Data Science and Big Data in Biology, Physical Science and Engineering

doi:10.3390/books978-3-7258-0035-3

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Data Science and Big Data in Biology, Physical Science and Engineering

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February 2024

238 pages

ISBN978-3-7258-0036-0 (Hardback)
ISBN978-3-7258-0035-3 (PDF)

https://doi.org/10.3390/books978-3-7258-0035-3

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This book is a reprint of the Special Issue Data Science and Big Data in Biology, Physical Science and Engineering that was published in

Chemistry & Materials Science

Computer Science & Mathematics

Engineering

Environmental & Earth Sciences

Medicine & Pharmacology

Physical Sciences

Summary

Big Data analysis is one of the most contemporary areas of development and research in the present day. Tremendous amounts of data are generated every single day from digital technologies and modern information systems, such as cloud computing and Internet of Things (IoT) devices. Analysis of these enormous amounts of data has become a crucial need and requires a lot of effort in order to extract valuable knowledge for decision-making, which in turn will help both academia and industry.Big Data and Data Science have appeared due to the significant need for generating, storing, organizing, and processing immense amounts of data. Data scientists strive to use Artificial Intelligence (AI) and Machine Learning (ML) approaches and models to allow computers to detect and identify what the data represents and be able to detect patterns more quickly, efficiently, and reliably than humans.The goal behind this Special Issue is to explore and discuss various principles, tools, and models in the context of Data Science, as well as diverse and varied concepts and techniques in Big Data in Biology, Chemistry, Biomedical Engineering, Physics, Mathematics, and other areas that work with Big Data.

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Keywords

SuperLearner ensemble machine learning; cross-validation; generalized low rank model; bioarchaeology; sex prediction; central Italy; big data; biodiversity; data curation; data generation; cyber infrastructure; data access; science communication; rough set theory; genetic algorithm; discretization; classification; data pre-processing; business intelligence; self-service tools; systemic quality model; software selection; business intelligence; data model; data warehouse; enterprise system; IT governance; IT performance monitoring; warehouse management; logistics; dynamic storage location assignment; reinforcement learning; deep learning; artificial intelligence; neural network; deep neural network; decision tree; nonlinear data classification; back propagation; gradient descent; machine learning; deep learning; transfer learning; deep transfer learning; progressive learning; self-directed learning; self-directed design; pedagogy; plan-oriented; web evaluation criteria; design thinking; COVID-19; self-awareness system; cyber-physical systems; CNN; Industry 5.0; transformer models; web-based attacks; program management; data analytics; machine learning; artificial intelligence; agile development; machine learning; churn prediction; imbalanced data; combined data sampling techniques; hyperparameter optimization; n/a