TY - GEN
T1 - An optimized deep convolutional neural network architecture for concept drifted image classification
AU - Jameel, Syed Muslim
AU - Hashmani, Manzoor Ahmed
AU - Alhussain, Hitham
AU - Rehman, Mobashar
AU - Budiman, Arif
N1 - Funding Information:
This research study is conducted in Universiti Teknologi PETRONAS (UTP), Malaysia as a part of research project ?Correlation between Concept Drift Parameters and Performance of Deep Learning Models: Towards Fully Adaptive Deep Learning Models? under Fundamental Research Grant Scheme (FRGS) Ministry of Higher Education (MOHE) Malaysia.
Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Machine Learning (ML) is a branch of Artificial Intelligence, which is continuously evolving to overcome current technological challenges faced by industries. These technological changes are due to modernization in industries for Business Intelligence (BI) i.e., 4th Industrial Revolution. Among the other ML approaches, Image Classification plays a significant role for Business Intelligence and upfront several new challenges in online and non-stationary environment, such as Concept Drift. To overcome the CD issue, one of the fundamental requirements is optimization of classifier. Whereas, Convolutional Neural Network (CNN) is considered best classifier/model for Image Classification. Therefore, the aim of this study is to investigate the optimize architecture for CNN in Concept Drifted environment. This study examines the variety of CNN architectures (CNN1 to CNN4) through different configuration of CNN layers and tuning parameters under certain Concept Drift scenarios. Furthermore, a comparative analysis is performed among these CNN models by monitoring their classification accuracy, loss and computational complexity to validate the optimized CNN model experimentally. In future, proposed Optimize Deep Neural Network architecture will be further investigated for high dimensional Imagery data-streams, for example color and multispectral imagery.
AB - Machine Learning (ML) is a branch of Artificial Intelligence, which is continuously evolving to overcome current technological challenges faced by industries. These technological changes are due to modernization in industries for Business Intelligence (BI) i.e., 4th Industrial Revolution. Among the other ML approaches, Image Classification plays a significant role for Business Intelligence and upfront several new challenges in online and non-stationary environment, such as Concept Drift. To overcome the CD issue, one of the fundamental requirements is optimization of classifier. Whereas, Convolutional Neural Network (CNN) is considered best classifier/model for Image Classification. Therefore, the aim of this study is to investigate the optimize architecture for CNN in Concept Drifted environment. This study examines the variety of CNN architectures (CNN1 to CNN4) through different configuration of CNN layers and tuning parameters under certain Concept Drift scenarios. Furthermore, a comparative analysis is performed among these CNN models by monitoring their classification accuracy, loss and computational complexity to validate the optimized CNN model experimentally. In future, proposed Optimize Deep Neural Network architecture will be further investigated for high dimensional Imagery data-streams, for example color and multispectral imagery.
KW - Concept drift
KW - Convolutional neural network
KW - Image classification
UR - http://www.scopus.com/inward/record.url?scp=85072824401&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-29516-5_70
DO - 10.1007/978-3-030-29516-5_70
M3 - Conference contribution
AN - SCOPUS:85072824401
SN - 9783030295158
T3 - Advances in Intelligent Systems and Computing
SP - 932
EP - 942
BT - Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 1
A2 - Bi, Yaxin
A2 - Bhatia, Rahul
A2 - Kapoor, Supriya
PB - Springer Verlag
T2 - Intelligent Systems Conference, IntelliSys 2019
Y2 - 5 September 2019 through 6 September 2019
ER -