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Stuttgart in-depth analysis. This work presents AQTA, an interactive system-user-system interface to involve and support users for environmental time series data, for dynamic future predictions and detailed patterns analysis. AQTA combines visual representations of multiple time series (i.e.i.e., historical, present) with future prediction information generated by deep machine learning models. AQAT helps analysts engage in a back-and-forth dialogue with the designed models such as Long Short-Term Memory(LSTM), Random Forest (RF), Multi-Convolutional Neural Network (MCNN), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). These models can be dynamically selected in real-time (on the fly) and the analysts can compare the results visually in different conditions.
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Usage: Select any district to view total, recovered and active cases.
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Short description of portfolio item number 1
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Short description of portfolio item number 2
Published in , 2020
Here you can find a list of conference proceedings.
Published in , 2021
This link contains the full list of publication in journals.
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
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#November 2018: (Our work on machine learning and DSN was featured by GCS).[https://www.hlrs.de/whats-new/news/archive/detail-view/2018-11-05/]