CalgaryR: The Fall season unrolls

Satellites, Machine Learning and Urban Ecology: the experience of Kryvyi Rih City, Yevhen Vasylenko.
This presentation showcases a classification of land cover types in Kryvyi Rih, using Random Forests. This work led to public debate and policy revisions for greening industrial zones. The methodologies developed continue to inform decisions on optimizing green areas around industrial sites.

ECG Abnormalities Detection using CNN and LSTM, Sungki Park
This presentation covers a mixed deep learning technique, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for detecting ECG abnormalities, such as arrhythmia and myocardial infarction. CNNs are used to extract spatial features from the 1D ECG time series, identifying patterns like R-peaks and QRS complexes. The model efficiently learns local features from heartbeats. LSTM layers capture temporal dependencies, modelling the sequential nature of the ECG signals to detect irregular heartbeat rhythms. The model is trained on preprocessed ECG data, which is normalized and segmented into smaller windows. CNN extracts features, and the LSTM captures temporal patterns across time steps. The final dense layer classifies the signals as either normal or a type of arrhythmia. This approach may reduce manual analysis time and enable potential real-time heart monitoring in wearable devices

Teaching R using R in the cloud, Pablo Adames
This is how an R-workshop was built from the ground up with R packages, R tooling and Shinyapps cloud infrastructure.