Application of Advanced Computing and Artificial Intelligence in Engineering and Science

Dear Colleagues,

During the last decade, artificial intelligence reached a high level of maturity from scientific experimentation in labs to powerful applications in business and industry. AI comprises a rather wide range of methods and techniques spanning various approaches in symbolic and sub-symbolic information processing that evolved and matured during a short history of more than 50 years. They can be succinctly grouped into three intertwined classes of approaches comprising representation, reasoning and learning methods.

Modern AI techniques work hand-in-hand with advanced computing technologies based on computer networks and emerging computing architectures. Modern trends in advanced computing architectures include high-performance, cloud, edge, and fog computing, as well as IoT, distributed ledgers and blockchains.

Currently, AI technologies have penetrated virtually all areas of life and society. The recent technical performance of AI methods and technologies, as well as their impact in business and economy, present new and amazing achievements while also raising new scientific and practical challenges that require a careful investigation by the research community. In particular, the synergy between new AI technologies and applications with advanced computing architectures is seen to be of major importance. A notable example is the intertwining of decentralized computing and machine learning, which led to federated learning.

The topics of interest for this Special Issue address the application of advanced computing and artificial intelligence methods including, but not limited to:

• Machine learning (including classification and clustering, neural networks, deep and reinforcement learning, federated learning);
• Knowledge representation and ontologies;
• Reasoning (rule-based systems, logic programming, constraint satisfaction, theorem proving);
• Imprecise and uncertain reasoning (including Bayesian and fuzzy approaches);
• Combinatorial optimization and heuristic search;
• Bio- and nature-inspired computing;
• Natural language and speech processing;
• Computer vision;
• Distributed multi-agent systems;
• Agent-based modeling and simulation;
• High-performance, edge, fog, cloud computing, IoT
• Distributed ledgers and blockchains

to various fields of science and engineering including, but not limiting to:

• Autonomous driving;
• Electric vehicles;
• Smart Homes, smart cities and smart future;
• Hazard modeling and mitigation;
• Manufacturing and Industry 4.0;
• Remote sensing and operation;
• Robotics, automation and intelligent control;
• Energy management;
• Mobility, transportation and logistics;
• Agriculture 5.0 and Smart Farming;
• Healthcare and drug discovery.

Prof. Dr. Costin Badica
Prof. Dr. Nick Bassiliades
Dr. Kalliopi Kravari
Dr. Theodoros Kosmanis
Guest Editors

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