Software Technologies

15th International Conference, ICSOFT 2020, Online Event, July 7–9, 2020, Revised Selected Papers

AvMarten van Sinderen,Leszek A. Maciaszek

Häftad, Engelska, 2021

Del i serien Communications in Computer and Information Science

553 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Beskrivning

This book constitutes the thoroughly refereed proceedings of the 15th International Conference on Software Technologies, ICSOFT 2020, which was held virtually due to the Covid-19 pandemic. project management software; software and systems integration among others.

Produktinformation

Utgivningsdatum:2021-07-21
Mått:155 x 235 x 16 mm
Vikt:423 g
Format:Häftad
Språk:Engelska
Serie:Communications in Computer and Information Science
Antal sidor:263
Förlag:Springer Nature Switzerland AG
ISBN:9783030830069

Utforska kategorier

Innehållsförteckning

Shared Autonomous Mobility on Demand: A Fuzzy-based Approach and Its Performance in the Presence of Uncertainty.- Efficient Scheduling of Periodic, Aperiodic, and Sporadic Real-time Tasks with Deadline Constraints.- R-TNCES State Space Generation using Ontology-based Method on a Distributed Cloud-based Architecture.- MLCA: A Model-Learning-Checking Approach for IoT Systems.- A Real-time Integration of Semantic Annotations into Air Quality Monitoring Sensor Data.- On Improvement of Formal Verification of Reconfigurable Real-time Systems using TCTL and CTL-based Properties on IaaS Cloud Environment.- A Genetic Algorithm with Tournament Selection for Automated Testing of Satellite On-board Image Processing.- Model-based Threat Modeling for Cyber-physical Systems: A Computer-aided Approach.- A Machine Learning based Methodology for Web Systems Codeless Testing with Selenium.- Multilevel Readability Interpretation against Software Properties: A Data-centric Approach.- Efficient Verification of Reconfigurable Discrete-event System using Isabelle/HOL Theorem Prover and Hadoop.- A Method for the Joint Analysis of Numerical and Textual IT-System Data to Predict Critical System States.