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Electronics
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Application of Data Mining in Decision Support Systems (DSSs)
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Application of Data Mining in Decision Support Systems (DSSs)

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 202

Special Issue Editors

Prof. Dr. Yanwei Yu

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Ocean University of China, Qingdao 266005, China
Interests: data mining; machine learning; database systems
Special Issues, Collections and Topics in MDPI journals
Dr. Guiyuan Jiang

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Ocean University of China, Qingdao 266005, China
Interests: intelligent transportation systems; machine learning

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue on the “Application of Data Mining in Decision Support Systems (DSSs)” for an upcoming edition of Electronics. In recent years, data mining (DM) has emerged as a key technology to extract valuable insights from large datasets, and its integration with decision support systems has proven to be transformative in many domains.

This Special Issue highlights the importance of applying data mining techniques to enhance decision support systems across diverse domains, including healthcare, finance, marketing, e-government, transportation, and beyond. Data mining can automate complex decision processes, offer real-time insights, and help uncover hidden relationships in data that traditional approaches often overlook. With increasing reliance on data-driven strategies, the potential for improvement in decision making is enormous, making this research highly relevant and timely.

Original research articles and reviews are welcome for submission in this Special Issue. Research areas may include (but are not limited to) the following:

  • Integration of machine learning and data mining methods with DSSs;
  • Predictive analytics and decision making in DSSs;
  • Big data analytics for decision support;
  • Applications of data mining in healthcare, finance, supply chain, marketing, transportation, and e-government;
  • Data-driven decision models and frameworks;
  • Real-time decision making with data mining techniques;
  • Challenges and future directions for data mining in DSSs.

We look forward to receiving your contributions.

Prof. Dr. Yanwei Yu
Dr. Guiyuan Jiang
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

  • data mining
  • decision support systems
  • predictive analytics
  • machine learning
  • big data analytics

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Published Papers (1 paper)

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Research

22 pages, 4480 KiB  
Article
MGMR-Net: Mamba-Guided Multimodal Reconstruction and Fusion Network for Sentiment Analysis with Incomplete Modalities
by Chengcheng Yang, Zhiyao Liang, Tonglai Liu, Zeng Hu and Dashun Yan
Electronics 2025, 14(15), 3088; https://doi.org/10.3390/electronics14153088 (registering DOI) - 1 Aug 2025
Abstract
Multimodal sentiment analysis (MSA) faces key challenges such as incomplete modality inputs, long-range temporal dependencies, and suboptimal fusion strategies. To address these, we propose MGMR-Net, a Mamba-guided multimodal reconstruction and fusion network that integrates modality-aware reconstruction with text-centric fusion within an efficient state-space [...] Read more.
Multimodal sentiment analysis (MSA) faces key challenges such as incomplete modality inputs, long-range temporal dependencies, and suboptimal fusion strategies. To address these, we propose MGMR-Net, a Mamba-guided multimodal reconstruction and fusion network that integrates modality-aware reconstruction with text-centric fusion within an efficient state-space modeling framework. MGMR-Net consists of two core components: the Mamba-collaborative fusion module, which utilizes a two-stage selective state-space mechanism for fine-grained cross-modal alignment and hierarchical temporal integration, and the Mamba-enhanced reconstruction module, which employs continuous-time recurrence and dynamic gating to accurately recover corrupted or missing modality features. The entire network is jointly optimized via a unified multi-task loss, enabling simultaneous learning of discriminative features for sentiment prediction and reconstructive features for modality recovery. Extensive experiments on CMU-MOSI, CMU-MOSEI, and CH-SIMS datasets demonstrate that MGMR-Net consistently outperforms several baseline methods under both complete and missing modality settings, achieving superior accuracy, robustness, and generalization. Full article
(This article belongs to the Special Issue Application of Data Mining in Decision Support Systems (DSSs))
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