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
Recent Advances in Technologies and Optimization for Intelligent Data Processing
share announcement

Recent Advances in Technologies and Optimization for Intelligent Data Processing

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

Deadline for manuscript submissions: closed (15 September 2024) | Viewed by 25650

Special Issue Editors

Dr. Tony Huang

E-Mail Website
Guest Editor
Faculty of Transdisciplinary Innovation, University of Technology Sydney, Ultimo 2007, Australia
Interests: data science; network analysis and visualisation; human–computer interactions
Special Issues, Collections and Topics in MDPI journals
Dr. Lun-ping Hung

E-Mail Website
Guest Editor
Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei City 112, Taiwan
Interests: smart care; internet of things
Prof. Dr. Chun-Cheng Lin

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
Interests: metaheuristic algorithms; machine learning; internet of things; wireless networks; computational management science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We have recently entered a new age of data that presents a series of novel challenges to humans and social civilization. This Special Issue will bring together researchers and professionals from a diverse range of disciplines to promote cross-disciplinary research and collaboration on data-related issues and provide a platform for sharing cutting-edge cross-disciplinary research findings and technological advancements in the fields of information technology, data science, and optimization. By fostering collaboration and facilitating research and networking opportunities across relevant disciplines, this conference seeks to drive innovation in these areas.

As such, we invite submissions of original unpublished research articles and reviews (including extended conference papers) for presenting and discussing new research findings and recent technical advances. Our topics of interest include, but are not limited to, the following areas:

  • Big data;
  • Computer graphics and visual analytics;
  • Health care and informatics;
  • Data-based systems and interfaces;
  • Data science;
  • Optimization

Dr. Tony Huang
Dr. Lun-ping Hung
Prof. Dr. Chun-Cheng Lin
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

  • big data
  • internet of things
  • health informatics
  • human–computer interactions
  • visual analytics
  • data science
  • optimization

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 policies can be found here.

Published Papers (1 paper)

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

Other

38 pages, 2596 KiB  
Systematic Review
A Systematic Review of Synthetic Data Generation Techniques Using Generative AI
by Mandeep Goyal and Qusay H. Mahmoud
Electronics 2024, 13(17), 3509; https://doi.org/10.3390/electronics13173509 - 4 Sep 2024
Cited by 26 | Viewed by 25093
Abstract
Synthetic data are increasingly being recognized for their potential to address serious real-world challenges in various domains. They provide innovative solutions to combat the data scarcity, privacy concerns, and algorithmic biases commonly used in machine learning applications. Synthetic data preserve all underlying patterns [...] Read more.
Synthetic data are increasingly being recognized for their potential to address serious real-world challenges in various domains. They provide innovative solutions to combat the data scarcity, privacy concerns, and algorithmic biases commonly used in machine learning applications. Synthetic data preserve all underlying patterns and behaviors of the original dataset while altering the actual content. The methods proposed in the literature to generate synthetic data vary from large language models (LLMs), which are pre-trained on gigantic datasets, to generative adversarial networks (GANs) and variational autoencoders (VAEs). This study provides a systematic review of the various techniques proposed in the literature that can be used to generate synthetic data to identify their limitations and suggest potential future research areas. The findings indicate that while these technologies generate synthetic data of specific data types, they still have some drawbacks, such as computational requirements, training stability, and privacy-preserving measures which limit their real-world usability. Addressing these issues will facilitate the broader adoption of synthetic data generation techniques across various disciplines, thereby advancing machine learning and data-driven solutions. Full article
(This article belongs to the Special Issue Recent Advances in Technologies and Optimization for Intelligent Data Processing)
►▼ Show Figures

Figure 1

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