Computer Assisted Methods in Engineering and Science (CAMES) is a referred international journal, published quarterly, indexed by Scopus and EBSCO, providing a scientific exchange forum and an authoritative source of information in the field of computational sciences and related areas of applied engineering. The objective of the journal is to support researchers and practitioners by offering them the means facilitating access to the newest research results reported by leading experts in the field, publication of own contributions, and dissemination of information relevant to the scope of the journal.

Papers published in the journal will fall largely into three main categories:

  • Contributions presenting new research methods of mathematical modeling and computer simulations in engineering and applied sciences, including traditional areas such as solid and structural mechanics, material science, fluid dynamics, acoustics and electromagnetics but going beyond them to account for application relevant issues in physics, chemistry, biology and mathematics, scientific computing, large scale optimization, intelligent systems as well as in multi-scale and multi-physics problems.
  • Articles describing novel applications of computational techniques supporting engineering practice and education in areas like mechanical, aerospace, civil, naval, software, chemical and architectural engineering, materials science as well as demonstrations of their practical use in solving real life problems.
  • State-of-the-art tutorials, providing the readership with a guidance on important research directions as observed in the current world literature on computer assisted methods in engineering and sciences.

The journal will also publish book reviews and information on activities of the European Community on Computational Methods in Applied Sciences (ECCOMAS).

Print ISSN: 2299-3649

Journal Metrics

MNiSW (2021): 70   |   CiteScore 2020: 0.5   |   SJR 2021: 0.224   |   SNIP 2020: 0.111


Call for papers on Artificial Intelligence-based Future Intelligent Networks and Communication Security


Recent techniques in the field of science explores the study and development of algorithms that can learn from and make predictions and decisions based on collected data through the Intelligent devices. Big Data Analytics, AI and Software Defined Networking are helping to drive the management of data and usage of the exceptional increase of computational power provided by Cloud Computing. This Special Issue explores novel concepts and cutting-edge research and developments towards designing the fully automated advanced digital networks. Fostered by technological advances in Big Data, AI and ML, such systems potentially have a wide range of applications in networking and communication security. The special issue covers analytical techniques for handling the huge amount of data generated by the Internet of Things, from architectures and platforms to security and privacy issues, applications, and challenges as well as future directions. The next generation protocols will learn and train themselves dynamically to improve the reliability, fault tolerance, security, and storage optimization in Cloud. These new concepts will allow networks algorithms to ‘learn’ and change elastically based on the information they are exposed to.

Read more about Call for papers on Artificial Intelligence-based Future Intelligent Networks and Communication Security

Current Issue

Vol 29 No 1–2 (2022): Advanced Optimization Methods for Uncertainties in Intelligent Industrial Systems

Guest Editors: Dr. I. Jeena Jacob, Dr. Badrul Hisham bin Ahmad and Dr. Z. Faizal Khan

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