4 papers
IoT-Enhanced CNN-Based Labelled Crack Detection for Additive Manufacturing Image Annotation in Industry 4.0
Mohsen Asghari Ilani, Yaser Mike Banad
This paper presents an IoT-enhanced deep learning framework for automated crack detection in Additive Manufacturing (AM) surfaces using convolutional neural networks (CNNs). By int…
Brain Tumor Detection Through Diverse CNN Architectures in IoT Healthcare Industries: Fast R-CNN, U-Net, Transfer Learning-Based CNN, and Fully Connected CNN
Mohsen Asghari Ilani, Yaser M. Banad
Artificial intelligence (AI)-powered deep learning has advanced brain tumor diagnosis in Internet of Things (IoT)-healthcare systems, achieving high accuracy with large datasets. B…
LabelImg: CNN-Based Surface Defect Detection
Mohsen Asghari Ilani, Yaser Mike Banad
In the journey of computer vision system development, the acquisition and utilization of annotated images play a central role, providing information about object identity, spatial…
TransMatch: A Transfer-Learning Framework for Defect Detection in Laser Powder Bed Fusion Additive Manufacturing
Mohsen Asghari Ilani, Yaser Mike Banad
Surface defects in Laser Powder Bed Fusion (LPBF) pose significant risks to the structural integrity of additively manufactured components. This paper introduces TransMatch, a nove…