Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.6
Journals
Electronics
Special Issues
Feature Papers in Computer Science & Engineering, 2nd Edition
share announcement

Feature Papers in Computer Science & Engineering, 2nd Edition

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

Deadline for manuscript submissions: 15 April 2025 | Viewed by 3939

Special Issue Editors

Prof. Dr. Ping-Feng Pai

E-Mail Website
Guest Editor
Department of Information Management, National Chi Nan University, Nantou 54561, Taiwan
Interests: machine learning; neural networks; deep learning; big data; data mining; metaheuristics and optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Krzysztof Szczypiorski

E-Mail Website
Guest Editor
Institute of Telecommunications, Warsaw University of Technology, 00-665 Warszawa, Poland
Interests: cybersecurity; digital forensics; steganography; anomaly detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the Computer Science and Engineering Section is compiling a collection of papers on this field of research, welcoming both contributions and recommendations from Editorial Board Members and leading experts in the field.

This Special Issue aims to publish high-quality articles, including in-depth reviews of the state of the art and original, up-to-date contributions covering the use of intelligent models and/or the IoT in sectors of interest. Any submission introducing innovative elements and related to Deeptech is welcome. We hope that these articles will be widely read and have a great influence on the field as a whole. The articles will be compiled in a print edition after the deadline and will be appropriately promoted.

The topics of interest include all subjects involving advanced intelligence models and their applications in the following areas:

  • IoT and its applications;
  • Industry 4.0;
  • Smart cities;
  • Biotechnology;
  • Precision agriculture;
  • Fintech;
  • Quantum economy;
  • Blockchain;
  • Cybersecurity;
  • Big data analytics and artificial intelligence.

Prof. Dr. Ping-Feng Pai
Prof. Dr. Krzysztof Szczypiorski
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

  • IoT and its applications
  • Industry 4.0
  • smart cities
  • biotechnology
  • precision agriculture
  • fintech
  • quantum economy
  • blockchain
  • cybersecurity
  • big data analytics and artificial intelligence

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Related Special Issue

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

14 pages, 1015 KiB  
Article
Evaluating Causal Reasoning Capabilities of Large Language Models: A Systematic Analysis Across Three Scenarios
by Lei Wang and Yiqing Shen
Electronics 2024, 13(23), 4584; https://doi.org/10.3390/electronics13234584 - 21 Nov 2024
Viewed by 218
Abstract
Large language models (LLMs) have shown their capabilities in numerical and logical reasoning, yet their capabilities in higher-order cognitive tasks, particularly causal reasoning, remain less explored. Current research on LLMs in causal reasoning has focused primarily on tasks such as identifying simple cause-effect [...] Read more.
Large language models (LLMs) have shown their capabilities in numerical and logical reasoning, yet their capabilities in higher-order cognitive tasks, particularly causal reasoning, remain less explored. Current research on LLMs in causal reasoning has focused primarily on tasks such as identifying simple cause-effect relationships, answering basic “what-if” questions, and generating plausible causal explanations. However, these models often struggle with complex causal structures, confounding variables, and distinguishing correlation from causation. This work addresses these limitations by systematically evaluating LLMs’ causal reasoning abilities across three representative scenarios, namely analyzing causation from effects, tracing effects back to causes, and assessing the impact of interventions on causal relationships. These scenarios are designed to challenge LLMs beyond simple associative reasoning and test their ability to handle more nuanced causal problems. For each scenario, we construct four paradigms and employ three types of prompt scheme, namely zero-shot prompting, few-shot prompting, and Chain-of-Thought (CoT) prompting in a set of 36 test cases. Our findings reveal that most LLMs encounter challenges in causal cognition across all prompt schemes, which underscore the need to enhance the cognitive reasoning capabilities of LLMs to better support complex causal reasoning tasks. By identifying these limitations, our study contributes to guiding future research and development efforts in improving LLMs’ higher-order reasoning abilities. Full article
(This article belongs to the Special Issue Feature Papers in Computer Science & Engineering, 2nd Edition)
►▼ Show Figures

Figure 1

17 pages, 19605 KiB  
Article
TOLGAN: An End-To-End Framework for Producing Traditional Orient Landscape
by Booyong Kim, Heekyung Yang and Kyungha Min
Electronics 2024, 13(22), 4468; https://doi.org/10.3390/electronics13224468 - 14 Nov 2024
Viewed by 310
Abstract
We present TOLGAN that generates traditional oriental landscape (TOL) image from a map that specifies the locations and shapes of the elements composing TOL. Users can create a TOL map by using a user interface or a segmentation scheme from a photograph. We [...] Read more.
We present TOLGAN that generates traditional oriental landscape (TOL) image from a map that specifies the locations and shapes of the elements composing TOL. Users can create a TOL map by using a user interface or a segmentation scheme from a photograph. We design the generator of TOLGAN as a series of decoding layers where the map is applied between the layers. The generated TOL image is further enhanced through an AdaIN architecture. The discriminator of TOLGAN processes a generated image and its groundtruth TOL artwork image. TOLGAN is trained through a dataset composed of paired TOL artwork images and their TOL maps. We present a tool through which users can produce a TOL map by specifying and organizing the elements of TOL artworks. TOLGAN successfully generates a series of TOL images from the TOL map. We evaluate our approach using a quantitative way by estimating FID and ArtFID scores and a qualitative way by executing two user studies. Through these studies, we prove the excellence of our approach by comparing our results with those from several important existing works. Full article
(This article belongs to the Special Issue Feature Papers in Computer Science & Engineering, 2nd Edition)
►▼ Show Figures

Figure 1

11 pages, 2979 KiB  
Article
Rare Data Image Classification System Using Few-Shot Learning
by Juhyeok Lee and Mihui Kim
Electronics 2024, 13(19), 3923; https://doi.org/10.3390/electronics13193923 - 4 Oct 2024
Viewed by 736
Abstract
Advances in deep learning can address a variety of computer vision problems. In particular, deep learning has shown high performance in image processing. However, large datasets are required to train deep learning models. Previous studies have addressed the problem of data scarcity via [...] Read more.
Advances in deep learning can address a variety of computer vision problems. In particular, deep learning has shown high performance in image processing. However, large datasets are required to train deep learning models. Previous studies have addressed the problem of data scarcity via the few-shot learning technique. However, a drawback of these studies is that large datasets are required when new tasks are performed. Hence, this study uses data augmentation techniques to address this shortcoming. Furthermore, we propose an image classification system with a few-shot learning technique that achieves high accuracy, even on rare datasets. Compared with traditional image classification models, the proposed system improves classification accuracy by approximately 18% using 100 data points. Full article
(This article belongs to the Special Issue Feature Papers in Computer Science & Engineering, 2nd Edition)
►▼ Show Figures

Figure 1

Review

Jump to: Research

13 pages, 1682 KiB  
Review
Mapping Computer Vision Syndrome: An Engineering Problem in Human–Computer Interaction
by Dejan Viduka, Vanja Dimitrijević, Dragan Rastovac, Milan Gligorijević, Ana Bašić, Srđan Maričić and Stevan Jokić
Electronics 2024, 13(22), 4460; https://doi.org/10.3390/electronics13224460 - 14 Nov 2024
Viewed by 410
Abstract
Computer Vision Syndrome (CVS) is a highly prevalent syndrome today, yet it remains relatively understudied, leading to limited awareness among users about this syndrome and its preventive measures. This study aims to draw attention to this syndrome among authors and researchers and encourage [...] Read more.
Computer Vision Syndrome (CVS) is a highly prevalent syndrome today, yet it remains relatively understudied, leading to limited awareness among users about this syndrome and its preventive measures. This study aims to draw attention to this syndrome among authors and researchers and encourage further research in this area. Data were retrieved from the databases PubMed, Lens, Scopus, and Google Scholar, compiling existing articles and publications from the CVS domain. Analyses cover the period from 1 January to 31 December 2023. Zotero 6.0.27, VOSviewer 1.6.20, and Microsoft Excel software were used for data analysis. A total of 893 papers were reviewed, with 578 papers included in our analysis. The study presents five different analyses showing top authors and publishers, publication trends over the years, as well as papers by source, and, finally, the most frequently used keywords. The results highlight trends in various aspects related to this issue, through the analysis of published articles over the years, along with prominent authors and their respective countries. The focus of this research is on computer vision syndrome and its representation in scientific databases. What is clearly evident from this study is the increasing trend in research over the years, as well as the leading countries in these studies. However, it is also apparent that further research in this area is needed to bring new insights to researchers and raise awareness among users who encounter computers in their daily work. Full article
(This article belongs to the Special Issue Feature Papers in Computer Science & Engineering, 2nd Edition)
►▼ Show Figures

Figure 1

20 pages, 2132 KiB  
Review
How Artificial Intelligence (AI) Is Powering New Tourism Marketing and the Future Agenda for Smart Tourist Destinations
by Lázaro Florido-Benítez and Benjamín del Alcázar Martínez
Electronics 2024, 13(21), 4151; https://doi.org/10.3390/electronics13214151 - 23 Oct 2024
Viewed by 1744
Abstract
Artificial intelligence (AI) is a disruptive technology that is being used by smart tourist destinations (STDs) to develop new business models and marketing services to increase tourists’ experiences and sales, revenue, productivity, and efficiency and STDs. However, the adoption of AI applications and [...] Read more.
Artificial intelligence (AI) is a disruptive technology that is being used by smart tourist destinations (STDs) to develop new business models and marketing services to increase tourists’ experiences and sales, revenue, productivity, and efficiency and STDs. However, the adoption of AI applications and platforms requires a high economic budget for STDs that want to integrate this digital tool into their future agenda and tourism development plans, especially when they set them up for marketing plans and operational processes. This iterative technology needs regular maintenance as well, leading to recurring costs and specialised crews in advanced technologies and marketing activities. This study aims to show the impact of AI advancements on STDs’ tourism marketing to enhance the quality of services and illustrate their future agenda to improve tourists’ experiences. A comprehensive literature review on AI technology and STDs has been conducted to illustrate new tourism marketing in their future agenda. Moreover, this study presents real examples of AI technology in a tourism context to better understand the potential of this digital tool. The findings of the current study support the idea that AI is a multipurpose tool that helps manage, monitor, and analyse sales information; revenue management; minimise prediction errors; streamline operations; and develop better marketing strategies, optimising economic resources, reducing marketing costs, and responding dynamically to changing needs for tourists and residents in STDs. Furthermore, the investment in AI technologies by STDs helps enhance the quality of products and services, and attract new investments, which benefit the regional economies and population’s quality of life. This study is the first to address the use of AI to improve tourist marketing in STDs, which is its primary uniqueness. Also, this study identifies new opportunities and initiatives through AI that can be developed to help tourism marketing in STDs. Full article
(This article belongs to the Special Issue Feature Papers in Computer Science & Engineering, 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop