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Applied Sciences
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Innovative Applications of Artificial Intelligence in Engineering
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Innovative Applications of Artificial Intelligence in Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 1664

Special Issue Editors

Prof. Dr. Liang Bao

E-Mail Website
Guest Editor
School of Computer Science and Technology, Xidian University, Xi’an 710071, China
Interests: AI engineering; big data analytics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chase Wu

E-Mail Website
Guest Editor
Department of Data Science, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: big data; data-intensive computing; parallel and distributed computing; high-performance networking; large-scale scientific visualization; wireless sensor networks; cyber security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) engineering is an emergent discipline focused on developing tools, processes, and systems to enable the application of artificial intelligence in numerous real-world contexts, such as manufacturing, energy, transportation, and software engineering. According to Gartner, by 2025, the 10% of enterprises that establish AI engineering best practices will generate at least three times more value from their AI efforts than the 90% of enterprises that do not. Therefore, this Special Issue intends to present the latest ideas and practical applications in the field of AI engineering, from theory, methodology, and processes to its practical use.

This Special Issue will publish high-quality, original research papers. Topics of interest include, but are not limited to, the following:

  • Artificial intelligence, machine learning, and deep learning;
  • AI applications in intelligent manufacturing;
  • AI applications in intelligent mining;
  • AI applications in intelligent transportation;
  • AI for software engineering and software engineering for AI;
  • AI applications in many other engineering fields.

Prof. Dr. Liang Bao
Prof. Dr. Chase Wu
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. Applied Sciences 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
  • deep learning
  • intelligent manufacturing
  • intelligent transportation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

15 pages, 19648 KiB  
Article
End-to-End Information Extraction from Courier Order Images Using a Neural Network Model with Feature Enhancement
by Wei Shen, Han Li, Youbo Jin and Chase Q. Wu
Appl. Sci. 2025, 15(2), 698; https://doi.org/10.3390/app15020698 - 12 Jan 2025
Viewed by 687
Abstract
Recently, cross-border logistics has experienced rapid development. Cross-border logistics courier orders come in various formats, featuring diverse layouts. Additionally, there is no standardized format for the writing of address and other information on these courier orders. It is challenging for current automated recognition [...] Read more.
Recently, cross-border logistics has experienced rapid development. Cross-border logistics courier orders come in various formats, featuring diverse layouts. Additionally, there is no standardized format for the writing of address and other information on these courier orders. It is challenging for current automated recognition models to handle such images. In this paper, we presented an end-to-end trainable neural network model based on feature enhancement, SwFB, capable of achieving end-to-end conversion from raw images to structured text information. We constructed our feature enhancement module, Co-G-Ma, based on a convolutional neural network (CNN), gated recurrent unit (GRU), and multi-head attention. We collected real cross-border logistics courier order images from a postal company in Zhejiang province, China, to build our dataset, COFIE, and conducted a series of experiments to explore the impact of hyperparameters on the extraction of key field text. Comparative experiments were also performed with other models on publicly available datasets CORD and SROIE. The experimental results demonstrate that our model achieves advanced performance in extracting visual text information and exhibits strong generalization. Full article
(This article belongs to the Special Issue Innovative Applications of Artificial Intelligence in Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Results of text detection, recognition, and extraction.</p> Full article ">Figure 2
<p>An example of conversion from JSON to SEQs.</p> Full article ">Figure 3
<p>Structure of SwFB.</p> Full article ">Figure 4
<p>Complete images of courier orders.</p> Full article ">Figure 5
<p>Samples from COFIE.</p> Full article ">Figure 6
<p>Results of different learning rates.</p> Full article ">Figure 7
<p>Results of different input resolutions.</p> Full article ">Figure 8
<p>Predictions generated by our model.</p> Full article ">
23 pages, 38564 KiB  
Article
Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
by Soohyun Wang and Byoungkug Kim
Appl. Sci. 2024, 14(24), 11604; https://doi.org/10.3390/app142411604 - 12 Dec 2024
Viewed by 511
Abstract
In this study, we proposed a YOLOv8-based Multi-Level Multi-Head Attention mechanism utilizing EO and IR cameras to enable rapid and accurate detection of vessels of various sizes in maritime environments. The proposed method integrates the Scale-Sensitive Cross Attention module and the Self-Attention module, [...] Read more.
In this study, we proposed a YOLOv8-based Multi-Level Multi-Head Attention mechanism utilizing EO and IR cameras to enable rapid and accurate detection of vessels of various sizes in maritime environments. The proposed method integrates the Scale-Sensitive Cross Attention module and the Self-Attention module, with a particular focus on enhancing small object detection performance in low-resolution IR imagery. By leveraging a multi-level attention mechanism, the model effectively improves detection performance for both small and large objects, outperforming the baseline YOLOv8 model. To further optimize the performance of IR cameras, we introduced a color palette preprocessing technique and identified the optimal palette through a comparative analysis. Experimental results demonstrated that the Average Precision increased from 85.3 to 88.2 in EO camera images and from 68.2 to 73 in IR camera images when the Black Hot palette was applied. The Black Hot palette, in particular, provided high luminance contrast, effectively addressing the single-channel and low-resolution limitations of IR imagery, and significantly improved small object detection performance. The proposed technique shows strong potential for enhancing vessel detection performance under diverse environmental conditions and is anticipated to make a practical contribution to real-time maritime monitoring systems. Furthermore, by delivering high reliability and efficiency in data-constrained environments, this method demonstrates promising scalability for applications in various object detection domains. Full article
(This article belongs to the Special Issue Innovative Applications of Artificial Intelligence in Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Color mapping for IR camera image. (<b>a</b>) IR Original (<b>b</b>) Ironbow (<b>c</b>) Rainbow (<b>d</b>) White Hot (<b>e</b>) Black Hot (<b>f</b>) Sepia (<b>g</b>) Arctic.</p> Full article ">Figure 2
<p>Overall overview of Yolov8-based detection model.</p> Full article ">Figure 3
<p>The attention process in the SA and SSCA modules of ML-MHA. C represents the number of channels.</p> Full article ">Figure 4
<p>VC2050F Camera.</p> Full article ">Figure 5
<p>Performance improvement comparison using the ML-MHA mechanism in YOLO models for EO and IR cameras (<b>Top</b>) EO Camera; (<b>Bottom</b>) IR Camera.</p> Full article ">Figure 6
<p>Comparison of detection results using EO cameras under various environment. (<b>a</b>) Day; (<b>b</b>) Night; (<b>c</b>) Sunny; (<b>d</b>) Seafog.</p> Full article ">Figure 7
<p>Detection results of various color palettes for IR Camera. (<b>a</b>) IR Original; (<b>b</b>) Ground Truth; (<b>c</b>) Ironbow; (<b>d</b>) Rainbow; (<b>e</b>) White Hot; (<b>f</b>) Black Hot; (<b>g</b>) Sepia; (<b>h</b>) Arctic.</p> Full article ">Figure 8
<p>Comparison of detection results in an IR camera scene. (<b>a</b>) IR Original; (<b>b</b>) Black Hot; (<b>c</b>) Sepia.</p> Full article ">Figure 9
<p>Box plot of AP by color palette.</p> Full article ">
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