Name: CalgaryR: The Fall season unrolls
Start: 2024-10-22T18:00:00-06:00
End: 2024-10-22T20:00:00-06:00
Location: (Room 440A and D) Taylor Family Digital Library, University of Calgary

**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.

John Brosz

Pablo Adames

Chel Hee Lee

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R User Groups

Thank you for being a generous sponsor of all our events.

Nima Safaian

Thank you for being a generous sponsor of all our events. 

Pacific Inst. of Mathematical Sciences

Cenovus Energy

Dept. Math & Stats, Univ. of Calgary

R Consortium

Technology

Statistical Computing

Machine Learning

Predictive Analytics

Data Mining

R Project for Statistical Computing

Data Science

Applied Statistics

Statistical Modeling

Business Analytics

Bayesian Statistics

Data Analytics

Big Data

Open Source

Statistical Science

1. **Yevhen Vasylenko, APEGA G.I.T.**
    An experienced geologist with extensive expertise in exploration and mining geology, resource estimation, 3D modelling, data audits, and geological data analysis. His published work involves environmental data analysis, geospatial data analysis and visualization. He has developed machine learning models based on remote sensing data using R.
2. **Sungki Park,** **Senior Software Engineer, Clarity.ca**
3. 20 years of experience in the IT industry, currently Sungki is a senior full-stack software developer at Clarity. Previously, he worked as a software engineer in the aviation industry, developing an engine to provide flight-tracking services using satellite data. His expertise is cloud architecture and scalable solutions using various programming languages such as C, C++, JAVA, C#, and Python. Sungki is also currently enrolled in a Master of Data Science and Analytics program at the University of Calgary
4. **Pablo Adames, P. Eng.**
    Software Engineer focusing on Software Development for Simulation, AI Vision and Machine Learning. Pablo works in Linux, bash, C++, Python and Java, however, Rust and R are nearest and dearest to his heart.

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CalgaryR: The Fall season unrolls

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