Manuscript Topics

Interconnected technologies provide large volumes of data that are often valuable in different contexts. In today's world of digital transformation, various types of device and networks reinforce the use of big data science and artificial intelligence. The pandemic of the coronavirus disease SARS-CoV-2 (COVID-19) is providing an avenue for various investigations to transit with the support of this type of data that are generated in the current interconnected world. These data are primarily unstructured and well defined within the context of big data, data science, machine learning/deep, and artificial intelligence. Data from medical images, traceability of infected patients and outbreak areas, mobility in public transport, environmental monitoring, etc., usually geo-referenced, are of great interest for these investigations. Such massive data are generated from diverse sources, ranging from internet of the things (IoT) to social media. For these types of data, traditional techniques for structured data analytics are unsuitable and insufficient to generate information and discover relevant knowledge in times of the COVID-19 pandemic. Thus, artificial intelligence methods to process medical images, sentiment analysis, and related others, to achieve social distancing are fields of great relevance. Despite the focus of this Special Issue is the machine learning/deep and statistical modeling for facing the COVID-19 pandemic, we welcome contributions in artificial intelligence, classification, and unsupervised learning, as well as in the topics detailed below related to other real-world phenomena in biosciences and engineering. We strongly encourage interdisciplinary works in these areas.

This Special Issue looks for submissions, but not limited to, in applied data science with potential applications in COVID-19 and emphasis in the following areas (in alphabetical order):
Artificial intelligence；
Bayesian methods；
Big data, dimensionality high, and large-scale data analysis；
Deep learning；
Machine learning；
Statistical learning；
Evolutionary-based, game-based, physics-based, and swarm-based algorithms, among others；
Multivariate analysis as clustering, PCA, and PLS, among others；
Statistical modeling and its diagnostics

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