regression is defined as linear regression with only one input variable (x). When there are several input variables, the linear regression is referred to as multiple linear regression. The research uses a linear regression technique to predict cycling performance based on cadence analysis. The linear regression algorithm reveals a linear relationship between a dependent (y) variable and one or more independent (y) variables, thus the name. Because linear regression reveals a linear relationship, it determines how the value of the dependent variable changes as the value of the independent variable changes. This analysis use the Mean Squared Error (MSE) expense function for Linear Regression, which is the average of squared errors between expected and real values. Value of R squared had been recorded in this project. A low R-squared value means that the independent variable is not describing any of the difference in the dependent variable-regardless of variable importance, this is letting know that the defined independent variable, although meaningful, is not responsible for much of the variance in the dependent variable's mean. By using multiple regression, the value of R-squared in this project is acceptable because over than 0.7 and as known this project based on human behaviour and usually the R-squared value hardly to have more than 0.3 if involve human factor but in this project the R-squared is acceptable.

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Development of Harumanis Mango Insidious Fruit Rot (IFR) Detection by Utilising Vibration-Based Sensors and PCA with Random Forest

2023-01-01 , Salleh N.M. , Abu Hassan Abdullah , Sukhairi Sudin , Noor Shazliza Zakaria

Utilising single or multiple modalities systems, non-destructive techniques have been used to assess and determine the quality of mango (magnifera indica L.). It is challenging to anticipate and varies by cultivar at what harvest maturity stage will result in the optimum postharvest quality. Insidious Fruit Rot (IFR) is a disease that affects mangoes. When infected with Insidious Fruit Rot (IFR), the mango variety Harumanis does not exhibit exterior mutilation at the time of harvest or during the mature stage. However, a lack of density in the sinus area can occasionally be detected. Traditional ways of locating the diseases or pests living in the mango are useless for the commercialization of the product. This research presents the investigation done on IFR infection detection using piezoelectric vibration sensors and electret microphones. Data derived by the sensors were processed using the PCA and Random Forest methods to determine the non-IFR and the mango afflicted with IFR. The proposed approach achieved correct classification and is expected to be useful for planters in detecting IFR correctly before Harumanis mangoes were marketed.

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Measurement of Negative Lightning Return Strokes Using a Proposed Small-Scale Parallel Plate Antenna at the Central Region in Peninsular Malaysia

2021-12-01 , Haris F.A. , Ab Kadir M.Z.A. , Sukhairi Sudin , Johari D. , Hamzah M.N.

Lightning can occur between the clouds (intra-clouds), ground-to-cloud (CG), and inside the clouds. The lightning strike hazards can be managed appropriately using a lightning detector system consisting of an antenna, buffer amplifier circuit, and a measurement device. In this study, the development and fabrication of small-scale parallel plate antenna were carried out by reducing the physical height and antenna dimension to measure the generated electric fields. The experimental work was conducted at the rooftop of the College of Engineering, Universiti Tenaga Nasional (UNITEN), Selangor, Malaysia, from August 2019 to March 2020. The total number of 115 return strokes (RS) of negative lightning events were recorded during the measurement period. Characterization of seven types of criteria for negative return stroke has been analysed and compared to the existing parallel plate antenna with a similar climate condition and different countries. Based on the comparative study, the proposed and the existing parallel plate antenna shows a good agreement. Hence, the proposed small-scale parallel plate antenna can be used as a portable, lightweight, and easy to install device for the lightning measurement system.

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Potential of Near-Infrared (NIR) spectroscopy technique for early detection of Insidious Fruit Rot (IFR) disease in Harumanis mango

2021-12-01 , Noor Shazliza Zakaria , Abu Hassan Abdullah , Fathinul Syahir Ahmad Sa'ad , Muhamad Khairul Ali Hassan , Mohd Nazri Abu Bakar , Saad A.R.M. , Sukhairi Sudin , Ibrahim M.F.

Harumanis mango 'Insidious Fruit Rot'(IFR), is one of the common issues that hampered the fruit quality and consequently lowered the premium value of Harumanis Mango. Physically and visually the affected fruit does not show any attributes that indicates the presence of IFR on any part of the fruit until it has been cut open. This paper investigates the feasibility of a non-destructive method to screen the Harumanis mango from IFR using near-infra red light and artificial neural network. A common NIR light emitting diodes of 1000nm wavelength was used as the light source to emit NIR light while a photodiode was used to measure the intensity of the reflected NIR light from Harumanis mango. Early detection of IFR were done manually by local expert using acoustic method by flicking fingers to detect any abnormality inside the fruit. Sample data on NIR Spectroscopy reflectance results of 120 samples were used to classify the presence of IFR using neural network. Mean value of NIR reflectance of RBG for Harumanis mango with an incidence of Insidious Fruit Rot are R= 0.651, G= 0.465 and B=0.458, while without IFR are R = 0.211, G=0.15 and B=0.146. Using MATLAB's neural network training tool, a training set regression was obtained with an accuracy value of 0.9805 for prediction of IFR, thus this value is very high in accuracy.

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Rssi-based for device-free localization using deep learning technique

2020-06-01 , Abdul Syafiq Abdull Sukor , Latifah Munirah Kamarudin , Ammar Zakaria , Norasmadi Abdul Rahim , Sukhairi Sudin , Hiromitsu Nishizaki

Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.

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Automated Negative Lightning Return Strokes Characterization Using Brute-Force Search Algorithm

2022-04-01 , Haris F.A. , Kadir M.Z.A.A. , Sukhairi Sudin , Jasni J. , Johari D. , Zaini N.H.

Over the years, many studies have been conducted to measure, analyze, and characterize the lightning electric field waveform for a better conception of the lightning phenomenon. Moreover, the characterization mainly on the negative return strokes also significantly contributed to the development of the lightning detection system. Those studies mostly performed the characterization using a conventional method based on manual observations. Nevertheless, this method could compromise the accuracy of data analysis due to human error. Moreover, a longer processing time would be required to analyze data, especially for larger sample sizes. Hence, this study proposed the development of an automated negative lightning return strokes characterization using a brute-force search algorithm. A total of 170 lightning electric field waveforms were characterized automatically using the proposed algorithm. The manual and automated data were compared by evaluating their percentage difference, arithmetic mean (AM), and standard deviation (SD). The statistical analysis showed a good agreement between the manual and automated data with a percentage difference of 1.19% to 4.82%. The results showed that the proposed algorithm could provide an efficient structure and procedure by reducing the processing time and minimizing human error. Non-uniformity among users during negative lightning return strokes characterization can also be eliminated.

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Rssi-based for device-free localization using deep learning technique

2020-06-01 , Abdul Syafiq Abdull Sukor , Latifah Munirah Kamarudin , Ammar Zakaria , Norasmadi Abdul Rahim , Sukhairi Sudin , Nishizaki H.

Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.

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IoT-based Carbon Monoxide (CO) Real-Time Warning System Application in Vehicles

2021-12-01 , Kamarudin A.A.A. , Nurul Syahirah Khalid , Ismail Ishaq Ibrahim , Muhammad Aizat Abu Bakar , Mahadi M.Z. , Shazmin Aniza Abdul Shukor , Sukhairi Sudin , Mohd Zamri Hasan , Humairah Mansor

The project is about develop a system and application for detect the presence of Carbon Monoxide(CO) in car, since recently there are many cases of drowning while sleeping in car due to inhaling CO. The build system are able to detect the presence of CO and provide warning about level of CO to the users. It uses Blynk application to monitors level of CO inside the vehicle, MQ-9 gas sensor as the input sensor, ESP 8266 as medium to send data to the application via IoT-based and the level concentration of CO is displayed on the LCD in real-time displayed. For the output, it has 3 different condition based on the level concentration of CO. This project has been testing in six different situation. Based on the result, ambience air and in car with open window situation have lowest of CO level. Meanwhile, the highest of CO level is detect in smoke that are produced from fuel combustion of the car exhaust at distance 5 cm. Additionally, Principal Component Analysis (PCA) is used to analysed the ability of this system in clustering for each situation. As a result, PCA have clearly clustering data for every situation with the value of PC1 is 71.82% and PC2 is 28.18%, hence it is verified that the build system is able to applied in detecting the presence of CO. This project is believed able in helping to reduce the numbers of cases people drowning while sleeping due to inhaling CO in the car.

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Cloud-based System for University Laboratories Air Monitoring

2020-09-21 , Abu Hassan Abdullah , Sukhairi Sudin , Mustafa M.H. , Fathinul Syahir Ahmad Sa'ad , Khairul Azwan Ismail , Muhammad Aizat Abu Bakar , Mohamed Elshaikh Elobaid Said Ahmed , Abdul Ghapar Ahmad , Zahari Awang Ahmad , Sara Yasina Yusuf

Indoor air such as house, shopping complex, hospital, university, office and hotel should be monitor for human safety and wellbeing. These closed areas are prone to harmful air pollutants i.e. allergens, smoke, mold, particles radon and hazardous gas. Laboratories in university are special room in which workers (student, technician, teaching/research assistants, researcher and lecturer) conduct their works and experiment. The activities and the environment will generate specific air pollutant which concentration depending to their parameters. Anyone in the environment that exposure to these pollutants may affect safety and health issue. This paper proposes a study of development of a cloud-based electronic nose system for university laboratories air monitoring. The system consists of DSP33-based electronic nose (e-nose) as nodes which measure main indoor air pollutant along with two thermal comfort variables, temperature and relative humidity. The e-noses are placed at five different laboratories for acquiring data in real time. The data will be sent to a web server and the cloud-based system will process, analyse using Neuro-Fuzzy classifier and display on a website in real time. The system will monitor the laboratories air pollutants and thermal comfort by predict the pollutant concentration and dispersion in the area i.e. Air Pollution Index (API). In case of air hazard safety (e.g., gas spills detection and pollution monitoring), the system will alert the security by activate an alarm and through e-mail. The website will display the API of the area in real-time. Results show that the system performance is good and can be used to monitor the air pollutant in the university laboratories.

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Hand-held shelf life decay detector for non-destructive fruits quality assessment

2024 , Nurul Syahirah Khalid , Nordiana Shariffudiin , Shazmin Aniza Abdul Shukor , Humairah Mansor , Masturah Tunnur Mohamad Talib , Sukhairi Sudin , Ismail I. Ibrahim , Mohd Zamri Hasan , Siti Marhainis Othman , N.D.N Dalila , M.Thaqif B.N Ashimi

Perishable food such as fruits have a limited shelf life and can quickly degrade if not properly stored. One method for detecting decay in these foods is the use of ethylene gas. Ethylene is a naturally occurring hormone that is released by fruits as they ripen. By measuring the levels of ethylene in the storage area, it is possible to detect when fruits and vegetables are starting to degrade. This information can then be used to act, such as removing spoiled produce and adjusting storage conditions, to extend the shelf life of the remaining products. By utilizing ethylene gas for early detection of decay, it is possible to improve food safety and reduce food waste. The project aims to utilized ethylene gas from perishable food such as fruits before decay. This project proposed portable or hand-held detection ethylene gas by including temperature and humidity. The sensor will be measuring the level of ethylene gas, temperature and humidity. Next, machine learning method; K-Nearest Neighbour(KNN) were used to evaluate the accuracy of the proposed system. This project, a hand-held decay detector for perishable food products is believed can help to prevent food waste by detecting early signs of spoilage in fruits.

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