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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
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.
ISSN: 2076-3417
Other Identifiers: 10.3390/app122110766
Appears in Collections:Department of Applied Informatics

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