International Journal of Advanced Research in Engineering and Technology (IJARET), 2024
Human Activity Recognition (HAR) is essential in recognizing and classifying
human actions perfor... more Human Activity Recognition (HAR) is essential in recognizing and classifying human actions performed at home through internet of things (IoT) devices and artificial intelligence (AI) technologies. The IoT smart devices such as sensors together with AI techniques like deep learning models are used to identify activities performed by individuals, such activities include; sleeping, watching TV, walking and more. The identification of human behaviour changes helps for healthcare, security control and more. The goal of this research is to create models that can more accurately forecast the activities that occupants of smart homes will engage in deep learning models like Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). The experimental results demonstrated that ANN outperformed with an excellent accuracy of 99.4% and 99.8% in households A and B respective, compared to CNN and RNN in identifying human behavior in smart residence using the ARAS dataset.
Journal of Information Systems and Telecommunication (JIST), 2022
Activity Recognition is essential for exploring human activities in smart homes in the presence o... more Activity Recognition is essential for exploring human activities in smart homes in the presence of multiple sensors as residents interact with household appliances. Smart homes use intelligent IoT devices linked to residents' homes to track human behavior as humans interact with the home's equipment, which may improve healthcare and security issues for the residents. Although remarkable studies have been done for pattern recognition and prediction of human activities in smart homes based on single residents and multiple residents using wearable sensors. However, not much research has been done on using Activity Recognizing Ambient Sensing (ARAS) residents. In this paper, we suggested using the ARAS dataset and newly emerged algorithms such as Deep learning Models to predict the activities of daily living (ADL). We compared the performance of deep learning models (ANN, CNN, and RNN) with that of classification models (DT, LDA, Adaboost, GB, XGBoost, MPL, and KNN) to figure out the ADL in the smart home residents. The experimental results demonstrated that DL models outperformed with an excellent accuracy compared to conventional classifiers in houses A and B in recognizing ADL in smart homes. This work proves that Deep Learning Models perform best in analyzing ARAS datasets compared to traditional machine learning algorithms.
Internet of Things (IoT) and Big Data have played a significant role in the development and susta... more Internet of Things (IoT) and Big Data have played a significant role in the development and sustainability of Smart Cities. They are the backbones of Smart Cities’ efforts to connect human resources, social capital, and ICT infrastructure to address public challenges, achieve sustainable development and improve people’s standard of life. It helps Smart Cities improve public services, such as water management, healthcare, education, security issues, transportation, energy usage, and other community to achieve a better life. Big Data helps to analyze, store and back-ups a massive amount of data generated in smart cities in seconds day by day. For this matter, different analytical tools are employed, such as data analytics, machine learning, statistics, and data mining. Even though there are many benefits offered to smart cities; the Smart Cities encounters challenges that need to be addressed. Many of these challenges focus on security and privacy, infrastructures and operational and handling massive data. This paper describes the overview of IoT, Big Data, and Smart Cities. The Applications of IoT and Smart Cities were also discussed; the features of Smart Cities and Big Data; Different challenges of IoT, Big Data, and Smart Cities are described. Also, the proposed research work has presented the relationship between IoT and Big Data to Smart Cities; the role of IoT and Big Data in smart cities was identified; finally, the future directions for IoT and Big Data regarding Smart Cities are discussed.
Internet of Things (IoT) and Big Data have played a significant role in the development and susta... more Internet of Things (IoT) and Big Data have played a significant role in the development and sustainability of Smart Cities. They are the backbones of Smart Cities’ efforts to connect human resources, social capital, and ICT infrastructure to address public challenges, achieve sustainable development and improve people’s standard of life. It helps Smart Cities improve public services, such as water management, healthcare, education, security issues, transportation, energy usage, and other community to achieve a better life. Big Data helps to analyze, store and back-ups a massive amount of data generated in smart cities in seconds day by day. For this matter, different analytical tools are employed, such as data analytics, machine learning, statistics, and data mining. Even though there are many benefits offered to smart cities; the Smart Cities encounters challenges that need to be addressed. Many of these challenges focus on security and privacy, infrastructures and operational and ...
International Journal of Advanced Research in Engineering and Technology (IJARET), 2024
Human Activity Recognition (HAR) is essential in recognizing and classifying
human actions perfor... more Human Activity Recognition (HAR) is essential in recognizing and classifying human actions performed at home through internet of things (IoT) devices and artificial intelligence (AI) technologies. The IoT smart devices such as sensors together with AI techniques like deep learning models are used to identify activities performed by individuals, such activities include; sleeping, watching TV, walking and more. The identification of human behaviour changes helps for healthcare, security control and more. The goal of this research is to create models that can more accurately forecast the activities that occupants of smart homes will engage in deep learning models like Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). The experimental results demonstrated that ANN outperformed with an excellent accuracy of 99.4% and 99.8% in households A and B respective, compared to CNN and RNN in identifying human behavior in smart residence using the ARAS dataset.
Journal of Information Systems and Telecommunication (JIST), 2022
Activity Recognition is essential for exploring human activities in smart homes in the presence o... more Activity Recognition is essential for exploring human activities in smart homes in the presence of multiple sensors as residents interact with household appliances. Smart homes use intelligent IoT devices linked to residents' homes to track human behavior as humans interact with the home's equipment, which may improve healthcare and security issues for the residents. Although remarkable studies have been done for pattern recognition and prediction of human activities in smart homes based on single residents and multiple residents using wearable sensors. However, not much research has been done on using Activity Recognizing Ambient Sensing (ARAS) residents. In this paper, we suggested using the ARAS dataset and newly emerged algorithms such as Deep learning Models to predict the activities of daily living (ADL). We compared the performance of deep learning models (ANN, CNN, and RNN) with that of classification models (DT, LDA, Adaboost, GB, XGBoost, MPL, and KNN) to figure out the ADL in the smart home residents. The experimental results demonstrated that DL models outperformed with an excellent accuracy compared to conventional classifiers in houses A and B in recognizing ADL in smart homes. This work proves that Deep Learning Models perform best in analyzing ARAS datasets compared to traditional machine learning algorithms.
Internet of Things (IoT) and Big Data have played a significant role in the development and susta... more Internet of Things (IoT) and Big Data have played a significant role in the development and sustainability of Smart Cities. They are the backbones of Smart Cities’ efforts to connect human resources, social capital, and ICT infrastructure to address public challenges, achieve sustainable development and improve people’s standard of life. It helps Smart Cities improve public services, such as water management, healthcare, education, security issues, transportation, energy usage, and other community to achieve a better life. Big Data helps to analyze, store and back-ups a massive amount of data generated in smart cities in seconds day by day. For this matter, different analytical tools are employed, such as data analytics, machine learning, statistics, and data mining. Even though there are many benefits offered to smart cities; the Smart Cities encounters challenges that need to be addressed. Many of these challenges focus on security and privacy, infrastructures and operational and handling massive data. This paper describes the overview of IoT, Big Data, and Smart Cities. The Applications of IoT and Smart Cities were also discussed; the features of Smart Cities and Big Data; Different challenges of IoT, Big Data, and Smart Cities are described. Also, the proposed research work has presented the relationship between IoT and Big Data to Smart Cities; the role of IoT and Big Data in smart cities was identified; finally, the future directions for IoT and Big Data regarding Smart Cities are discussed.
Internet of Things (IoT) and Big Data have played a significant role in the development and susta... more Internet of Things (IoT) and Big Data have played a significant role in the development and sustainability of Smart Cities. They are the backbones of Smart Cities’ efforts to connect human resources, social capital, and ICT infrastructure to address public challenges, achieve sustainable development and improve people’s standard of life. It helps Smart Cities improve public services, such as water management, healthcare, education, security issues, transportation, energy usage, and other community to achieve a better life. Big Data helps to analyze, store and back-ups a massive amount of data generated in smart cities in seconds day by day. For this matter, different analytical tools are employed, such as data analytics, machine learning, statistics, and data mining. Even though there are many benefits offered to smart cities; the Smart Cities encounters challenges that need to be addressed. Many of these challenges focus on security and privacy, infrastructures and operational and ...
Uploads
Papers by john kasubi
human actions performed at home through internet of things (IoT) devices and artificial
intelligence (AI) technologies. The IoT smart devices such as sensors together with AI
techniques like deep learning models are used to identify activities performed by
individuals, such activities include; sleeping, watching TV, walking and more. The
identification of human behaviour changes helps for healthcare, security control and
more. The goal of this research is to create models that can more accurately forecast
the activities that occupants of smart homes will engage in deep learning models like
Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Recurrent
Neural Network (RNN). The experimental results demonstrated that ANN outperformed
with an excellent accuracy of 99.4% and 99.8% in households A and B respective,
compared to CNN and RNN in identifying human behavior in smart residence using the
ARAS dataset.
residents interact with household appliances. Smart homes use intelligent IoT devices linked to residents' homes to track
human behavior as humans interact with the home's equipment, which may improve healthcare and security issues for the
residents. Although remarkable studies have been done for pattern recognition and prediction of human activities in smart
homes based on single residents and multiple residents using wearable sensors. However, not much research has been done
on using Activity Recognizing Ambient Sensing (ARAS) residents. In this paper, we suggested using the ARAS dataset and
newly emerged algorithms such as Deep learning Models to predict the activities of daily living (ADL). We compared the
performance of deep learning models (ANN, CNN, and RNN) with that of classification models (DT, LDA, Adaboost, GB,
XGBoost, MPL, and KNN) to figure out the ADL in the smart home residents. The experimental results demonstrated that
DL models outperformed with an excellent accuracy compared to conventional classifiers in houses A and B in recognizing
ADL in smart homes. This work proves that Deep Learning Models perform best in analyzing ARAS datasets compared to
traditional machine learning algorithms.
human actions performed at home through internet of things (IoT) devices and artificial
intelligence (AI) technologies. The IoT smart devices such as sensors together with AI
techniques like deep learning models are used to identify activities performed by
individuals, such activities include; sleeping, watching TV, walking and more. The
identification of human behaviour changes helps for healthcare, security control and
more. The goal of this research is to create models that can more accurately forecast
the activities that occupants of smart homes will engage in deep learning models like
Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Recurrent
Neural Network (RNN). The experimental results demonstrated that ANN outperformed
with an excellent accuracy of 99.4% and 99.8% in households A and B respective,
compared to CNN and RNN in identifying human behavior in smart residence using the
ARAS dataset.
residents interact with household appliances. Smart homes use intelligent IoT devices linked to residents' homes to track
human behavior as humans interact with the home's equipment, which may improve healthcare and security issues for the
residents. Although remarkable studies have been done for pattern recognition and prediction of human activities in smart
homes based on single residents and multiple residents using wearable sensors. However, not much research has been done
on using Activity Recognizing Ambient Sensing (ARAS) residents. In this paper, we suggested using the ARAS dataset and
newly emerged algorithms such as Deep learning Models to predict the activities of daily living (ADL). We compared the
performance of deep learning models (ANN, CNN, and RNN) with that of classification models (DT, LDA, Adaboost, GB,
XGBoost, MPL, and KNN) to figure out the ADL in the smart home residents. The experimental results demonstrated that
DL models outperformed with an excellent accuracy compared to conventional classifiers in houses A and B in recognizing
ADL in smart homes. This work proves that Deep Learning Models perform best in analyzing ARAS datasets compared to
traditional machine learning algorithms.