Accelerating drug repurposing for COVID-19 treatment by modeling mechanisms of action using cell image features and machine learning
Authors (first, second and last of 7)
Collection
Cognitive Vision & Intelligent Computation Approaches for COVID-19
Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. It has been recognized as a pandemic by the World Health Organization. As of 4th June 2020, there have been over 6.5 million confirmed cases worldwide, with over 3,88K deaths. It is affecting 213 countries and territories around the world. The symptoms of the disease vary from mild to moderate respiratory illness. Older people and those with underlying medical conditions like diabetes, cardiovascular disease, respiratory problems, and cancer are more likely to develop serious illness. So far COVID-19 has claimed many lives across the globe.
There are several research efforts are being made in the area of intelligent computation for the prediction and detection of the growth and trends of COVID-19 along with the need of enhancements.
In recent years, there are several approaches already been proposed to mimic the human intelligence capabilities with the hybridization of prior knowledge and visual information for the cognitive vision. It has the capability of acquiring contextual knowledge from visual experiences also. So, knowledge-based vision systems may integrate the data-driven approaches that perform computer vision tasks through intelligent computation (Artificial Intelligence and Machine Learning Algorithms) offered by prior knowledge into an inference process. So, cognitive vision and intelligent computation may be helpful in the analysis of COVID-19 growth with community behavior. Wrapping this data with artificial intelligence and machine learning algorithms may be helpful in forecasting different aspects related to COVID-19 and its symptom correlation. It is also helpful in epidemiological correlations between different aspects of disease spreading based on the previous history and cognitive mapping of the people during this pandemic.
Specifically, innovative contributions that either solve or advance the understanding of issues related to new technologies and applications in the real world in the direction of cognitive vision and intelligent computation for the prediction and detection of the growth and trends of COVID-19 are very welcome.
Potential topics include, but are not limited to the following:
- Symptoms correlation and epidemiology
- Cognitive vision in growth and trend of COVID-19
- Cognitive vision approach in early detection of COVID-19
- Cognitive vision and differential diagnosis
- Role of cognitive vision and intelligent computing in COVID-19 detection and prediction
- Machine learning based approaches for monitoring and detection in case of COVID-19
- Computational correlation in pneumonia and COVID-19
- COVID-19 detection using deep learning models
- Artificial intelligence-based methods in COVID-19 related data collection and visualization
- Data mining and knowledge discovery in healthcare
- Decision support systems for healthcare and wellbeing
- Evolutionary algorithms for symptoms detection and impact evaluation
- Intelligent computing platforms
- Big data and cloud computing frameworks and architectures for applied computation
- Visualization and interactive interfaces in case of COVID-19
- Cognitive vision and intelligent hospital management
- Cognitive-intelligent human behavior mapping of COVID-19 patients
Editors
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Dr. Ashutosh Kumar Dubey
Senior Member (IEEE and ACM), Chitkara University, Punjab, India
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Dr. Vincenzo Piuri
IEEE Fellow, 2015 IEEE Vice President (Technical Activities), University of Milan, Italy
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Dr. Sreenatha Anavatti
University of New South Wales (UNSW at Canberra), Australia
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Dr. Umesh Chandra Pati
Senior Member IEEE, National Institute of Technology, Rourkela, Odisha, India
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Dr. Ahmed M. Elmisery
Faculty of Computing, Engineering and Science, University of South Wales, United Kingdom
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Dr. Sam Goundar
British University Vietnam/University of Staffordshire, Hung Yen, Vietnam
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Dr. Abhishek Kumar
Senior Member IEEE, Chitkara University, Punjab, India
Articles (2 in this collection)
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Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model
Authors (first, second and last of 5)
