Abdul Syafiq Abdull Sukor
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Preferred name
Abdul Syafiq Abdull Sukor
Official Name
Abdull Sukor, Abdul Syafiq
Alternative Name
Abdull Sukor, Abdul Syafiq
Sukor, Abdul Syafiq Abdull
Sukor, Abdul Syafiq Bin Abdull
Abdull Sukor, A. S.
Sukor, A. S.A.
Main Affiliation
Scopus Author ID
57209073616
Researcher ID
L-8520-2019

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Superpixels-based automatic density peaks and fuzzy clustering approach in COVID-19 lung segmentation

2023-12 , Ooi Wei Herng , Aimi Salihah Abdul Nasir , Fatin Nabilah Shaari , Abdul Syafiq Abdull Sukor

Clustering algorithms that rely on minimizing an objective function suffer from the drawback of requiring manual setting of the number of clusters. This limitation becomes particularly evident when applied to image segmentation, where the large number of pixels can lead to memory overflow issues. To overcome this challenge, a reference of Automatic Fuzzy Clustering Framework (AFCF) for image segmentation method has been used as the comparison to the Density Peaks Clustering (DPC) algorithm. AFCF used superpixel algorithm to reduce the spatial information of data during computation, DPC algorithm to generate decision graph, and prior entropy-based fuzzy clustering (PEFC) algorithm to achieve fully automatic segmentation method in determining the number of cluster and the clustering result. In this study, 50 open-source healthy, COVID-19 and pneumonia infected radiographs dataset are acquired from the Kaggle and Github. The radiographs dataset that segmented by DPC is down sampling to 100*100 pixels due to overloading computation. At the end of the image segmentation, a segmentation performance evaluation is conducted based on sensitivity, specificity, accuracy, precision, recall, F-score and time consumed. The result shows that AFCF algorithm has the better overall performance with higher accuracy of 92.48% and F-score 0.9455. Meanwhile, the most highlighted evaluation index is drop to the time consume comparison, AFCF has around 2.7 times faster processing speed compare to DPC algorithm.

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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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Development of In-situ Sensing System and Classification of Water Quality using Machine Learning Approach

2022-01-01 , Abdul Syafiq Abdull Sukor , Mohamad Naim Muhamad , Ab Wahab M.N.

Quality of water applied to the agriculture sector is one of the factors for agriculture farming to be successful. The use of bad quality irrigation water can cause soil problems. In general, determining water quality model is one of the many interests as it can be used to classify the conditions of water. This project focuses on developing the in-situ sensing system of water quality sensors that can detect parameters of water quality such as pH level, electric conductivity, temperature and total dissolved solid. To validate the approach, there are three types of water samples in a dataset that was collected which include water pipes, soap water and drain water. The types of machine learning models used for classification process are Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree. The performance showed that SVM model was the highest, ANN was intermediate, and Decision Tree was the lowest. This shows that the SVM model of machine learning approach is the most suitable to be used as the classification model to classify the status of water quality.

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An IoT-based automated gate system using camera for home security and parcel delivery

2024-02-08 , Mohd Wafi Nasrudin , Jamaluddin A.F. , Ismail I. , Norfatihah Bahari , Vijayasarveswari Veeraperumal , Wan Azani Wan Mustafa , Abdul Syafiq Abdull Sukor , Abdul Rahim A.N.

The Internet of Things (IoT) has made it possible to set up smart home security and parcel delivery. Therefore, this work proposed an automated gate system using camera for home security and parcel delivery with integrated Internet of Things (IoT). An automated gate system will capture and identify the image of face visitors and delivery riders for admin authentication to open the gate and parcel box. This proposed work is controlled and monitored through mobile apps. The primary purpose and inspiration of this work are to help the delivery rider put the parcel into the parcel box provided if there is no person in the house, and the owner can pick up the parcel without being broken or robbed when she/he comes back home. When the delivery rider presses the button near the gate, the admin will receive the notification "Someone coming,". The admin will click the "okay"button and the system will take a picture using the camera in Blynk App. After the admin verifies that is the delivery rider, the admin will open the box and the delivery rider can access the parcel door box and put the goods inside the box. Another advantage of this work, it also allows familiar people to access our home. The same process with the delivery rider where the visitor needs to press the bell and the admin needs to verify before the visitor can access the single gate. The result indicates that this work is able to monitor and control the gate and parcel door box using an IoT application.

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Pattern Clustering Approach for Activity Recognition in Smart Homes

2022-01-01 , Abdul Syafiq Abdull Sukor , Ammar Zakaria , Latifah Munirah Kamarudin , Wahab M.N.A.

In recent years, studies in activity recognition have shown an increasing amount of attention among other researchers. Activity recognition is usually performed through two steps: activity pattern clustering and classification processes. Clustering allows similar activity patterns to be grouped together while classification provides a decision-making process to infer the right activity. Although many related works have been suggested in these areas, there is some limitation as most of them are focused only on one part of these two processes. This paper presents a work that combines pattern clustering and classification into one single framework. The former uses the Self Organizing Map (SOM) to cluster activity data into groups while the latter utilizes semantic activity modelling to infer the right type of activity. Experimental results show that the combined method provides higher recognition accuracy compared to the traditional method of machine learning. Furthermore, it is more appropriate for a dynamic environment of human living.

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Predictive Analysis of In-Vehicle Air Quality Monitoring System Using Deep Learning Technique

2022-10-01 , Abdul Syafiq Abdull Sukor , Cheik Goh Chew , Latifah Munirah Kamarudin , Mao X. , Nishizaki H. , Ammar Zakaria , Syed Muhammad Mamduh Syed Zakaria

In-vehicle air quality monitoring systems have been seen as promising paradigms for monitoring drivers’ conditions while they are driving. This is because some in-vehicle cabins contain pollutants that can cause drowsiness and fatigue to drivers. However, designing an efficient system that can predict in-vehicle air quality has challenges, due to the continuous variation in parameters in cabin environments. This paper presents a new approach, using deep learning techniques that can deal with the varying parameters inside the vehicle environment. In this case, two deep learning models, namely Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are applied to classify and predict the air quality using time-series data collected from the built-in sensor hardware. Both are compared with conventional methods of machine learning models, including Support Vector Regression (SVR) and Multi-layer Perceptron (MLP). The results show that GRU has an excellent prediction performance with the highest coefficient of determination value (R2) of 0.97.

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Smart Waste Management System

2022-01-01 , Ab Wahab M.N. , Tay S.C. , Abdul Syafiq Abdull Sukor , Mohamed A.S.A. , Mahinderjit Singh M.

The increasing amount of waste in landfill has created a serious environmental problem which demands a more reliable solution in handling the collection of wastes. To this date, recycling is one of the solutions to manage the waste as it collects and processes recyclable materials into new products instead of throwing the trash to the landfill. However, the consciousness of recycling in our society is still devastatingly lower than expected as people are faced with many challenges that impede them to recycle. One of the challenges is to segregate the waste according to its group. People are still having difficulty to clearly distinguish recyclable materials due to the lack of recycling knowledge. Thus, this paper aims to develop a system that can separate the waste automatically and channel them to the proper bins. To do that, a camera is used to capture the image of the waste. Then, image classification using deep learning model is used to classify different types of wastes. The developed model is then embedded in Raspberry Pi and a servo motor is used to direct the waste to the respective bins for real-world implementation. Experimental results show that the proposed system can identify the categories of waste within the accuracy of 77–85%. This system is expected to deliver the importance of recycling and cultivate recycling practices to the public and finally reduced waste generation on land.

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Predictive analysis of In-Vehicle air quality monitoring system using deep learning technique

2022 , Abdul Syafiq Abdull Sukor , Goh Chew Cheik , Latifah Munirah Kamarudin , Xiaoyang Mao , Hiromitsu Nishizaki , Ammar Zakaria , Syed Muhammad Mamduh Syed Zakaria

In-vehicle air quality monitoring systems have been seen as promising paradigms for monitoring drivers’ conditions while they are driving. This is because some in-vehicle cabins contain pollutants that can cause drowsiness and fatigue to drivers. However, designing an efficient system that can predict in-vehicle air quality has challenges, due to the continuous variation in parameters in cabin environments. This paper presents a new approach, using deep learning techniques that can deal with the varying parameters inside the vehicle environment. In this case, two deep learning models, namely Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are applied to classify and predict the air quality using time-series data collected from the built-in sensor hardware. Both are compared with conventional methods of machine learning models, including Support Vector Regression (SVR) and Multi-layer Perceptron (MLP). The results show that GRU has an excellent prediction performance with the highest coefficient of determination value (R2) of 0.97.

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Review of analysis of EEG signals for stress detection

2024-03-07 , Mazlan M.R. , Abdul Syafiq Abdull Sukor , Abdul Hamid Adom , Jamaluddin R.

Mental stress is one of the major contributors to a variety of health issues. Various measures and diagnoses have been created by neurologists and psychiatrists to determine the intensity of mental stress in its early phases. In the literature, several neuroimaging devices and methods for assessing mental stress have been presented. The key candidate chosen is the electroencephalogram (EEG) signal which contains valuable information regarding mental states and conditions. This paper presents reviews of current works on EEG signal analysis for assessing mental stress. The reviews emphasize the significant disparities in the research outcomes and claims of different results from various data analysis methodologies. Accordingly, methods of EEG signals analysis will be used to study the effect of various extracted features and classification methods that associate with mental stress. Apart from that, the utilization of Artificial Intelligence (AI) approach is also investigated to study its significance towards stress detection.

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Pattern Clustering Approach for Activity Recognition in Smart Homes

2022-01-01 , Abdul Syafiq Abdull Sukor , Ammar Zakaria , Latifah Munirah Kamarudin , Wahab M.N.A.

In recent years, studies in activity recognition have shown an increasing amount of attention among other researchers. Activity recognition is usually performed through two steps: activity pattern clustering and classification processes. Clustering allows similar activity patterns to be grouped together while classification provides a decision-making process to infer the right activity. Although many related works have been suggested in these areas, there is some limitation as most of them are focused only on one part of these two processes. This paper presents a work that combines pattern clustering and classification into one single framework. The former uses the Self Organizing Map (SOM) to cluster activity data into groups while the latter utilizes semantic activity modelling to infer the right type of activity. Experimental results show that the combined method provides higher recognition accuracy compared to the traditional method of machine learning. Furthermore, it is more appropriate for a dynamic environment of human living.

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