2nd ICAI 2022

International Conference on Automotive Industry 2022

Mladá Boleslav, Czech Republic

case, the logic of the program allows not only to analyze the routes but also to define the threats or incompatibilities related to safety. This, in turn, allows to reduce the operational risk of processes, by continuous monitoring of violations without the suspicion of continuous monitoring review. The algorithm registers only selected types of events and does not affect the sense of freedom of employees, after using appropriate communication. Transfer learning opens the door for users to share knowledge and achievements in general machine learning disciplines which is crucial for minimizing work and improving quality in today’s economy. References [1] Chollet, F. (2020). Transfer learning & fine-tuning [online]. [cit. 2020/05/12]. Available at: https://keras.io/guides/transfer_learning/ [2] Erboz, G. (2017) . How to Define Industry 4.0: The Main Pillars of Industry 4.0. In Managerial trends in the development of enterprises in globalization era. Nitra: Slovak University of Agriculture, pp. 761–767. [3] Karimpanal, T.G. and Bouffanais, R. (2018). Self-Organizing Maps for Storage and Transfer of Knowledge in Reinforcement LearningSelf–organizing maps for storage and transfer of knowledge in reinforcement learning. Adaptive Behavior , vol. 27, pp. 111–126. [4] Menon, A., Omman, B. and Asha, S. (2021). Pedestrian Counting Using Yolo V3 . In 2021 International Conference on Innovative Trends in Information Technology (ICITIIT) , 11–12 Feb. 2021, Kottayampp: pp. 1–9. [5] Radivojević, G. and Milosavljević, L. (2019). The Concept of Logistics 4.0 , In 4th Logistics International Conference , Belgrade: University of Belgrade, Faculty of Transport and Traffic Engineering, pp. 283–292. [6] Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: pp. 779–788. [7] Rosi, B. and Lerher, T. (2017). The Fourth Industrial Revolution as a Challenge for intralogistics . In International Scientific Conference on Innovation. Novi Sad: University Business Academy, Faculty of Economics and Engineering Management, pp. 47–63. [8] Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C. and Liu, C. (2018). A Survey on Deep Transfer Learning. In Artificial Neural Networks and Machine Learning – ICANN 2018 . Rhodes: LNCS vol. 11141, Springer, Cham, pp. 270–279.

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