Journal Information
Engineering Applications of Artificial Intelligence (EAAI)
https://www.sciencedirect.com/journal/engineering-applications-of-artificial-intelligence
Impact Factor:
7.500
Publisher:
Elsevier
ISSN:
0952-1976
Viewed:
36054
Tracked:
66
Call For Papers
The International Journal of Intelligent Real-Time Automation

A journal of IFAC, the International Federation of Automatic Control Artificial Intelligence (AI) is playing a major role in the fourth industrial revolution and we are seeing a lot of evolution in various machine learning methodologies. AI techniques are widely 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. Submitted papers should report some novel aspects of AI used for a real world engineering application and also validated using some public data sets for easy replicability of the research results.

Papers which do not respect the 4 following conditions will be desk-rejected without being sent to reviewers:

    Papers on new metaphor-based metaheuristics are very rarely accepted by EAAI (see details on this in the guide online).
    The abstract should clearly specify which is the contribution in AI and which is the application in engineering.
    The use of undefined acronyms in the title and in the abstract is forbidden.
    The papers must be formatted in single-column format.

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

    Internet–of–things and cyber-physical systems
    Intelligent transportation systems & smart vehicles
    Big data analytics, understanding complex networks
    Neural networks, fuzzy systems, neuro-fuzzy systems
    Deep learning and real world applications
    Self-organizing, emerging or bio-inspired system
    Global optimization, Meta-heuristics and their applications: Evolutionary Algorithms, swarm intelligence, nature and biologically inspired meta-heuristics, etc.
    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
    Real-time intelligent automation, and their associated supporting methodologies and techniques, including control theory and industrial informatics
    Knowledge processing, knowledge elicitation and acquisition, knowledge representation, knowledge compaction, knowledge bases, expert 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
    Industrial experiences in the application of the above techniques, e.g. case studies or benchmarking exercises
    Robotics

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

The Editors of Engineering Applications of Artificial Intelligence wish to inform authors that this journal will not publish papers that propose "novel" metaphor-based metaheuristics, unless the authors:

    present their method using the normal, standard optimization terminology;
    show that the new method brings useful and novel concepts to the field;
    motivate the use of the metaphor on a sound, scientific basis;
    present a fair comparison with other state-of-the-art methods using state-of-the-art practices for benchmarking algorithms.

For more details on the International Federation of Automatic Control (IFAC), visit their home page at http://www.ifac-control.org

Software publication

We invite you to convert your open source software into an additional journal publication in Software Impacts, a multi-disciplinary open access journal. Software Impacts provides a scholarly reference to software that has been used to address a research challenge. The journal disseminates impactful and re-usable scientific software through Original Software Publications which describe the application of the software to research and the published outputs.

For more information contact us at: software.impacts@elsevier.com
Last updated by Dou Sun in 2024-07-13
Special Issues
Special Issue on Artificial Intelligence for High-Performance Computing systems
Submission Date: 2024-09-15

High-performance computing (HPC) and artificial intelligence (AI) are increasingly being used together to solve complex problems. On the one hand HPC can be used to accelerate the training and deployment of machine learning (ML) models on HPC systems, while AI can also be used to optimize the performance of HPC systems themselves. Designing and implementing algorithms efficiently exploiting such complex and heterogeneous systems opens numerous research challenges. The aim of the « AI for HPC systems » special issue is thus to collect works addressing those, including (but not limited to) AI algorithms on GPU, TPU, FPGA; AI for energy efficient HPC and Cloud systems; real world applications of AI on HPC systems. Guest editors: Dr. Grégoire Danoy Dr. Grégoire Danoy (Executive Guest Editor) University of Luxembourg, Esch-sur-Alzette, Luxembourg Email: gregoire.danoy@uni.lu Areas of Expertise: artificial intelligence, optimisation, metaheuristics, machine learning Dr. Didier El Baz Dr. Didier El Baz Team SARA, LAAS-CNRS, Toulouse, France Email: elbaz@laas.fr Areas of Expertise: Parallel computing, distributed computing, optimization, machine learning Manuscript submission information: Tentative Schedule: First Submission Date: 27th October 2023 Final Submission Deadline: 15th September 2024 Notification of Acceptance: 31st December 2024 Contributed papers must be submitted via the Engineering Applications of Artificial Intelligence online submission system (Editorial Manager®): Please select the article type “VSI: AI for HPC Systems” when submitting the manuscript online. Please refer to the Guide for Authors to prepare your manuscript For any further information, the authors may contact the Guest Editors. Keywords: (high performance computing) AND (artificial intelligence) OR (machine learning) OR (optimization)
Last updated by Dou Sun in 2024-07-13
Special Issue on Metaheuristics for Sustainable Manufacturing
Submission Date: 2024-10-31

The term manufacturing refers to the secondary industry in the supply chain domain in which a type of raw material is converted into a product usable to the customer. Generally, manufacturing involves the utilization of labor, traditional and automated machine tools, chemical, and biological processes, assembly, etc. The customer could be the end-user or next level of the supply chain in which the product could be transformed with added value or assembled further with other products. For the manufacturing industry to survive and thrive innovation, quality, safety, and competitiveness must be achieved at the minimum cost, i.e., efficiently utilizing the available resources such as material, labor, machines tools, land, time, robots, computer software and hardware, etc. As manufacturing is generally carried out on a large scale, any efficiency improvement in the utilization of these resources impacts positively on the entire supply chain and the end product. The result could be reduction in the overall cost of manufacturing, reduction in pollution and waste, improvement in the quality of the product, increase in customer satisfaction, etc., which eventually nurtures sustainability. It is important in global viewpoint especially for developing countries where the production is being outsourced and the customer market is all over the world. The efficient utilization here refers to the minimization of cost, and maximization of yield satisfying the constraints. There are several classical optimization approaches have been studied in the literature and being applied in the manufacturing industries for optimizing several associated parameters and variables. The common optimization approaches are associated with Linear Programming Problem and heuristics. However, as the manufacturing designs are becoming more complex and miniature with inclination towards increase in variety, quality improvement and cost minimization, it is becoming essential to develop and employ novel optimization techniques which can handle a variety of class of data, give the rich and acceptable quality of solutions at reasonable computational cost. In recent times, several Artificial Intelligence (AI) based nature-inspired optimization methods have been proposed. They are commonly referred to as metaheuristics. They could be further classified as bio-inspired, socio-inspired and physics-based methods. The methods are driven by simple rules in the specific algorithmic framework. So far, these algorithms have been applied in several domains such as transportation, healthcare, design engineering, etc.; however, optimization-related discussion in the multifaceted domain of manufacturing is still quite limited. The issue can accommodate the original contributions from within the below domains (not limited to): 1. Novel or modified metaheuristics for manufacturing quality control and reliability 2. Metaheuristic solutions to enhance process efficiency and sustainability 3. Nature-inspired optimization methods in production, manufacturing and logistics 4. Metaheuristic solutions to human resource and safety 5. Metaheuristic solutions to Industrial waste Management 6. Metaheuristic solutions to material movement and handling systems 7. Novel or modified metaheuristics for machining processes 8. Novel or modified metaheuristics for manufacturing processes 9. Metaheuristic solutions to improve energy consumption Guest editors: Dr. Anand J Kulkarni (Executive Guest Editor) MIT World Peace University, Pune, India Email: anand.j.kulkarni@mitwpu.edu.in Areas of Expertise: optimization, metaheuristics Dr. Patrick Siarry Université Paris-Est Creteil, Creteil, France Email: siarry@u-pec.fr Areas of Expertise: optimization, metaheuristics Manuscript submission information: Tentative Schedule: Submission Open Date: April 30, 2024 Submission Deadline: October 31, 2024 Notification of Acceptance: February 28, 2025
Last updated by Dou Sun in 2024-06-13
Special Issue on AI-Driven Innovations in Cyber-Physical Systems: Advancements, Challenges, and Ethical Considerations
Submission Date: 2024-12-13

The proposed special issue, titled "AI-Driven Innovations in Cyber-Physical Systems: Advancements, Challenges, and Ethical Considerations," aims to delve into the intricate relationship between Artificial Intelligence (AI) and Cyber-Physical Systems (CPS) across diverse domains. By gathering a variety of papers focusing on the intersection of AI and CPS, the issue will explore topics such as control systems, security, energy management, healthcare, manufacturing, and smart cities. This collection will highlight the dynamic synergy between AI and CPS, offering novel insights and pushing the boundaries of existing knowledge. With a focus on the rapid evolution of AI technologies within CPS and the inclusion of ethical considerations, this special issue aims to fill a void in the current research landscape. Aligned with the mission of the journal, the issue caters to a broad audience of researchers, practitioners, and policymakers interested in the interdisciplinary nature of AI and CPS. By addressing practical implications and ethical concerns, this collection seeks to provide valuable insights to professionals and academics alike. Overall, "AI-Driven Innovations in Cyber-Physical Systems" aims to contribute significantly to the field while resonating with the readership of the journal. Guest editors: Assist. Prof. Antonio Galli (Executive Guest Editor) Department of Electrical Engineering and Information Technologies, University of Naples, Italy Email: antonio.galli@unina.it Areas of Expertise: deep learning and big data analytics for industrial applications Prof. Vincenzo Moscato Department of Electrical Engineering and Information Technologies, University of Naples, Italy Email: vmoscato@unina.it Areas of Expertise: multimedia, knowledge management and big data analytics Prof. Mouzhi Ge Deggendorf Institute of Technology, Germany Email: mouzhi.ge@th-deg.de Areas of Expertise: big data analytics, intelligent healthcare systems, internet of things and recommender systems Manuscript submission information: Tentative Schedule: Submission Open Date: June 13, 2024 Submission Deadline: December 13, 2024 Notification of Acceptance: January 31, 2025
Last updated by Dou Sun in 2024-05-12
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