Multimodal Emotion Recognition: Emotion Classification Through the Integration of EEG and Facial Expressions by Songul Erdem Guler, Fatma Patlar Akbulut

“…The results validate the effectiveness of this approach, demonstrating the high accuracy of the Gated Recurrent Unit (GRU) model, which achieved an average of 91.8% classification accuracy on unimodal (EEG-only) data and an average of 97.8% classification accuracy on multimodal (EEG and facial expressions) datasets in the multi-class emotion categories. …”
Get full text

Experimental assessment of аdversarial attacks to the deep neural networks in medical image recognition by D. M. Voynov, V. A. Kovalev

“…The white-box Projected Gradient Descent attacks were examined based on 8 classification tasks and 13 image datasets containing more than 900 000 chest X-ray and histology images of malignant tumors. …”
Get full text

Classifications for Proliferative Vitreoretinopathy (PVR): An Analysis of Their Use in Publications over the Last 15 Years by Salvatore Di Lauro, Mustafa R. Kadhim, David G. Charteris, J. Carlos Pastor

“…Furthermore, 3 authors (2.9%) used modified-customized classifications and 4 (3.8%) classification errors were identified. When the updated Retina Society Classification was used, only 10.4% of authors used a full C grade description. …”
Get full text

The application of machine learning approaches to classify and predict fertility rate in Ethiopia  by Ewunate Assaye Kassaw, Biruk Beletew Abate, Bekele Mulat Enyew, Ashenafi Kibret Sendekie

Get full text