Engineering Applications of Artificial Intelligence

The International Journal of Intelligent Real-Time Automation

Engineering Applications of Artificial Intelligence - ISSN 0952-1976
Source Normalized Impact per Paper (SNIP): 2.148 Source Normalized Impact per Paper (SNIP):
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR): 1.371 SCImago Journal Rank (SJR):
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.
Impact Factor: 2.368 (2015) Impact Factor:
The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
© Thomson Reuters Journal Citation Reports 2015
5 Year Impact Factor: 2.604 (2015) Five-Year Impact Factor:
To calculate the five year Impact Factor, citations are counted in 2014 to the previous five years and divided by the source items published in the previous five years.
© Journal Citation Reports 2015, Published by Thomson Reuters
Volumes: Volumes 57-66
Issues: 10 issues
ISSN: 09521976

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Artificial Intelligence (AI) techniques are now being used by the practicing engineer to solve a whole range of hitherto intractable problems. This journal provides an international forum for rapid publication of work describing the practical application of AI methods in all branches of engineering.

Focal points of the journal include, but are not limited to innovative applications of:

• Real-time intelligent automation, and their associated supporting methodologies and techniques, including control theory and industrial informatics,

• Architectures, algorithms and techniques for distributed AI systems, including multi-agent based control and holonic control,

• Decision-support systems,

• Aspects of reasoning: abductive, case-based, model-based, non-monotonic, incomplete, progressive and approximate reasoning,

• Applications of chaos theory and fractals,

• Metaheuristics and their applications in intelligent automation and global optimization: Evolutionary Algorithms, swarm intelligence, nature and biologically inspired metaheuristics, etc.,

• Knowledge processing, knowledge elicitation and acquisition, knowledge representation, knowledge compaction, knowledge bases, expert systems,

• Neural networks, fuzzy systems, neuro-fuzzy systems,

• Deep learning and real world applications,

• Perception, e.g. image processing, pattern recognition, vision systems, tactile systems, speech recognition and synthesis,

• Aspects of software engineering, e.g. intelligent programming environments, verification and validation of AI-based software, software and hardware architectures for the real-time use of AI techniques, safety and reliability,

• Intelligent fault detection, fault analysis, diagnostics and monitoring,

• Self-organizing, emerging or bio-inspired system,

• Big data analytics, Understanding complex networks, Internet-of-things and cyber-physical systems, Intelligent transportation systems & smart vehicles

• Industrial experiences in the application of the above techniques, e.g. case studies or benchmarking exercises.

Engineering Applications of Artificial Intelligence publishes:

• Survey papers/tutorials.

• Contributed papers — detailed expositions of new research or applications.

• Case studies or software reviews — evaluative and descriptive reviews of existing available AI software systems, discussing the experience gained and lessons learnt from using or developing AI systems for engineering applications.

• IFAC EAAI Forum — problems arising from engineering practice, needing to be solved by somebody; solutions to problems discussed in this forum or elsewhere; critiques of a position or claim found in the literature.

For more details on the International Federation of Automatic Control (IFAC), visit their home page at