NuComBHDA: The 1° International Workshop on Advanced Numerical Computations for Big Human Data Analysis

Abstract

Recently, healthcare has been considered as one of the most important research application topics. To this end, and with the emergence of new technological trends, a large number of promising clinical results have been achieved. The combination of recent Machine Learning methods with healthcare and human-life science applications have delivered appreciable results. Human-life data (i.e., genomics, RMI) stored into specific files require very large sizes. Despite the newest existing architectures, the management, analysis and computational processing of this data turns out to be very difficult. In order to gain an advantage of performance in terms of accuracy, efficiency and reliability, numerical analysis combined with artificial intelligence methods can achieve significant outcomes.

During the recent COVID-19 pandemic, scientists, clinicians, and healthcare experts around the world keep on searching for a new technology to tackle this pandemic. Applying Machine Learning (ML) and Artificial Intelligence (AI) methods can be very helpful in providing a new approach to fight against the novel Coronavirus outbreak. Several research papers propose new methods which are able to perform a prediction or treatment for the novel COVID-19 disease. ML, combined with efficient numerical schemas, give us new frontiers in the war against this pandemic, in comparison to the use of canonical approaches.

This workshop invites novel contributions that have a strong scientific impact in advancing the current standards, methods and tools in computational healthcare sciences and on advanced numerical computations for big human data analysis.

Goals

The goal of this workshop is to provide a forum for discussing and analyzing recent trends in data mining and analysis, mainly focused on computer science and numerical aspects. Moreover, the aforementioned fields are widely exploitable for several human life problems (e.g. genoma analysis, healthcare prediction, cancer risk management). The application of advanced numerical techniques with Machine learning methods performs very appreciable results in the health- care sciences. The aim is to bridge theoretical issues and numerical aspects to debate on mathematical and computational foundations of algorithmic approaches able to infer knowledge from data. Speeches and Communications on Clustering, Genomics and Proteomics analysis, Supervised and Unsupervised Classification schemes, Data Mining methods, Numerical schema, Efficient and Reliable algorithmic strategies for data analysis and mining are welcome.

Topics of interest include, but are not limited to

• Supervised and Unsupervised classification algorithms for healthcare
• Algorithms and strategies for air-pollution study in human-life quality
• Mathematics for Data mining and Machine Learning
• Machine Learning methods for human-life improving
• High-performance and numerical methods for Big Human Data analysis

Workshop Organizers