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Kuopio-University-Hospital-Microsurgery-Department-Visualize-Interactive-Software
The Visualize Interactive is a desktop software developed to visualize physiological signals during the activities called mesh alignment, knotting, and go-around which are done by the participants of the research team who participated in the synchronization and analysis of the biomarkers under noise and stress. You can download and install the application by using the following link, https://drive.google.com/drive/folders/1ZKrVuZ17Yat7EvErFob9E1PCWSaoPHpmTransfer_Learning_Covid-19_Pneumonia_Healthy
This deep learning model(CNN) uses Transfer learning by Feature Extraction and Fine Tuning in order to make multiclass-classification between COVID-19, Pneumonia and Healthy images.COVID-19_In_Italy_Exploratory_Data_Analysis
This repository is created in order to analyze why COVID-19 spreading faster in North Italy when compared to other regions of Italy.Machine-Learning-From-Scratch-PDFs
Start from crash course of Python, continue with the Numpy, Matplotlib and Machine Learning with exercises. This pdfs are referenced and cited from the University of Helsinki, Finland Machine Learning Summer Course.COVID-19_Detection_Desktop_Application_Software
It is a Python desktop application software which takes a chest x-ray image and then produces probabilities belong to each class with a Grad-CAM.Computer-Graphics-with-WebGL---Sample-Projects
2D and 3D Transformations, Lighting, Texture Properties, and more...Data-Science-Internship-Works-at-TURKSAT
Aim of this internship is first to learn about data science, big data visualization, machine learning and deep learning, implement the following tools: anaconda, jupyter notebook, spider and the libraries following: numpy,pandas,matplotlib,seaborn,plotly,scikit-learn by describing the platforms used by data scientists like kaggle and finally integrating the machine learning into e-government platform.Synchronization-and-Analysis-of-the-Biomarkers-Under-Noise-and-Stress-Project
So, in this paper, we will propose a data summary tables, visualizations which make some comparisons by using different properties of participants, and a deep learning model which first train the model by using the experiment outcomes analysis dataset and then test the accuracy, so that we can understand how much use the collected data is. Visualizations will be provided by using both R and Python and their rich visualization tools libraries. These visualizations consist of comparisons between the participants who are working in a silent environment and noise environment. You may not see clearly some of the visualizations on Github.You can download and run yourself with the provided *.csv file.Love Open Source and this site? Check out how you can help us