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 Biometrics-based Applications
Submission Date: 2023-08-15

Biometrics technologies were primarily used by law enforcement. Nowadays, they are increasingly being used by government agencies and private industries to verify a person's identity. Indeed, biometrics-based applications have come to play an integral role in society, e.g., for identity management, surveillance, access control, social and welfare management, and automatic border control, with these applications alone being used either directly or indirectly by billions of individuals. Biometrics systems recognize a person based on physiological characteristics, such as fingerprints, hand, facial features, iris patterns, or behavioral characteristics that are learned or acquired, including the signing way, typing rhythm, or even walking pattern.

This special issue is open to submissions of original research papers by any scientist or researcher of Artificial Intelligence areas for biometrics-based applications. The objective of this special issue is to provide a stage for worldwide researchers to publish their recent and original results in artificial intelligence for biometrics applications.

Topics of interest include, but are not limited to:

    Machine learning approaches
    Dictionary learning, sparse representations, hybrid representations
    Deep learning, transfer learning, domain adaptation, optimal transport
    Bayesian learning
    Fuzzy models
    Reinforcement learning
    Presentation Attack Detection
    Adversarial Attack Detection
    DeepFake Detection

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.