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
Intelligent Data and Information Processing Application in the Digital Economy
share announcement

Intelligent Data and Information Processing Application in the Digital Economy

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

Deadline for manuscript submissions: 15 October 2025 | Viewed by 4373

Special Issue Editors

Dr. John Wang

E-Mail Website
Guest Editor
Feliciano School of Business, Montclair State University, Montclair, NJ 07043, USA
Interests: business analytics; data mining; supply chain management; decision support systems
Special Issues, Collections and Topics in MDPI journals
Dr. Yanchang Zhao

E-Mail Website
Guest Editor
Data61, CSIRO, Canberra, ACT 2601, Australia
Interests: interpretable/explainable AI; causal discovery and inference; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the current era of rapid technological advancement and the exponential growth of data within the digital economy, we are confronted with a significant challenge: the volume of data has far exceeded our capacity to efficiently process, analyze, store, and interpret data. This challenge is further intensified by the widespread adoption of social media and networking platforms, which have contributed to an unprecedented increase in data generation. As trillions of interconnected devices contribute to this vast expanse of data, it becomes imperative to extract meaningful insights to enhance our quality of life and drive global progress.

This Special Issue is dedicated to exploring the critical role of intelligent data and information processing in the digital economy. It provides a comprehensive examination of topics such as AI education, economics, finance, sustainability, ethics, governance, cybersecurity, blockchain, and knowledge management.

The research papers selected for this Special Issue will showcase the latest developments in the digital economy, encompassing areas such as big data analysis, data visualization, data pre-processing, data engineering, machine learning, neural networks, fuzzy logic, statistical pattern recognition, knowledge filtering, cryptography and cryptanalysis, databases and data mining, information hiding, cloud computing, information retrieval and integration, robotics, control systems, intelligent agents, and Command, Control, Communication, and Computers (C4) technologies.

We are confident that this collection of papers will serve as a valuable resource for researchers and professionals engaged in the advancement of intelligent data and information processing.

We look forward to receiving your esteemed contributions.

Dr. John Wang
Dr. Yanchang Zhao
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

  • intelligent data analysis
  • big data processing
  • information processing
  • digital economy

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 (2 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

15 pages, 1314 KiB  
Article
Optimization Study of Coal Gangue Detection in Intelligent Coal Selection Systems Based on the Improved Yolov8n Model
by Guilin Zong, Yurong Yue and Wei Shan
Electronics 2024, 13(21), 4155; https://doi.org/10.3390/electronics13214155 - 23 Oct 2024
Cited by 2 | Viewed by 1098
Abstract
To address the low recognition accuracy of models for coal gangue images in intelligent coal preparation systems—especially in identifying small target coal gangue due to factors such as camera angle changes, low illumination, and motion blur—we propose an improved coal gangue separation model, [...] Read more.
To address the low recognition accuracy of models for coal gangue images in intelligent coal preparation systems—especially in identifying small target coal gangue due to factors such as camera angle changes, low illumination, and motion blur—we propose an improved coal gangue separation model, Yolov8n-improvedGD(GD—Gangue Detection), based on Yolov8n. The optimization strategy includes integrating the GCBlock(Global Context Block) from GCNet(Global Context Network) into the backbone network to enhance the model’s ability to capture long-range dependencies in images and improve recognition performance. The CGFPN (Contextual Guidance Feature Pyramid Network) module is designed to optimize the feature fusion strategy and enhance the model’s feature expression capabilities. The GSConv-SlimNeck architecture is employed to optimize computational efficiency and enhance feature map fusion capabilities, thereby improving the model’s robustness. A 160 × 160 scale detection head is incorporated to enhance the sensitivity and accuracy of small coal and gangue detection, mitigate the effects of low-quality data, and improve target localization accuracy. Full article
(This article belongs to the Special Issue Intelligent Data and Information Processing Application in the Digital Economy)
►▼ Show Figures

Figure 1

29 pages, 420 KiB  
Article
Predictive Modeling of Customer Response to Marketing Campaigns
by Mohammed El-Hajj and Miglena Pavlova
Electronics 2024, 13(19), 3953; https://doi.org/10.3390/electronics13193953 - 7 Oct 2024
Viewed by 2808
Abstract
In today’s data-driven marketing landscape, predicting customer responses to marketing campaigns is essential for optimizing both engagement and Return On Investment (ROI). This study aims to develop a predictive model using a Decision Tree (DT) to identify key factors influencing customer behavior and [...] Read more.
In today’s data-driven marketing landscape, predicting customer responses to marketing campaigns is essential for optimizing both engagement and Return On Investment (ROI). This study aims to develop a predictive model using a Decision Tree (DT) to identify key factors influencing customer behavior and improve campaign targeting. The methodology involves building the DT model, initially achieving an accuracy of 87.3%. However, the model faced challenges with precision and recall due to class imbalance. To address this, a resampling technique was applied, which significantly improved model performance, increasing recall from 44% to 83.1% and the F1-score from 49% to 74.2%. Key influential features identified include the recency of a customer’s purchase, their duration as a customer, and their response history to previous campaigns. This study demonstrates the practicality and interpretability of the DT model, offering actionable insights for marketing professionals seeking to enhance campaign effectiveness and customer targeting. Full article
(This article belongs to the Special Issue Intelligent Data and Information Processing Application in the Digital Economy)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop