An Alzheimer's disease category progression sub-grouping analysis using manifold learning on ADNI.

Many current statistical and machine learning methods have been used to explore Alzheimer's disease (AD) and its associated patterns that contribute to the disease. However, there has been limited success in understanding the relationship between cognitive tests, biomarker data, and patient AD category progressions. In this work, we perform exploratory data analysis of AD health record data by analyzing various learned lower dimensional manifolds to separate early-stage AD categories further.

Automated recognition of the cricket batting backlift technique in video footage using deep learning architectures.

There have been limited studies demonstrating the validation of batting techniques in cricket using machine learning. This study demonstrates how the batting backlift technique in cricket can be automatically recognised in video footage and compares the performance of popular deep learning architectures, namely, AlexNet, Inception V3, Inception Resnet V2, and Xception. A dataset is created containing the lateral and straight backlift classes and assessed according to standard machine learning metrics.