CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images حسب Subrato Bharati (10664279)

"…A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. …"

Summary of steps in iterative point set registration for scRNA-seq data. حسب Amir Alavi (9569337)

Data_Sheet_1_Dried shiitake mushroom grade recognition using D-VGG network and machine vision.docx حسب Li Wang (15202)

"…The method uses the VGG16 network as the base framework, optimizes the convolutional layer of the network, and uses a global average pooling layer instead of a fully connected layer to reduce the risk of model overfitting. …"

Datasheet1_A Workflow for Rapid Unbiased Quantification of Fibrillar Feature Alignment in Biological Images.zip حسب Stefania Marcotti (5896853)

"…Using pre-existing datasets of cell and ECM images, we demonstrate our approach and compare and contrast this workflow with two other well-known ImageJ algorithms to quantify image feature alignment. These comparisons reveal that AFT has a number of advantages due to its grid-based FFT approach. 1) Flexibility in defining the window and neighborhood sizes allows for performing a parameter search to determine an optimal length scale to carry out alignment metrics. …"