Pure and New Mathematics in AI

 

Journal abbreviation:

P. N. Math. AI

 

Pure and New Mathematics in AI is an international open-access peer-reviewed journal. The journal publishes original, high-quality research articles, review articles, editorials, commentaries, methods, and more. It is available for professionals in related fields worldwide to read and use, and we are committed to ensuring that articles published in it receive maximum visibility.
The journal focus areas include but are not limited to:

  1. Algorithm design and analysis
  2. Artificial intelligence
  3. Symbolic computation
  4. Software formal methods
  5. Artificial neural networks
  6. Machine learning
  7. Image processing
  8. Mathematical methods for biomedical imaging
  9. Intelligent computing
  10. Mathematical theory of information optics and its applications
  11. Information security, and digital signal processing.

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As part of the submission process, authors are required to check off their submission's compliance with all of the following items, and submissions may be returned to authors that do not adhere to these guidelines.

  1. The submission has not been previously published, nor is it under the consideration of another journal (or an explanation has been provided in Comments to the Editor).
  2. The submission file is in Microsoft Word format.
  3. Where available, URLs for the references have been provided.
  4. The text adheres to the stylistic and bibliographic requirements outlined in the Author Guidelines, which is found in About the Journal.
  5. If submitting to a peer-reviewed section of the journal, the instructions in Ensuring a Blind Review have been followed.
 

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Article Processing Charges (APCs)

Pure and New Mathematics in AI is an Open Access Journal under EnPress Publisher. All articles published in Pure and New Mathematics in AI are accessible electronically from the journal website without commencing any kind of payment. In order to ensure contents are freely available and maintain publishing quality, Article Process Charges (APCs) are applicable to all authors who wish to submit their articles to the journal to cover the cost incurred in processing the manuscripts. Such cost will cover the peer-review, copyediting, typesetting, publishing, content depositing and archiving processes. Those charges are applicable only to authors who have their manuscript successfully accepted after peer-review.

Journal TitleAPCs
Pure and New Mathematics in AI$800

We encourage authors to publish their papers with us and don’t wish the cost of article processing charges to be a barrier especially to authors from the low and lower middle income countries/regions. A range of discounts or waivers are offered to authors who are unable to pay the article processing charges. Authors can write in to apply for a waiver and requests will be considered on a case-by-case basis.

 

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Vol 1, No 1 (2024)

Table of Contents

Open Access
Original Research Article
Article ID: 8052
PDF
by Dominic Rando, Yun Lu, Myung Soon Song, Francis J. Vasko
P. N. Math. AI 2024 , 1(1);    29 Views
Abstract In the operations research (OR) literature several highly efficient solution methods for the Quadratic Knapsack Problem (QKP) have been documented. However, these solution approaches are not readily available for industrial applications. In this short paper, we demonstrate that OR practitioners must be careful in their use of general-purpose integer programming software such as Gurobi when solving QKPs. We verify the very positive impact of fine-tuning parameters when solving QKPs with Gurobi.
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Open Access
Original Research Article
Article ID: 6585
PDF
by Guodong Zhang
P. N. Math. AI 2024 , 1(1);    39 Views
Abstract New estimations on settling-time for fixed-time stabilization of nonlinear systems are derived. By using the new proposed results on fixed-time stable and designing proper effective event-triggered control (ETC), fixed-time stabilization (FTS) for a kind of delayed neural networks is investigated. The new estimations on settling-time for fixed-time stabilization can be used to discussed other systems, such as complex networks, multi-agent systems and so on. At last, example simulations are given to corroborate the effectiveness of the derived results.
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