Tootfinder

Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network
Mahin Montasir Afif, Abdullah Al Noman, K. M. Tahsin Kabir, Md. Mortuza Ahmmed, Md. Mostafizur Rahman, Mufti Mahmud, Md. Ashraful Babu
https://arxiv.org/abs/2506.17165

Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network
Generative Adversarial Networks (GAN) have shown potential in expanding limited medical imaging datasets. This study explores how different ratios of GAN-generated and real brain tumor MRI images impact the performance of a CNN in classifying healthy vs. tumorous scans. A DCGAN was used to create synthetic images which were mixed with real ones at various ratios to train a custom CNN. The CNN was then evaluated on a separate real-world test set. Our results indicate that the model maintains hig…

A visco-plastic constitutive model for accurate densification and shape predictions in powder metallurgy hot isostatic pressing
Subrato Sarkar, Jason R Mayeur, KPK Ajjarapu, Fred A List III, Soumya Nag, Ryan R Dehoff
https://arxiv.org/abs/2506.11946

A visco-plastic constitutive model for accurate densification and shape predictions in powder metallurgy hot isostatic pressing
Powder metallurgy hot isostatic pressing (PM-HIP) is an advanced manufacturing process that produces near net shape parts with high material utilization and uniform microstructures. Despite being used frequently to produce small-scale components, the application of PM-HIP to large-scale components is limited due to inadequate understanding of its complex mechanisms that cause unpredictable post-HIP shape distortions. A computational model can provide necessary information about the intermediate…