Please use this identifier to cite or link to this item:
https://ruomo.lib.uom.gr/handle/7000/1529
Title: | A Machine Learning-Based Model for Epidemic Forecasting and Faster Drug Discovery |
Authors: | Stergiou, Konstantinos D. Minopoulos, Georgios Memos, Vasileios A. Stergiou, Christos Koidou, Maria P. Psannis, Kostas E. |
Type: | Article |
Subjects: | FRASCATI::Engineering and technology |
Keywords: | artificial intelligence deep learning drug discovery epidemic diseases forecasting machine learning neural networks |
Issue Date: | 2022 |
Publisher: | MDPI |
Source: | Applied Sciences |
Volume: | 12 |
Issue: | 21 |
First Page: | 10766 |
Volume Title: | Application of Data Analytics in Smart Healthcare |
Abstract: | Today, healthcare system models should have high accuracy and sensitivity so that patients do not have a misdiagnosis. For this reason, sufficient knowledge of the area is required, with the medical staff being able to validate the correctness of their decisions. Therefore, artificial intelligence (AI) in combination with other emerging technologies could provide many benefits in the medical sector. In this paper, we demonstrate the combination of Internet of Things (IoT) and cloud computing (CC) with AI-related techniques such as artificial intelligence (AI), machine learning (ML), deep learning (DL), and neural networks (NN) in order to provide a useful approach for scientists and doctors. Our proposed model makes use of these immersive technologies so as to provide epidemic forecasting and help accelerate drug and antibiotic discovery. |
URI: | https://doi.org/10.3390/app122110766 https://ruomo.lib.uom.gr/handle/7000/1529 |
ISSN: | 2076-3417 |
Other Identifiers: | 10.3390/app122110766 |
Appears in Collections: | Department of Applied Informatics |
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applsci-12-10766.pdf | 909,3 kB | Adobe PDF | View/Open |
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