Journal Information
Engineering Applications of Artificial Intelligence (EAAI)
http://www.journals.elsevier.com/engineering-applications-of-artificial-intelligence/
Impact Factor:
2.819
Publisher:
ELSEVIER
ISSN:
0952-1976
Viewed:
8210
Tracked:
19

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Call For Papers
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: Genetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization, etc.,
• Knowledge processing, knowledge elicitation and acquisition, knowledge representation, knowledge compaction, knowledge bases, expert systems,
• Neural networks, fuzzy systems, neuro-fuzzy systems,
• 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,
• 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. 
Last updated by Dou Sun in 2018-11-05
Special Issues
Special Issue on Pushing Artificial Intelligence to Edge: Emerging Trends, Issues and Challenges
Submission Date: 2019-11-15

Driven by the Internet of Things (IoT), a new computing model - Edge computing - is currently evolving, which allows IoT data processing, storage and service supply to be moved from Cloud to the local Edge devices such as smart phones, smart gateways or routers and base stations that can offer computing and storage capabilities on a smaller scale in real-time. EoT pushes data storage, computing and controls closer to the IoT data source(s); therefore, it enables each Edge device to play its own role of determining what information should be stored or processed locally and what needs to be sent to the Cloud for further use. Thus, EoT enables IoT services to meet the requirements of low latency, high scalability and energy efficiency, as well as to mitigate the traffic burdens of the transport network. However, current expansion of the IoT and digital transformation is generating new demands on computing and networking infrastructures across all industries (automotive, aerospace, life safety, medical, entertainment and manufacturing, etc). Hence, it is becoming challenging for Edge computing to deal with these emerging IoT environments. In order to overcome this issue, there is a need for intelligent Edge or Artificial Intelligence (AI) powered Edge computing (Edge-AI) to manage all the new data needs from these sectors. AI with its machine learning (ML) abilities can be fused into Edge to extend its power for intelligently investigating, collecting, storing and processing the large amounts of IoT data to maximize the potential of data analytics and decision-making in real time with minimum delay. There are many application areas where Edge-AI can be used, such as fall detection systems for the elderly, intelligent clothes for safety applications, smart access systems, smart camera, smart fitness systems, pet monitoring systems, self-predictive electric drives, and so on. While researchers and practitioners have been making progress within the area of Edge-AI, still there exist several challenging issues that need to be addressed for its large-scale adoption. Some of these issues are: credibility and trust management, distributed optimization of multi-agent system in Edge, self-organization, self-configuration, and self-discovery of edge nodes, lack of standards in containerization area (Docker, Open Container Initiative etc.) for Edge-AI, security risk for the data that needs to be processed at the edge, lack of efficient scheduling algorithms to optimize AI or machine learning in Edge computing structure, new operating system for edge artificial intelligence, etc. This special issue targets a mixed audience of researchers, academics and industries from different communities to share and exchange new ideas, approaches, theories and practice to resolve the challenging issues associated with the leveraging of intelligent Edge paradigm. Therefore, the suggested topics of interest for this special issue include, but are not limited to: Novel middleware support for Edge intelligence Network function virtualization technologies that leverage Edge intelligence Trust, security and privacy issues for Edge-AI Distributed optimization of multi agent systems for Edge intelligence Self-organization, self-configuration, and self-discovery of Edge node Semantic interoperability for Edge intelligence Autonomic resource management for Edge-AI Mobility, Interoperability and Context-awareness management for Edge-AI Container based approach to implement AI in Edge Applications/services for Edge artificial intelligence New operating system for Edge intelligence 5G-enabled services for Edge intelligence Software and simulation platform for Edge AI AI, Blockchain and Edge computing
Last updated by Dou Sun in 2019-06-09
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