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Mathematics
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Advances of Intelligent Systems and Computing
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Advances of Intelligent Systems and Computing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 4956

Special Issue Editor

Prof. Dr. Changhai Nie

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China
Interests: software testing and software quality assurance; artificial intelligence

Special Issue Information

Dear Colleagues,

We are entering an intelligent era, where software is defining and realizing everything, data have become the soul and blood of the world, computing power is becoming the engine of the world, intelligent systems are everywhere, and intelligent computing is booming. The kernel of intelligent systems and computing is mathematics, which is the application of quantitative relations and spatial form laws in various fields.

After the system has achieved a certain level of intelligence, it can handle relatively simple tasks, so as to partially liberate people's productivity. At this time, increasing the scale of the system is equivalent to increasing the scale of manpower. Machine intelligence and human intelligence have their own advantages. The amount of machine computing is large and tireless. Therefore, it is possible to achieve fine management for many jobs, which often leads to cost savings.

For this Special Issue, we are seeking papers on modern intelligent systems and computing, which may cover but are not limited to the following areas: 

computational intelligence, soft computing (including neural networks, fuzzy systems, evolutionary computing and the integration of these paradigms), social intelligence, environmental intelligence, artificial life, virtual world and society, cognitive science and systems, perception and vision, DNA and immune based systems, self-organizing and adaptive systems, E-learning and teaching, people-centered computing, recommendation system, intelligent control, robotics, man–machine team, knowledge-based paradigm, learning paradigm, intelligent data analysis, knowledge management, intelligent agent, intelligent decision-making and support, intelligent network security, trust management, interactive entertainment, network intelligence and multimedia.

Prof. Dr. Changhai Nie
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • applied statistics
  • data mining
  • artificial intelligence
  • deep learning
  • machine learning
  • intelligent computing
  • statistical prediction
  • evolutionary computation
  • knowledge graph
  • big data

Published Papers (3 papers)

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Research

19 pages, 1581 KiB  
Article
Representing Hierarchical Structured Data Using Cone Embedding
by Daisuke Takehara and Kei Kobayashi
Mathematics 2023, 11(10), 2294; https://doi.org/10.3390/math11102294 - 15 May 2023
Viewed by 996
Abstract
Extracting hierarchical structure in graph data is becoming an important problem in fields such as natural language processing and developmental biology. Hierarchical structures can be extracted by embedding methods in non-Euclidean spaces, such as Poincaré embedding and Lorentz embedding, and it is now [...] Read more.
Extracting hierarchical structure in graph data is becoming an important problem in fields such as natural language processing and developmental biology. Hierarchical structures can be extracted by embedding methods in non-Euclidean spaces, such as Poincaré embedding and Lorentz embedding, and it is now possible to learn efficient embedding by taking advantage of the structure of these spaces. In this study, we propose embedding into another type of metric space called a metric cone by learning an only one-dimensional coordinate variable added to the original vector space or a pre-trained embedding space. This allows for the extraction of hierarchical information while maintaining the properties of the pre-trained embedding. The metric cone is a one-dimensional extension of the original metric space and has the advantage that the curvature of the space can be easily adjusted by a parameter even when the coordinates of the original space are fixed. Through an extensive empirical evaluation we have corroborated the effectiveness of the proposed cone embedding model. In the case of randomly generated trees, cone embedding demonstrated superior performance in extracting hierarchical structures compared to existing techniques, particularly in high-dimensional settings. For WordNet embeddings, cone embedding exhibited a noteworthy correlation between the extracted hierarchical structures and human evaluation outcomes. Full article
(This article belongs to the Special Issue Advances of Intelligent Systems and Computing)
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18 pages, 2523 KiB  
Article
A Two-Stage Framework for Directed Hypergraph Link Prediction
by Guanchen Xiao, Jinzhi Liao, Zhen Tan, Xiaonan Zhang and Xiang Zhao
Mathematics 2022, 10(14), 2372; https://doi.org/10.3390/math10142372 - 06 Jul 2022
Cited by 3 | Viewed by 1440
Abstract
Hypergraphs, as a special type of graph, can be leveraged to better model relationships among multiple entities. In this article, we focus on the task of hyperlink prediction in directed hypergraphs, which finds a wide spectrum of applications in knowledge graphs, chem-informatics, bio-informatics, [...] Read more.
Hypergraphs, as a special type of graph, can be leveraged to better model relationships among multiple entities. In this article, we focus on the task of hyperlink prediction in directed hypergraphs, which finds a wide spectrum of applications in knowledge graphs, chem-informatics, bio-informatics, etc. Existing methods handling the task overlook the order constraints of the hyperlink’s direction and fail to exploit features of all entities covered by a hyperlink. To make up for the deficiency, we present a performant pipelined model, i.e., a two-stage framework for directed hyperlink prediction method (TF-DHP), which equally considers the entity’s contribution to the form of hyperlinks, and emphasizes not only the fixed order between two parts but also the randomness inside each part. The TF-DHP incorporates two tailored modules: a Tucker decomposition-based module for hyperlink prediction, and a BiLSTM-based module for direction inference. Extensive experiments on benchmarks—WikiPeople, JF17K, and ReVerb15K—demonstrate the effectiveness and universality of our TF-DHP model, leading to state-of-the-art performance. Full article
(This article belongs to the Special Issue Advances of Intelligent Systems and Computing)
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23 pages, 2645 KiB  
Article
Sequence-to-Sequence Remaining Useful Life Prediction of the Highly Maneuverable Unmanned Aerial Vehicle: A Multilevel Fusion Transformer Network Solution
by Shaojie Ai, Jia Song and Guobiao Cai
Mathematics 2022, 10(10), 1733; https://doi.org/10.3390/math10101733 - 18 May 2022
Cited by 5 | Viewed by 1586
Abstract
The remaining useful life (RUL) of the unmanned aerial vehicle (UAV) is primarily determined by the discharge state of the lithium-polymer battery and the expected flight maneuver. It needs to be accurately predicted to measure the UAV’s capacity to perform future missions. However, [...] Read more.
The remaining useful life (RUL) of the unmanned aerial vehicle (UAV) is primarily determined by the discharge state of the lithium-polymer battery and the expected flight maneuver. It needs to be accurately predicted to measure the UAV’s capacity to perform future missions. However, the existing works usually provide a one-step prediction based on a single feature, which cannot meet the reliability requirements. This paper provides a multilevel fusion transformer-network-based sequence-to-sequence model to predict the RUL of the highly maneuverable UAV. The end-to-end method is improved by introducing the external factor attention and multi-scale feature mining mechanism. Simulation experiments are conducted based on a high-fidelity quad-rotor UAV electric propulsion model. The proposed method can rapidly predict more precisely than the state-of-the-art. It can predict the future RUL sequence by four-times the observation length (32 s) with a precision of 83% within 60 ms. Full article
(This article belongs to the Special Issue Advances of Intelligent Systems and Computing)
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