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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.
Chew Goh; Latifah Kamarudin; Ammar Zakaria; Hiromitsu Nishizaki; Nuraminah Ramli; Xiaoyang Mao; Syed Syed Zakaria; Ericson Kanagaraj; Abdul Abdull Sukor; Fauzan Elham. Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm. Sensors 2021, 21, 4956 .
AMA StyleChew Goh, Latifah Kamarudin, Ammar Zakaria, Hiromitsu Nishizaki, Nuraminah Ramli, Xiaoyang Mao, Syed Syed Zakaria, Ericson Kanagaraj, Abdul Abdull Sukor, Fauzan Elham. Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm. Sensors. 2021; 21 (15):4956.
Chicago/Turabian StyleChew Goh; Latifah Kamarudin; Ammar Zakaria; Hiromitsu Nishizaki; Nuraminah Ramli; Xiaoyang Mao; Syed Syed Zakaria; Ericson Kanagaraj; Abdul Abdull Sukor; Fauzan Elham. 2021. "Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm." Sensors 21, no. 15: 4956.
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%.
Abdul Syafiq Abdull Sukor; Rossi Setchi; Ze Ji. Abnormality Detection Approach in Smart Homes using Case-based Reasoning. 2020 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) 2020, 176 -181.
AMA StyleAbdul Syafiq Abdull Sukor, Rossi Setchi, Ze Ji. Abnormality Detection Approach in Smart Homes using Case-based Reasoning. 2020 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS). 2020; ():176-181.
Chicago/Turabian StyleAbdul Syafiq Abdull Sukor; Rossi Setchi; Ze Ji. 2020. "Abnormality Detection Approach in Smart Homes using Case-based Reasoning." 2020 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) , no. : 176-181.
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.
Abdul Syafiq Abdull Sukor; Latifah Munirah Kamarudin; Ammar Zakaria; Norasmadi Abdul Rahim; Sukhairi Sudin; Hiromitsu Nishizaki. RSSI-Based for Device-Free Localization Using Deep Learning Technique. Smart Cities 2020, 3, 444 -455.
AMA StyleAbdul Syafiq Abdull Sukor, Latifah Munirah Kamarudin, Ammar Zakaria, Norasmadi Abdul Rahim, Sukhairi Sudin, Hiromitsu Nishizaki. RSSI-Based for Device-Free Localization Using Deep Learning Technique. Smart Cities. 2020; 3 (2):444-455.
Chicago/Turabian StyleAbdul Syafiq Abdull Sukor; Latifah Munirah Kamarudin; Ammar Zakaria; Norasmadi Abdul Rahim; Sukhairi Sudin; Hiromitsu Nishizaki. 2020. "RSSI-Based for Device-Free Localization Using Deep Learning Technique." Smart Cities 3, no. 2: 444-455.
Abdul Syafiq Abdull Sukor; Ammar Zakaria; Norasmadi Abdul Rahim; Latifah Munirah Kamarudin; Rossi Setchi; Hiromitsu Nishizaki. A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes. Journal of Intelligent & Fuzzy Systems 2019, 36, 4177 -4188.
AMA StyleAbdul Syafiq Abdull Sukor, Ammar Zakaria, Norasmadi Abdul Rahim, Latifah Munirah Kamarudin, Rossi Setchi, Hiromitsu Nishizaki. A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes. Journal of Intelligent & Fuzzy Systems. 2019; 36 (5):4177-4188.
Chicago/Turabian StyleAbdul Syafiq Abdull Sukor; Ammar Zakaria; Norasmadi Abdul Rahim; Latifah Munirah Kamarudin; Rossi Setchi; Hiromitsu Nishizaki. 2019. "A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes." Journal of Intelligent & Fuzzy Systems 36, no. 5: 4177-4188.
A. S. Abdull Sukor; A. Zakaria; N. Abdul Rahim; L. M. Kamarudin; H. Nishizaki. Abnormality Detection Approach using Deep Learning Models in Smart Home Environments. Proceedings of the 7th International Conference on Communications and Broadband Networking - ICCBN 2019 2019, 22 -27.
AMA StyleA. S. Abdull Sukor, A. Zakaria, N. Abdul Rahim, L. M. Kamarudin, H. Nishizaki. Abnormality Detection Approach using Deep Learning Models in Smart Home Environments. Proceedings of the 7th International Conference on Communications and Broadband Networking - ICCBN 2019. 2019; ():22-27.
Chicago/Turabian StyleA. S. Abdull Sukor; A. Zakaria; N. Abdul Rahim; L. M. Kamarudin; H. Nishizaki. 2019. "Abnormality Detection Approach using Deep Learning Models in Smart Home Environments." Proceedings of the 7th International Conference on Communications and Broadband Networking - ICCBN 2019 , no. : 22-27.
Activity recognition plays a major role in smart home technologies in providing services to users. One of the approaches to identify activity is through the use of knowledge-driven reasoning. This paper presents a framework of semantic activity recognition, which is used to support smart home systems to identify users’ activities based on the existing context. The framework consists of two main components: a semantic knowledge base and an activity recognition module. The knowledge base is represented using ontology and it is used to provide a semantic understanding of the environment in order to classify users’ patterns of activities. Experimental results show that the proposed approach can support the classification process and accurately infer users’ activities with the accuracy of 90.9%.
Abdul Syafiq Abdull Sukor; Ammar Zakaria; Norasmadi Abdul Rahim; Rossi Setchi. Semantic Knowledge Base in Support of Activity Recognition in Smart Home Environments. International Journal of Engineering & Technology 2018, 7, 67 .
AMA StyleAbdul Syafiq Abdull Sukor, Ammar Zakaria, Norasmadi Abdul Rahim, Rossi Setchi. Semantic Knowledge Base in Support of Activity Recognition in Smart Home Environments. International Journal of Engineering & Technology. 2018; 7 (4.27):67.
Chicago/Turabian StyleAbdul Syafiq Abdull Sukor; Ammar Zakaria; Norasmadi Abdul Rahim; Rossi Setchi. 2018. "Semantic Knowledge Base in Support of Activity Recognition in Smart Home Environments." International Journal of Engineering & Technology 7, no. 4.27: 67.
Activity recognition is considered as an important task in many applications, particularly in healthcare services. Among these applications include medical diagnostic, monitoring of users' daily routine and detection of abnormal cases. This paper presents an approach for the activity recognition using an accelerometer sensor embedded in a smartphone. This approach uses a publicly available accelerometer dataset as the raw input signal. The features of the signal are selected based on the time and frequency domain. Then, Principal Component Analysis (PCA) is used to reduce the dimensionality of the features and extract the most significant ones that can classify human activities. A comparison process is performed between the original raw data and PCA-based features and additionally, time and frequency-domain features are also compared using several machine learning classifiers. The obtained results show that the PCA-based features obtain higher recognition rate while frequency-domain features have higher accuracy, with the rate of 96.11% and 92.10% respectively.
Abdul Syafiq Abdull Sukor; Ammar Zakaria; N. Abdul Rahim. Activity recognition using accelerometer sensor and machine learning classifiers. 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA) 2018, 233 -238.
AMA StyleAbdul Syafiq Abdull Sukor, Ammar Zakaria, N. Abdul Rahim. Activity recognition using accelerometer sensor and machine learning classifiers. 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA). 2018; ():233-238.
Chicago/Turabian StyleAbdul Syafiq Abdull Sukor; Ammar Zakaria; N. Abdul Rahim. 2018. "Activity recognition using accelerometer sensor and machine learning classifiers." 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA) , no. : 233-238.
Dirman Hanafi; Abdul Syafiq Abdull Sukor. Speaker Identification Using K-means Method Based on Mel Frequency Cepstral Coefficients(MFCC). i-manager’s Journal on Embedded Systems 2012, 1, 19 -28.
AMA StyleDirman Hanafi, Abdul Syafiq Abdull Sukor. Speaker Identification Using K-means Method Based on Mel Frequency Cepstral Coefficients(MFCC). i-manager’s Journal on Embedded Systems. 2012; 1 (1):19-28.
Chicago/Turabian StyleDirman Hanafi; Abdul Syafiq Abdull Sukor. 2012. "Speaker Identification Using K-means Method Based on Mel Frequency Cepstral Coefficients(MFCC)." i-manager’s Journal on Embedded Systems 1, no. 1: 19-28.