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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  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

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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);    47 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);    55 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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Open Access
Original Research Article
Article ID: 8499
PDF
by Jingxin Liu, Jun Peng, Amin Mansoori, Chaoran Zhan, Ye Huang, Huanbin Wang
P. N. Math. AI 2024 , 1(1);    4 Views
Abstract This paper presents a class of novel recurrent neural network approaches for a distributed partitioned optimization scenario, where the objective function is separable, strongly convex, and possibly nonsmooth, with the computation of a part of the solution being distributed to a vertex for execution. In our proposed algorithmic framework, the block splitting method allows the solution to be partitioned among vertices according to the divisible structure of the problem, so that each neuron only holds a local memory of the decision variable rather than the memory of the entire decision variable. A local timer is installed for each neuron. If a neuron is triggered by its own timer and a neighbor timer, it will reach an activated state and then update and transmit its own variable information. This asynchronous evolution strategy with time helps to save computational resources. The proposed algorithm is distributed and scalable, with the computation of a single neuron not depending on the size of the vertex network, and the convergence of the algorithm can be guaranteed.
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Open Access
Original Research Article
Article ID: 9798
PDF
by Ping Zhu, Pohua Lv, Weiming Zou, Xuetao Jiang, Jin Shi, Yang Zhang, Yirong Ma
P. N. Math. AI 2024 , 1(1);    33 Views
Abstract In order to give machines, the interpretable thinking ability of mathematicians, the automatic derivation engine for advanced mathematics symbolic systems was explored to develop, which could update machines from the shallow thinking ability, such as natural language understanding and elementary mathematical numerical computation, to deep thinking, such as equivalent derivation for symbolic systems. This article proposed the complex logic algorithm design and development method with the frameworks as the core components. Starting with problem-resolving examples, the initial idea, basic data structure, and programming features of this new method were introduced in detail. However, this article proposed the integrated development environment for this method, as well as the main scheduling algorithm, core process algorithm, workflow dynamic display algorithm, execution status monitoring algorithm, generalization processing method, etc. The new method could be applicable to intelligent system development tasks that needed to gradually accumulate instance experience and had practical significance for the complex logic algorithms development, visualization software design, reduction complexity for software test and maintenance, and software reliability improvement. This article used the application problem solved by partial differential equations as an example to explain this method from the whole process, such as lexical analysis, semantic analysis, symbolic system establishment, and equivalent derivation to result validation, demonstrating the new dynamism and potential for logic derivation-based classical artificial intelligence methods.
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