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Recurent Neural Network with CuDNNLSTM Model\n - Convolutional Autoencoder\n - Residual Network (ResNet) and MobileNet Model\n\n#### Workflow that is used in this Project\n- Data Processing/Transformation\n- Data Normalization\n- Image Transformation: Markov Transition Field\n- State of Art Models (Machine and Deep Learning)\n- Visulization of Train and Test Models: Accuracy and Loss \n- Classification Report, Accuracy, and Loss\n\n#### APIs that are used in this Project\n- tensorflow\n- sklearn\n- keras\n- matplotlib\n- numpy\n- pandas\n- pyts (Python Time Series Classification)\n\n# Time Series Dataset: Accelerometer Sensors\n\u003cp align=\"center\" width=\"100%\"\u003e\n    \u003cimg width=\"100%\" hight=\"100%\" src=\"https://user-images.githubusercontent.com/74346775/201532793-b86a0048-8997-460d-aea4-d35472cf11fc.png\"\u003e\n    \u003cimg width=\"100%\" hight=\"100%\" src=\"https://user-images.githubusercontent.com/74346775/201532822-1233416c-13c3-4837-96be-59725ea53f45.png\"\u003e\n\u003c/p\u003e\n\n# Image Transformation: Markov Transition Field\n\u003cp align=\"center\" width=\"100%\"\u003e\n    \u003cimg width=\"100%\" hight=\"100%\" src=\"https://user-images.githubusercontent.com/74346775/201532891-67e8b290-fe3c-4b49-a984-f1ff47b23c03.png\"\u003e\n    \u003cimg width=\"100%\" hight=\"100%\" src=\"https://user-images.githubusercontent.com/74346775/201532998-0571aac0-b677-4ca7-ab62-4404b7c65bfc.png\"\u003e\n\u003c/p\u003e\n\n# Visulization of Train and Test Models: Accuracy and Loss\n\u003cp align=\"center\" width=\"100%\"\u003e\n    \u003cimg width=\"22%\" hight=\"100%\" src=\"https://user-images.githubusercontent.com/74346775/201533204-00c72446-13f0-40de-9421-5c3db0f13e17.png\"\u003e \n    \u003cimg width=\"22%\" hight=\"100%\" src=\"https://user-images.githubusercontent.com/74346775/201533342-bf4121e1-1012-43ec-9b97-af2d09b6d02d.png\"\u003e \n    \u003cimg width=\"22%\" hight=\"100%\" src=\"https://user-images.githubusercontent.com/74346775/201533253-54e2d0e6-0f2b-4091-919e-caa61a79b093.png\"\u003e\n    \u003cimg width=\"22%\" hight=\"100%\" src=\"https://user-images.githubusercontent.com/74346775/201533399-36d97c39-1fc0-45cb-812d-89854b98eb7f.png\"\u003e\n\u003c/p\u003e\n\n# Classification Report\n\u003cp align=\"center\" width=\"100%\"\u003e\n    \u003cimg width=\"100%\" hight=\"100%\" src=\"https://user-images.githubusercontent.com/74346775/201533686-5b04affe-f675-4df2-b8de-6afd7a6f5fe1.png\"\u003e\n    \u003cimg width=\"100%\" hight=\"100%\" src=\"https://user-images.githubusercontent.com/74346775/201533697-c25ba7b9-f1e4-4e5d-aff7-a7db89ebce85.png\"\u003e\n\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faaaastark%2Faccelerometer-sensors-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faaaastark%2Faccelerometer-sensors-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faaaastark%2Faccelerometer-sensors-analysis/lists"}