A fully adaptive image classification approach for industrial revolution 4.0
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...
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| Main Authors: | , , , |
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| Format: | Article |
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2019
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053916603&doi=10.1007%2f978-3-319-99007-1_30&partnerID=40&md5=8b44db975dad66368b158c091e792dfc http://eprints.utp.edu.my/22228/ |
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| Summary: | 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.). © Springer Nature Switzerland AG 2019. |
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