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Mathematics
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Artificial Intelligence: Large Language Models and Big Data Analysis
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Artificial Intelligence: Large Language Models and Big Data Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: closed (20 May 2025) | Viewed by 2210

Special Issue Editor

Dr. Zongwei Luo

E-Mail Website
Guest Editor
Division of Science and Technology, BNU-HKBU United International College, Zhuhai 519088, China
Interests: AI; big data analytics; FinTech; sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce a Special Issue dedicated to cutting-edge developments in the field of artificial intelligence (AI), with a specific focus on language model (LLM) technologies and big data analysis. This issue aims to serve as an archival repository of excellence, encompassing foundational advancements in LLMs, their application enablers, ethical considerations, and insightful data analyses. The application of data analysis and algorithms is crucial for harnessing the full potential of LLMs. Effective data analysis enables researchers to uncover insights from vast datasets, ensuring that LLMs are trained on high-quality, representative information.

The scope of this Special Issue is expansive, aiming to capture a comprehensive array of discussions and insights into the applications of LLMs across various domains. We welcome submissions that explore the diverse potentials and challenges of LLM technologies, including innovative use cases and case studies that highlight their ethical implications and societal impacts.

Contributions are encouraged from researchers and practitioners involved in advancing LLMs and related AI technologies and data analysis.

Dr. Zongwei Luo
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

  • artificial intelligence
  • foundational technologies
  • technology adoption
  • large language models
  • application enablers
  • big data analysis

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Published Papers (1 paper)

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Research

18 pages, 1747 KiB  
Article
Deep Reinforcement Learning Algorithm with Long Short-Term Memory Network for Optimizing Unmanned Aerial Vehicle Information Transmission
by Yufei He, Ruiqi Hu, Kewei Liang, Yonghong Liu and Zhiyuan Zhou
Mathematics 2025, 13(1), 46; https://doi.org/10.3390/math13010046 - 26 Dec 2024
Cited by 2 | Viewed by 1033
Abstract
The optimization of information transmission in unmanned aerial vehicles (UAVs) is essential for enhancing their operational efficiency across various applications. This issue is framed as a mixed-integer nonconvex optimization challenge, which traditional optimization algorithms and reinforcement learning (RL) methods often struggle to address [...] Read more.
The optimization of information transmission in unmanned aerial vehicles (UAVs) is essential for enhancing their operational efficiency across various applications. This issue is framed as a mixed-integer nonconvex optimization challenge, which traditional optimization algorithms and reinforcement learning (RL) methods often struggle to address effectively. In this paper, we propose a novel deep reinforcement learning algorithm that utilizes a hybrid discrete–continuous action space. To address the long-term dependency issues inherent in UAV operations, we incorporate a long short-term memory (LSTM) network. Our approach accounts for the specific flight constraints of fixed-wing UAVs and employs a continuous policy network to facilitate real-time flight path planning. A non-sparse reward function is designed to maximize data collection from internet of things (IoT) devices, thus guiding the UAV to optimize its operational efficiency. Experimental results demonstrate that the proposed algorithm yields near-optimal flight paths and significantly improves data collection capabilities, compared to conventional heuristic methods, achieving an improvement of up to 10.76%. Validation through simulations confirms the effectiveness and practicality of the proposed approach in real-world scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence: Large Language Models and Big Data Analysis)
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