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| 001 | 2199-4676 | ||
| 003 | DE-He213 | ||
| 005 | 20260610082455.0 | ||
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_a40860 _2local |
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| 210 | 1 | 0 | _aJ Reliable Intell Environ |
| 245 | 1 | 0 |
_aJournal of Reliable Intelligent Environments _h[electronic resource] / _cedited by Juan C. Augusto, Antonio Coronato. |
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer. |
|
| 300 | _bonline resource. | ||
| 520 | _a“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. . | ||
| 650 | 0 | _aComputers. | |
| 650 | 0 | _aSoftware engineering. | |
| 650 | 0 | _aArtificial intelligence. | |
| 650 | 0 | _aComputer simulation. | |
| 650 | 0 | _aUser interfaces (Computer systems). | |
| 650 | 0 | _aHuman-computer interaction. | |
| 650 | 0 | _aMedical informatics. | |
| 650 | 1 | 4 | _aHardware Performance and Reliability. |
| 650 | 2 | 4 | _aSoftware Engineering. |
| 650 | 2 | 4 | _aArtificial Intelligence. |
| 650 | 2 | 4 | _aComputer Modelling. |
| 650 | 2 | 4 | _aUser Interfaces and Human Computer Interaction. |
| 650 | 2 | 4 | _aHealth Informatics. |
| 700 | 1 |
_aAugusto, Juan C. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 700 | 1 |
_aCoronato, Antonio. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 776 | 0 | 8 |
_iPrinted version: _x2199-4668 |
| 856 | 4 | 0 |
_uhttp://link.springer.com/journal/40860 _zHybrid |
| 999 |
_c1876 _d1876 |
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