TY - GEN
T1 - A fully adaptive image classification approach for industrial revolution 4.0
AU - Jameel, Syed Muslim
AU - Hashmani, Manzoor Ahmed
AU - Alhussain, Hitham
AU - Budiman, Arif
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Industrial Revolution (IR) improves the way we live, work and interact with each other by using state of the art technologies. IR-4.0 describes a future state of industry which is characterized through the digitization of economic and production flows. The nine pillars of IR-4.0 are dependent on Big Data Analytics, Artificial Intelligence, Cloud Computing Technologies and Internet of Things (IoT). Image datasets are most valuable among other types of Big Data. Image Classification Models (ICM) are considered as an appropriate solution for Business Intelligence. However, due to complex image characteristics, one of the most critical issues encountered by the ICM is the Concept Drift (CD). Due to CD, ICM are not able to adapt and result in performance degradation in terms of accuracy. Therefore, ICM need better adaptability to avoid performance degradation during CD. Adaptive Convolutional ELM (ACNNELM) is one of the best existing ICM for handling multiple types of CD. However, ACNNELM does not have sufficient adaptability. This paper proposes a more autonomous adaptability module, based on Meta-Cognitive principles, for ACNNELM to further improve its performance accuracy during CD. The Meta-Cognitive module will dynamically select different CD handling strategies, activation functions, number of neurons and restructure ACNNELM as per changes in the data. This research contribution will be helpful for improvement in various practical applications areas of Business Intelligence which are relevant to IR-4.0 and TN50 (e.g., Automation Industry, Autonomous Vehicle, Expert Agriculture Systems, Intelligent Education System, and Healthcare etc.).
AB - Industrial Revolution (IR) improves the way we live, work and interact with each other by using state of the art technologies. IR-4.0 describes a future state of industry which is characterized through the digitization of economic and production flows. The nine pillars of IR-4.0 are dependent on Big Data Analytics, Artificial Intelligence, Cloud Computing Technologies and Internet of Things (IoT). Image datasets are most valuable among other types of Big Data. Image Classification Models (ICM) are considered as an appropriate solution for Business Intelligence. However, due to complex image characteristics, one of the most critical issues encountered by the ICM is the Concept Drift (CD). Due to CD, ICM are not able to adapt and result in performance degradation in terms of accuracy. Therefore, ICM need better adaptability to avoid performance degradation during CD. Adaptive Convolutional ELM (ACNNELM) is one of the best existing ICM for handling multiple types of CD. However, ACNNELM does not have sufficient adaptability. This paper proposes a more autonomous adaptability module, based on Meta-Cognitive principles, for ACNNELM to further improve its performance accuracy during CD. The Meta-Cognitive module will dynamically select different CD handling strategies, activation functions, number of neurons and restructure ACNNELM as per changes in the data. This research contribution will be helpful for improvement in various practical applications areas of Business Intelligence which are relevant to IR-4.0 and TN50 (e.g., Automation Industry, Autonomous Vehicle, Expert Agriculture Systems, Intelligent Education System, and Healthcare etc.).
KW - Concept Drift
KW - Image Classification
KW - Industrial Revolution 4.0
KW - Self Adaptability
UR - http://www.scopus.com/inward/record.url?scp=85053916603&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-99007-1_30
DO - 10.1007/978-3-319-99007-1_30
M3 - Conference contribution
AN - SCOPUS:85053916603
SN - 9783319990064
T3 - Advances in Intelligent Systems and Computing
SP - 311
EP - 321
BT - Recent Trends in Data Science and Soft Computing - Proceedings of the 3rd International Conference of Reliable Information and Communication Technology IRICT 2018
A2 - Mohammed, Fathey
A2 - Saeed, Faisal
A2 - Gazem, Nadhmi
A2 - Busalim, Abdelsalam
PB - Springer Verlag
T2 - 3rd International Conference of Reliable Information and Communication Technology, IRICT 2018
Y2 - 23 June 2018 through 24 June 2018
ER -