Predictive Modeling for Mold Material Defects Using CNN-Random Forest Fusion-Reference-Cited by-同舟云学术

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Predictive Modeling for Mold Material Defects Using CNN-Random Forest Fusion

Published:2024-06-28 Issue: Volume: Page:1-5
ISSN:
Container-title:2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS)
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Author:

Rajora Ricky¹,Banerjee Deepak²,Malhotra Sonal¹,Devlival Swati³,Kumar Gotte Ranjith⁴,Rajora Aarju⁵

Affiliation:

1. Graphic Era Hill University,Dehradun,Uttarakhand,India

2. Chitkara University Institute of Engineering and Technology, Chitkara University,Punjab,India

3. Graphic Era Deemed to be University,Dehradun,Uttarakhand,India

4. School of Computer Science & Artificial Intelligence, SR University,Warangal

5. Chandigarh Engineering College, Jhanjeri,Department of Computer Science & Engineering,Mohali

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10624684/10624740/10624825.pdf?arnumber=10624825

Reference16 articles.

1. Defect detection of injection molding products on small datasets using transfer learning

2. Körber, Patricia Lasserre;Abtin;Automated visual detection of geometrical defects in composite manufacturing processes using deep convolutional neural networks.,2022

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