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. 

Special Issue on Artificial Intelligence for Machinery Diagnostics and Prognostics
Submission Date: 2023-10-15

Machinery diagnostics and prognostics are involved in several engineering areas, such as manufacturing, energy, transportation, and aerospace. In these areas, the failure of key components such as bearings, gears, and motors will have a serious negative impact on the operation of the entire equipment. Timely detection of faults in machinery can ensure operational efficiency and quality, and prevent catastrophic accidents.

With the development of sensor technology and communication technology, machinery diagnostics and prognostics have entered the era of big data. The combination of artificial intelligence technology and big data has significantly improved the accuracy of machinery diagnostics and prognostics. Artificial intelligence technology makes it possible to mine information about the health status of equipment directly from the raw data, but the distribution of the data is a key factor in determining the effectiveness of data mining. For example, data imbalance, a real and unavoidable data distribution in the field of machinery diagnostics and prognostics, can seriously impact the training process and the convergence correctness of diagnostic and prognostic models. Therefore, it is important to eliminate the negative effects of data imbalance when constructing machinery diagnostics and prognostics models in order to improve the accuracy of diagnostics and prognostics in practical applications.

Artificial Intelligence has received increasing attention for machinery diagnostics and prognostics in recent years. However, there are still some outstanding issues that need to be improved. This special issue aims at stimulating discussions through state-of-the-art contributions on the latest research and development, up-to-date issues, and challenges.

Special Issue on Machine Learning/Artificial Intelligence Application in Healthcare Supply Chain
Submission Date: 2023-10-30

Nowadays, the growing challenges of supply chains have put pressure on health organizations. Managing the healthcare supply chain is more complex than other industries because dealing with patient health requires accurate, adequate, and timely medical supplies.New technologies such as Artificial Intelligence (AI) and Machine Learning (ML) have affected most industries. The logistics or supply chain industry is influenced by advances in AI technology. This powerful technology enables the automation and simplification of countless processes and helps companies save time and money. ML/AI approaches using computer techniques to solve complex problems, especially healthcare supply chain problems, have many applications.

Guest editors:

Assoc. Prof. Dragan Pamučar (Executive Guest Editor)University of Belgrade, Faculty of Organisational Sciences, Belgrade, SerbiaEmail: dragan.pamucar@fon.bg.ac.rs, dpamucar@gmail.com Areas of Expertise: Artificial intelligence; Supply chain; Logistics & transportation; Fuzzysystems; MCDM

Dr. Fariba GoodarzianEngineering Group, School of Engineering, University of Seville, Camino de los Descubrimientos s/n, 41092 Seville, SpainEmail: faribagoodarzian@us.es, faribagoodarzian7070@gmail.com 
Areas of Expertise: Supply chain management; Operations research; Artificial Intelligence;Logistics & healthcare management

Dr. Peiman GhasemiUniversity of Vienna, Department of Business Decisions and Analytics, Kolingasse 14-16, 1090 Vienna, AustriaEmail: peiman.ghasemi@univie.ac.at 
Areas of Expertise: Operations research; Game theory; Optimization; Simulation methods

Assoc. Prof. Vladimir SimicUniversity of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11010 Belgrade, SerbiaEmail: vsima@sf.bg.ac.rs 
Areas of Expertise: Fuzzy sets; Decision support systems; Multi-criteria decision making; Supply chain optimization; Transport, logistics & healthcare

Senior Assistant Prof. Irfan AliDepartment of Statistics & Operations Research Aligarh Muslim University, Aligarh-202002, IndiaEmail: irfii.st@amu.ac.in 
Areas of Expertise: Applied statistics; Supply chain networks & management; Mathematical programming; Fuzzy logic & optimization; Multi-objective optimization 

Special Issue on Recent Advances on Digital Economy-Oriented Artificial Intelligence
Submission Date: 2023-12-31

Nowadays, the digital economy (DE) is a key economic development direction, and artificial intelligence (AI) is a critical strategic tool of DE. “AI-assisted economy” has emerged as a new economic paradigm, widely accepted by governments around the world, and is regarded as one of the most important implementations of DE. Thus, the development of digital economy-oriented artificial intelligence (DE-oriented AI) technologies has not only theoretical significance but also practical value. DE-oriented AI technologies are distinct from general AI technologies in that they are theories, methods, algorithms, and software to promote the economic development or transformation in a digital manner.

DE is the main economic form after the agricultural economy and the industrial economy, of which the typical characteristics are (1) treating the data resources as key elements, (2) integrating information and communication technologies, (3) using modern information network as main carrier, and (4) leveraging all-factor digital transformation as an important economic driving force. The viewpoint that is generally accepted by academic and industrial communities is that data, algorithms, and computing power are three essential aspects of AI.  In fact, these three elements are connected with the first three characteristics of DE: the data are the core element of both AI and DE; AI algorithms should be built based on the integration of information and communication technologies; and the improvement of computing power needs the support of modern information network to implement cross-cluster computing. The fourth characteristic of DE is precisely the AI's contribution to DE.

Although AI and DE have received increasing attention, there are some bottlenecks for AI applications in DE. For example, how to train an efficient big AI model with relatively low computational resource consumption, how to use advanced AI technologies to intellectualize economic activities, and how to formalize the relationship between DE and AI. Thus, there is a large opportunity to investigate reliable, robust and efficient DE-oriented AI technologies. To share the most recent advances, current challenges and potential applications of Theories and Methods for DE-oriented AI, we are delighted and honored to propose this special issue of Engineering Applications of Artificial Intelligence.