Artificial Intelligence and Machine Learning Applications for Develo** the Diagnosis of COVID-19, Second Edition

Dear Colleagues,

This Special Issue is a continuation of our previous Special Issue “Artificial Intelligence and Machine Learning Applications for Develo** the Diagnosis of COVID-19”.

The design of computational medical diagnosis and prognosis models using state-of-the-art artificial intelligence and machine learning models is a challenging research field, especially in the context of COVID-19 as new variants emerge day by day. This Special Issue will focus on new approaches that cater to this field of research. The prognosis model should be updated with the most challenging datasets. Data pre-processing, data security, data unbalancing, and big data handing are of significant value in this regard. We expect a broad range of research ideas, including modern new approaches such as statistical machine learning, unsupervised model design, explainable artificial intelligence (XAI), representation learning, reinforcement learning, etc.

Dr. Somenath Chakraborty
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. COVID is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• artificial intelligence
• machine learning
• computational medical diagnosis
• prognosis model
• COVID-19