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
    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.
      3  30
  • 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.
      1  20
  • Publication
    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.
      3  30
  • 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  36
  • 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
    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.
      7  30
  • Publication
    A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes
    ( 2019)
    Abdul Syafiq Abdull Sukor
    ;
    Ammar Zakaria
    ;
    Norasmadi Abdul Rahim
    ;
    Latifah Munirah Kamarudin
    ;
    Rossi Setchi
    ;
    Hiromitsu Nishizaki
    Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and features. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users’ activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users’ particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users’ varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model.
      19  16
  • 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  27
  • 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.
      1  20
  • 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.
      1  31