Data Science and Big Data in Biology, Physical Science and Engineering
Materialtyp:
ArtikelUtgivningsinformation: MDPI - Multidisciplinary Digital Publishing Institute 2024Beskrivning: 1 electronic resource (238 p.)Innehållstyp: - text
- computer
- online resource
- 9783725800353
- 9783725800360
- Computing and Information Technology
- Computer science
- CNN
- COVID-19
- IT governance
- IT performance monitoring
- Industry 5.0
- SuperLearner ensemble machine learning
- agile development
- artificial intelligence
- back propagation
- big data
- bioarchaeology
- biodiversity
- business intelligence
- central Italy
- churn prediction
- classification
- combined data sampling techniques
- cross-validation
- cyber infrastructure
- cyber-physical systems
- data access
- data analytics
- data curation
- data generation
- data model
- data pre-processing
- data warehouse
- decision tree
- deep learning
- deep neural network
- deep transfer learning
- design thinking
- discretization
- dynamic storage location assignment
- enterprise system
- generalized low rank model
- genetic algorithm
- gradient descent
- hyperparameter optimization
- imbalanced data
- logistics
- machine learning
- neural network
- nonlinear data classification
- pedagogy
- plan-oriented
- program management
- progressive learning
- reinforcement learning
- rough set theory
- science communication
- self-awareness system
- self-directed design
- self-directed learning
- self-service tools
- sex predictio
Open Access Unrestricted online access star
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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eng
Freely available e-book