Data Science – Analytics and Applications
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Beschreibung:
This book offers the proceedings of the Second International Data Science Conference (iDSC2019), organized by Salzburg University of Applied Sciences, Austria. The Conference brought together researchers, scientists, and business experts to discuss new ways of embracing agile approaches to various facets of data science, including machine learning and artificial intelligence, data mining, data visualization, and communication. The papers gathered here include case studies of applied techniques, and theoretical papers that push the field into the future. The full-length scientific-track papers on Data Analytics are broadly grouped by category, including Complexity; NLP and Semantics; Modelling; and Comprehensibility.
Included among real-world applications of data science are papers on- Exploring insider trading using hypernetworks
- Data-driven approach to detection of autism spectrum disorder
- Anonymization and sentiment analysis of Twitter posts
Theoretical papers in the book cover such topics as Optimal Regression Tree Models Through Mixed Integer Programming; Chance Influence in Datasets with Large Number of Features; Adversarial Networks — A Technology for Image Augmentation; and Optimal Regression Tree Models Through Mixed Integer Programming.
Five shorter student-track papers are also published here, on topics such as
- State-of-the-art Deep Learning Methods to effect Neural Machine Translation from Natural Language into SQL
- A Smart Recommendation System to Simplify Projecting for a HMI/SCADA Platform
- Use of Adversarial Networks as a Technology for Image Augmentation
- Using Supervised Learning to Predict the Reliability of a Welding Process
This book offers the proceedings of the Second International Data Science Conference (iDSC2019), organized by Salzburg University of Applied Sciences, Austria. The Conference brought together researchers, scientists, and business experts to discuss new ways of embracing agile approaches to various facets of data science, including machine learning and artificial intelligence, data mining, data visualization, and communication. The papers gathered here include case studies of applied techniques, and theoretical papers that push the field into the future. The full-length scientific-track papers on Data Analytics are broadly grouped by category, including Complexity; NLP and Semantics; Modelling; and Comprehensibility.
Included among real-world applications of data science are papers on- Exploring insider trading using hypernetworks
- Data-driven approach to detection of autism spectrum disorder
- Anonymization and sentiment analysis of Twitter posts
Theoretical papers in the book cover such topics as Optimal Regression Tree Models Through Mixed Integer Programming; Chance Influence in Datasets with Large Number of Features; Adversarial Networks — A Technology for Image Augmentation; and Optimal Regression Tree Models Through Mixed Integer Programming.
Five shorter student-track papers are also published here, on topics such as
- State-of-the-art Deep Learning Methods to effect Neural Machine Translation from Natural Language into SQL
- A Smart Recommendation System to Simplify Projecting for a HMI/SCADA Platform
- Use of Adversarial Networks as a Technology for Image Augmentation
- Using Supervised Learning to Predict the Reliability of a Welding Process
German Abstracts.- Full Papers – Double Blind Reviewed.- Data Analytics | Complexity.- Data Analytics | NLP and Semantics.- Data Analytics | Modelling.- Data Analytics | Comprehensibility.- Short Papers