Tags
Language
Tags

New Trends in Database and Information Systems

Posted By:
New Trends in Database and Information Systems

New Trends in Database and Information Systems: ADBIS 2021 Short Papers, Doctoral Consortium and Workshops: DOING, SIMPDA, MADEISD, MegaData, CAoNS, Tartu, Estonia, August 24-26, 2021, Proceedings by Ladjel Bellatreche
English | EPUB | 2021 | 332 Pages | ISBN : 3030850811 | 26.1 MB

This book constitutes thoroughly reviewed and selected short papers presented at the 25th East-European Conference on Advances in Databases and Information Systems, ADBIS 2021, as well as papers presented at doctoral consortium and ADBIS 2021 workshops. Due to the COVID-19 the conference and satellite events were held in hybrid mode.

New Trends in Database and Information Systems

Posted By:
New Trends in Database and Information Systems

New Trends in Database and Information Systems: ADBIS 2021 Short Papers, Doctoral Consortium and Workshops: DOING, SIMPDA, MADEISD, MegaData, CAoNS, Tartu, Estonia, August 24-26, 2021, Proceedings by Ladjel Bellatreche
English | PDF | 2021 | 332 Pages | ISBN : 3030850811 | 21.4 MB

This book constitutes thoroughly reviewed and selected short papers presented at the 25th East-European Conference on Advances in Databases and Information Systems, ADBIS 2021, as well as papers presented at doctoral consortium and ADBIS 2021 workshops. Due to the COVID-19 the conference and satellite events were held in hybrid mode.

Data-Driven Modelling of Non-Domestic Buildings Energy Performance: Supporting Building Retrofit Planning

Posted By:
Data-Driven Modelling of Non-Domestic Buildings Energy Performance: Supporting Building Retrofit Planning

Data-Driven Modelling of Non-Domestic Buildings Energy Performance: Supporting Building Retrofit Planning by Saleh Seyedzadeh
English | EPUB | 2021 | 161 Pages | ISBN : 3030647501 | 14.2 MB

This book outlines the data-driven modelling of building energy performance to support retrofit decision-making. It explains how to determine the appropriate machine learning (ML) model, explores the selection and expansion of a reasonable dataset and discusses the extraction of relevant features and maximisation of model accuracy.

Data-Driven Modelling of Non-Domestic Buildings Energy Performance: Supporting Building Retrofit Planning

Posted By:
Data-Driven Modelling of Non-Domestic Buildings Energy Performance: Supporting Building Retrofit Planning

Data-Driven Modelling of Non-Domestic Buildings Energy Performance: Supporting Building Retrofit Planning by Saleh Seyedzadeh
English | PDF | 2021 | 161 Pages | ISBN : 3030647501 | 6.6 MB

This book outlines the data-driven modelling of building energy performance to support retrofit decision-making. It explains how to determine the appropriate machine learning (ML) model, explores the selection and expansion of a reasonable dataset and discusses the extraction of relevant features and maximisation of model accuracy.

Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications

Posted By:
Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications

Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications by Xiao-Sheng Si
English | PDF,EPUB | 2017 | 436 Pages | ISBN : 3662540282 | 23.8 MB

This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail.

Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis

Posted By:
Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis

Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis by Sujit Rokka Chhetri
English | EPUB | 2020 | 240 Pages | ISBN : 3030379612 | 33.03 MB

This book provides a new perspective on modeling cyber-physical systems (CPS), using a data-driven approach. The authors cover the use of state-of-the-art machine learning and artificial intelligence algorithms for modeling various aspect of the CPS.