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Electronics
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Intelligent Data and Information Processing
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Intelligent Data and Information Processing

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

Deadline for manuscript submissions: closed (20 March 2025) | Viewed by 1423

Special Issue Editors

Dr. Gun-Woo Kim

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Gyeongsang National University, Jinju 52828, Republic of Korea
Interests: NLP in e-healthcare; data mining (R recommendation & summarization); big data processing
Dr. Suwon Lee

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Gyeongsang National University, Jinju 52828, Republic of Korea
Interests: object tracking; hashing; large-scale image retrieval; simultaneous localization and mapping; camera calibration; 3D reconstruction
Special Issues, Collections and Topics in MDPI journals
Dr. Sang-Min Choi

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Gyeongsang National University, Jinju 52828, Republic of Korea
Interests: recommender systems; algorithm design; topic modeling

Special Issue Information

Dear Colleagues,

The rapid advancement in artificial intelligence (AI) and machine/deep learning (ML/DL) technologies has significantly transformed how data and information are processed and analyzed across various fields. The Special Issue on "Intelligent Data and Information Processing" aims to bring together cutting-edge research and developments in this dynamic area, focusing on innovative techniques and applications that leverage intelligent systems for enhanced data management and processing.

We invite original research articles exploring (but not limited to) the following topics:

  1. Machine/Deep Learning and Data Mining
  • Novel algorithms and techniques in supervised, unsupervised, and reinforcement learning.
  • Advanced data mining methods for discovering patterns and insights from large datasets.
  • Applications of ML/DL and data mining in real-world scenarios.
  1. Intelligent Information Retrieval and Recommendation
  • Enhanced algorithms for efficient and effective information retrieval.
  • Personalized recommendation and context-aware search techniques.
  • Information retrieval systems and their practical applications.
  1. IoT and Sensor Data Processing
  • Intelligent methods for processing and analyzing data from IoT devices and sensors.
  • Applications of AI in smart cities, healthcare monitoring, and industrial IoT.
  • Challenges and solutions in IoT data management.
  1. Human-Centered AI
  • Techniques for making AI systems more interpretable and user-friendly.
  • Human-in-the-loop approaches for improving AI decision-making.
  • Ethical considerations and responsible AI practices.

Dr. Gun-Woo Kim
Dr. Suwon Lee
Dr. Sang-Min Choi
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

  • machine/deep learning algorithms
  • big data mining
  • personalized recommendation
  • context-aware search
  • IoT data analysis
  • human-in-the-loop AI
  • ethical AI practices

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

Published Papers (1 paper)

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Research

32 pages, 2696 KiB  
Article
COMCARE: A Collaborative Ensemble Framework for Context-Aware Medical Named Entity Recognition and Relation Extraction
by Myeong Jin, Sang-Min Choi and Gun-Woo Kim
Electronics 2025, 14(2), 328; https://doi.org/10.3390/electronics14020328 - 15 Jan 2025
Viewed by 910
Abstract
The rapid expansion of medical information has resulted in named entity recognition (NER) and relation extraction (RE) essential for clinical decision support systems. Medical texts often contain specialized vocabulary, ambiguous abbreviations, synonyms, polysemous terms, and overlapping entities, which introduce significant challenges to the [...] Read more.
The rapid expansion of medical information has resulted in named entity recognition (NER) and relation extraction (RE) essential for clinical decision support systems. Medical texts often contain specialized vocabulary, ambiguous abbreviations, synonyms, polysemous terms, and overlapping entities, which introduce significant challenges to the extraction process. Existing approaches, which typically rely on single models such as BiLSTM or BERT, often struggle with these complexities. Although large language models (LLMs) have shown promise in various NLP tasks, they still face limitations in handling token-level tasks critical for medical NER and RE. To address these challenges, we propose COMCARE, a collaborative ensemble framework for context-aware medical NER and RE that integrates multiple pre-trained language models through a collaborative decision strategy. For NER, we combined PubMedBERT and PubMed-T5, leveraging PubMedBERT’s contextual understanding and PubMed-T5’s generative capabilities to handle diverse forms of medical terminology, from standard domain-specific jargon to nonstandard representations, such as uncommon abbreviations and out-of-vocabulary (OOV) terms. For RE, we integrated general-domain BERT with biomedical-specific BERT and PubMed-T5, utilizing token-level information from the NER module to enhance the context-aware entity-based relation extraction. To effectively handle long-range dependencies and maintain consistent performance across diverse texts, we implemented a semantic chunking approach and combined the model outputs through a majority voting mechanism. We evaluated COMCARE on several biomedical datasets, including BioRED, ADE, RDD, and DIANN Corpus. For BioRED, COMCARE achieved F1 scores of 93.76% for NER and 68.73% for RE, outperforming BioBERT by 1.25% and 1.74%, respectively. On the RDD Corpus, COMCARE showed F1 scores of 77.86% for NER and 86.79% for RE while achieving 82.48% for NER on ADE and 99.36% for NER on DIANN. These results demonstrate the effectiveness of our approach in handling complex medical terminology and overlapping entities, highlighting its potential to improve clinical decision support systems. Full article
(This article belongs to the Special Issue Intelligent Data and Information Processing)
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