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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  • Publication
    Context-aware activity recognition and abnormality detection approaches in smart home environments
    ( 2019)
    Abdul Syafiq Abdull Sukor
    The rising number of elderly population has become a common concern in many countries around the world. The issue has impacted social and economic life of modern societies due to the fact that elderly people are known to suffer from many medical disabilities. As one of the solutions, current technologically-driven approaches, particularly in the area of smart home environments have been developed in recent years to support the independent living and reduce the caregivers’ burden in taking care of elderly individuals. Sensors installed in the environments are used to monitor users’ daily routine to see trends in the behaviour and to be informed of any abnormal activity. However, the accurate interpretation of sensor data in identifying human activities and their abnormal behaviour is still limited. Furthermore, pattern analysis involving these two areas are becoming an increasingly scientific challenge to the real-world environments. This study intends to deal with the issue by investigating appropriate means of pattern recognition and data mining methods within smart home environments. In particular, the study attempts to develop an intelligent reasoning system that can identify residents’ activities and abnormal behaviour of the smart home residents. In this study, two types of activities are identified, i.e., context-related and motion-related activities. The former is classified using the hybrid approach while the latter is performed through the ensemble-based machine learning techniques. The output models produced by these activity recognition approaches are then used as the input for the deep learning networks to produce behavioural model of smart home residents. Experimental procedures are then performed to validate the proposed approach. First, a comparison between the knowledge-driven model and hybrid activity model is carried out to identify the context-related activity. Then, another comparison between the performances of single classifier with multi-classifier system is also performed to identify the motion related activity. Furthermore, for the abnormality detection, several types of reasoning systems are used. These include the case-based reasoning (CBR), deep learning models composed of multi-layer perceptron network (DMLP) and deep recurrent neural network (DRNN) as well as the conventional machine learning algorithms such as naïve Bayes (NB), Support Vector Machine (SVM) and multi-layer perceptron neural network (MLP). The experimental results show that the proposed hybrid approach has better classification rate to identify context-related activity compared to the knowledge-driven model, where the accuracy is obtained at 98.7% ± 0.4. Meanwhile, the multi-classifier system performs better than a single classifier in identifying motion-related activity, with the accuracy of 99.6% ± 0.2. Moreover, DMLP shows higher accuracy rate (98.2%) compared to the DRNN, CBR and other machine learning algorithms for the abnormality detection system. The presented results show that this study can give an impact to the improvement of reasoning process in identifying abnormal situations in smart homes. This can be used in many applications especially in healthcare domains. Furthermore, this study helps to benefit future technologists in order to achieve Society 5.0.
  • Publication
    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.
      1
  • Publication
    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.
      2  18
  • Publication
    Abnormality Detection Approach in Smart Homes using Case-based Reasoning
    ( 2020-06-01)
    Abdul Syafiq Abdull Sukor
    ;
    Rossi Setchi
    ;
    Ze Ji
    Today, the population of elderly people is dramatically increasing. To help with the problem, smart homes provide technologies and services that can help elderly people to live independently and comfortably in their own homes. One such service in smart homes is the detection of abnormal situations based on individuals' daily routine. This is important as some situations can lead to serious health issues if they have not been detected in the early stage. This paper presents a conceptual model for abnormality detection using case-based reasoning. It utilizes previous cases, which are built from a publicly available smart home dataset. To evaluate the performance, the cases are divided into two case-based sizes which contain seven and fourteen days of monitoring task. To avoid bias, the performance is also measured against two voluntary individuals who have no knowledge of the dataset. The results show that the system is able to detect abnormal situations with the best accuracy of 81.3%.
      3  1
  • Publication
    Investigation of Different Classifiers for Stress Level Classification using PCA-Based Machine Learning Method
    ( 2023-01-01)
    Mazlan M.R.B.
    ;
    Abdul Syafiq Abdull Sukor
    ;
    Abdul Hamid Adom
    ;
    Jamaluddin R.B.
    ;
    Saidatul Ardeenawatie Awang
    Undergraduate students experience several changes and face various problems during their time transitioning from adolescence to adulthood. One of the issues during this time is a mental stress disorder. Stress burdens the students either through mental or physical capabilities. The common method of determining stress includes physical examination and clinical diagnosis. However, the method is subjective and time-consuming as doctors need to make sure that their diagnosis is accurate. Thus, the severity of the stress stages could not be easily determined. A new method using machine learning-based algorithms coupled with EEG devices promises to overcome the issues with the current approaches. This paper presents an investigation using machine learning techniques based on Principal Component Analysis (PCA) which allows for the reduction in the dimensionality of datasets to enhance their interpretability while minimizing information loss. The pre-processed EEG data and PCA-based EEG data were compared and analyzed using three machine learning classifiers such as K-Nearest Network (KNN), Naive Bayes (NB) and Multilayer Perceptron (MLP). The results indicate that KNN demonstrated the highest average classification accuracy of 99%, while the other approaches mentioned above averaged around 50% and 80% for NB and MLP respectively. This investigation shows that the KNN classifier is most suitable for the proposed approach.
      1
  • Publication
    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.
      1
  • Publication
    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 Xiaoyang
    ;
    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.
      3  1
  • Publication
    Real-time in-vehicle air quality monitoring system using machine learning prediction algorithm
    ( 2021-08-01)
    Goh C.C.
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    ;
    Nishizaki H.
    ;
    Nuraminah Ramli
    ;
    Mao X.
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Kanagaraj E.
    ;
    Abdul Syafiq Abdull Sukor
    ;
    Elham M.F.
    This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2 ). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.
      1
  • Publication
    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.
      3  14
  • Publication
    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.
      1