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Electronics
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Data-Centric Artificial Intelligence: New Methods for Data Processing
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Data-Centric Artificial Intelligence: New Methods for Data Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 November 2024 | Viewed by 318

Special Issue Editors

Dr. Dawid Ewald

E-Mail Website
Guest Editor
Department of Computer Science, Kazimierz Wielki University, 85-064 Bydgoszcz, Poland
Interests: bee algorithms; fuzzy logic; artificial neural networks and their applications; language models; generative AI
Dr. Yutaka Watanobe

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu 965-8580, Japan
Interests: intelligent software; smart learning; cloud robotics; programming environment; visual languages
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data-centric artificial intelligence is developing rapidly thanks to advances in machine learning, natural language processing, and data visualization. These modern AI techniques enable us to better understand and process huge data sets, and provide companies and scientists with tools for extracting hidden patterns, discovering new knowledge, and automating complex analytical processes. In this Special Issue, we present application examples of these AI methods in solving real-world business and scientific problems.

We invite you to submit papers for this Special Issue dedicated to data-centric artificial intelligence, focusing on the following:

  1. New methods and techniques for processing large data sets;
  2. Topics related to machine learning, natural language processing, and data visualization;
  3. Practical applications of these methods in various fields.

This publication will supplement the existing literature by focusing on the latest trends and solutions in this area.

Dr. Dawid Ewald
Dr. Yutaka Watanobe
Guest Editors

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. Electronics 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 2400 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
  • machine learning
  • data processing
  • data visualization
  • natural language processing
  • fuzzy logic

Published Papers (1 paper)

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Research

16 pages, 858 KiB  
Article
Periodic Transformer Encoder for Multi-Horizon Travel Time Prediction
by Hui-Ting Christine Lin and Vincent S. Tseng
Electronics 2024, 13(11), 2094; https://doi.org/10.3390/electronics13112094 - 28 May 2024
Viewed by 148
Abstract
In the domain of Intelligent Transportation Systems (ITS), ensuring reliable travel time predictions is crucial for enhancing the efficiency of transportation management systems and supporting long-term planning. Recent advancements in deep learning have demonstrated the ability to effectively leverage large datasets for accurate [...] Read more.
In the domain of Intelligent Transportation Systems (ITS), ensuring reliable travel time predictions is crucial for enhancing the efficiency of transportation management systems and supporting long-term planning. Recent advancements in deep learning have demonstrated the ability to effectively leverage large datasets for accurate travel time predictions. These innovations are particularly vital as they address both short-term and long-term travel demands, which are essential for effective traffic management and scheduled routing planning. Despite advances in deep learning applications for traffic analysis, the dynamic nature of traffic patterns frequently challenges the forecasting capabilities of existing models, especially when forecasting both immediate and future traffic conditions across various time horizons. Additionally, the area of long-term travel time forecasting still remains not fully explored in current research due to these complexities. In response to these challenges, this study introduces the Periodic Transformer Encoder (PTE). PTE is a Transformer-based model designed to enhance traffic time predictions by effectively capturing temporal dependencies across various horizons. Utilizing attention mechanisms, PTE learns from long-range periodic traffic data for handling both short-term and long-term fluctuations. Furthermore, PTE employs a streamlined encoder-only architecture that eliminates the need for a traditional decoder, thus significantly simplifying the model’s structure and reducing its computational demands. This architecture enhances both the training efficiency and the performance of direct travel time predictions. With these enhancements, PTE effectively tackles the challenges presented by dynamic traffic patterns, significantly improving prediction performance across multiple time horizons. Comprehensive evaluations on an extensive real-world traffic dataset demonstrate PTE’s superior performance in predicting travel times over multiple horizons compared to existing methods. PTE is notably effective in adapting to high-variability road segments and peak traffic hours. These results prove PTE’s effectiveness and robustness across diverse traffic environments, indicating its significant contribution to advancing traffic prediction capabilities within ITS. Full article
(This article belongs to the Special Issue Data-Centric Artificial Intelligence: New Methods for Data Processing)
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