ISSN: 2661-3220 (Online)

Email:aia@bilpublishing.com

Artificial Intelligence Advances publishes original research papers that offers professional review and publication to freely disseminate research findings in all areas of Basic and Applied Computational Intelligence including Cognitive Aspects of Artificial Intelligence (AI), Constraint Processing, High–Level Computer Vision, Common Sense Reasoning and more. The Journal focuses on innovations of research methods at all stages and is committed to providing theoretical and practical experience for all those who are involved in these fields.

Artificial Intelligence Advances aims to discover innovative methods, theories and studies in its field by publishing original articles, case studies and comprehensive reviews.

The scope of the papers in this journal includes, but is not limited to:

  • Planning and Theories of Action
  • Heuristic Search
  • High-Level Computer Vision
  • Multiagent Systems
  • Machine Learning
  • Intelligent Robotics
  • Knowledge Representation
  • Intelligent Interfaces
  • Cognitive Aspects of AI
  • Common Sense Reasoning
  • AI and Philosophy
  • Automated Reasoning and Interface
  • Reasoning Under Uncertainty

Submission Preparation Checklist

By submitting a manuscript to the journal, the Author(s) verifies that the following items have been met:
  1. The submission has not been previously published under another journal, or is currently under consideration for another journal.
  2. The submission format should be in Microsoft Word. Other word processing software may be considered.
  3. DOIs or URLs have been provided wherever possible in the Reference List.
  4. The document(s) have been formatted according to the requirements under Author Guidelines. The placement of illustrations, figures, graphs, tables, and equations have been integrated into the main manuscript.
  5. Instructions in Ensuring a Blind Review have been followed in order to comply with the double-blind peer review process employed.

Vol 4, No 1 (2022)

Table of Contents

Articles

Mengyue Zhang, Jinyong Chen, Gang Wang, Min Wang, Kang Sun

Article ID: 4124
Views - 90  (Abstract) PDF - 29  (Download)
Abstract: Target recognition based on deep learning relies on a large quantity of samples, but in some specific remote sensing scenes, the samples are very rare. Currently, few-shot learning can obtain high-performance target classification models using only a few samples, but most researches are based on the natural scene. Therefore, this paper pro...
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Navid Moshtaghi Yazdani, Reihaneh Kardehi Moghaddam, Mohammad Hasan Olyaei

Article ID: 4361
Views - 101  (Abstract) PDF - 15  (Download)
Abstract:Safety is an important aim in designing safe-critical systems. To design such systems, many policy iterative algorithms are introduced to find safe optimal controllers. Due to the fact that in most practical systems, finding accurate information from the system is rather impossible, a new online training method is presented in this paper to perform an iterat...
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Mohammad Hasan Olyaei, Ali Olyaei, Sumaya Hamidi

Article ID: 4419
Views - 102  (Abstract) PDF - 15  (Download)
Abstract: The world’s elderly population is growing every year. It is easy to say that the fall is one of the major dangers that threaten them. This paper offers a Trained Model for fall detection to help the older people live comfortably and alone at home. The purpose of this paper is to investigate appropriate methods for diagnosing falls by analyzing the motion ...
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Elaheh Gavagsaz

Article ID: 4668
Views - 47  (Abstract) PDF - 5  (Download)
Abstract: The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes. Because of its operation, the application of this classification may be limited to problems with a certain number of instances, particularly, when run time is a consideration. However, the classification of large amounts of data has become ...
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Announcements

 

Congratulations to Professor Xiao-jun Wu on his appointment as Co-Editor-In-Chief of AIA

 
Prof. Xiao-Jun Wu received his B.S. degree in mathematics from Nanjing Normal University, Nanjing, PR China in 1991 and M.S. degree in 1996, and Ph.D. degree in Pattern Recognition and Intelligent System in 2002, both from Nanjing University of Science and Technology, Nanjing, PR China, respectively. He was a fellow of United Nations University, International Institute for Software Technology (UNU/IIST) from 1999 to 2000. From 1996 to 2006, he taught in the School of Electronics and Information, Jiangsu University of Science and Technology where he was an exceptionally promoted professor. He joined School of Information Engineering (now renamed as School of Artificial Intelligence and Computer Science), Jiangnan University in 2006 where he is a distinguished professor. He won the most outstanding postgraduate award by Nanjing University of Science and Technology.  
Posted: June 01,2022 More...
 
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