Journal of Reliable Intelligent Environments [electronic resource] / edited by Juan C. Augusto, Antonio Coronato.
Material type:
Continuing resourcePublisher: Cham : Springer International Publishing : Imprint: Springer.Description: online resourceISSN: - 2199-4676
- Computers
- Software engineering
- Artificial intelligence
- Computer simulation
- User interfaces (Computer systems)
- Human-computer interaction
- Medical informatics
- Hardware Performance and Reliability
- Software Engineering
- Artificial Intelligence
- Computer Modelling
- User Interfaces and Human Computer Interaction
- Health Informatics
“Intelligent Environments (IEs)” is growing fast as a multi-disciplinary field allowing many areas of research to have a real beneficial influence in our society. The basic idea behind these systems is that by enriching an environment with technology (sensors, processors, actuators, information terminals, and other devices interconnected through a network), a system can be built such that based on the real-time information gathered and the historical data accumulated, decisions can be taken to benefit the users of that environment. Expected benefits of this technology can be: (a) increasing safety (e.g., by monitoring lifestyle patterns or the latest activities and providing assistance when a possibly harmful situation is developing), (b) comfort (e.g., by adjusting temperature automatically), and (c) economy (e.g., controlling the use of lights). The Journal on Reliable Intelligent Environments focuses on theoretical developments and lessons learnt on the deployment of IEs. The broad areas represented in the journal reflect the fact that reliability of software systems is the result of a number of approaches combined so an overarching principle of our publication will be to be open and flexible to embrace all type of techniques which can increase confidence in Intelligent Environments systems: selection, installation and calibration of equipment deployment and maintenance problems requirements modeling simulation testing validation formal verification people-centred computing tools pilots design software development process security privacy usability safety performance quality of service large-scale integration scalability adaptiveness and autonomicity As of 2023 we only consider articles on Machine Learning which have a level of explainability and its quality will be an important element of assessment in deciding on whether to publish the submission. .
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