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Algorithms
Special Issues
Intelligent Information Processing Methods in Interdisciplinary
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Intelligent Information Processing Methods in Interdisciplinary

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 May 2027 | Viewed by 493

Special Issue Editor

Dr. Xiaoqiang Hua

E-Mail Website
Guest Editor
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: information geometry; Riemannian geometry; radar/sonar signal processing; underwater environmental modelling; image processing; machine learning; target detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In an era marked by an unprecedented pace of development in intelligent processing methods, its applications have permeated and transformed diverse fields including as healthcare, education, sports, and biology. This Special Issue on "Intelligent Information Processing Methods in Interdisciplinary" serves as an open and inclusive platform for researchers and practitioners to delve into the transformative potential of intelligent processing methods across these domains. This curated collection spotlights cutting-edge developments in AI-powered medical innovations, intelligent educational frameworks, data-driven sports analytics, and next-generation biotechnological solutions. By examining the integration of machine learning, deep learning, and other AI methodologies, the Special Issue not only showcases technical advancements but also emphasizes ethical considerations and real-world impact. The collection of peer-reviewed articles aspires to foster meaningful interdisciplinary dialog, bridge theoretical and applied research, and accelerate the translation of intelligent processing method innovations into actionable strategies that address complex, multifaceted challenges in our increasingly interconnected world.

This Special Issue focuses on recent advances in intelligent processing methods in interdisciplinary research, including a wide range of new processing techniques and experimental advances. Topics of interest include, but are not limited to, the following:

  • AI-driven medical technologies;
  • AI-driven biomedical signal processing;
  • AI-driven sensor signal processing;
  • AI-driven methods in sport and education;
  • Machine learning methods and their applications in interdisciplinary research;
  • Large language models and their applications in interdisciplinary research;
  • Few-shot learning and its applications in interdisciplinary research;
  • Federated learning and its applications in interdisciplinary research.

Dr. Xiaoqiang Hua
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Algorithms is an international peer-reviewed open access monthly 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 1800 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

  • AI-driven medical technologies
  • AI-driven biomedical signal processing
  • AI-driven sensor signal processing
  • AI-driven methods in sport and education
  • machine learning methods and their applications in interdisciplinary research
  • large language models and their applications in interdisciplinary research
  • few-shot learning and its applications in interdisciplinary research
  • federated learning and its applications in interdisciplinary research

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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

26 pages, 1313 KB  
Article
CausalAgent: A Hierarchical Graph-Enhanced Multi-Agent Framework for Causal Question Answering in Production Safety Accident Reports
by Tianyi Wang, Tao Shen, Zhiyuan Zhang, Shuangping Huang, Huiguo He, Qingguang Chen and Houqiang Yang
Algorithms 2026, 19(5), 355; https://doi.org/10.3390/a19050355 - 2 May 2026
Viewed by 92
Abstract
Accident reports provide a detailed account of environmental causes, unsafe human behaviors, and subsequent chain reactions. These records serve as essential resources for analyzing accident mechanisms and exploring potential risk patterns within production safety processes. Currently, Graph based Retrieval-Augmented Generation (RAG), which integrates [...] Read more.
Accident reports provide a detailed account of environmental causes, unsafe human behaviors, and subsequent chain reactions. These records serve as essential resources for analyzing accident mechanisms and exploring potential risk patterns within production safety processes. Currently, Graph based Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with Knowledge Graphs (KGs), has emerged as a leading approach for complex causal question answering over extensive unstructured accident documentation. However, the application of this technology in the production safety domain still encounters two primary challenges. First, knowledge graph construction using a single granularity fails to capture fine-grained case details and macro-level standard systems. Second, traditional one-step retrieval paradigms lack the capacity to track deep causal chains or interpret the complex logic of multi-factor coupling. To address these limitations, we propose CausalAgent, a hierarchical graph-enhanced multi-agent framework for causal question answering in production safety accident reports. This framework innovatively combines a Hierarchical Causal Graph (HC-Graph) and a Multi-Agent Collaborative Reasoning (MACR) mechanism. Specifically, the HC-Graph employs a two-layer architecture that links a fine-grained instance layer with a national standard causation layer to resolve conflicts in semantic granularity. The MACR mechanism converts complex natural language queries into executable structured queries and logic verification steps through the sequential cooperation of four specialized agents, namely the Graph Parsing Agent, the Problem Analysis Agent, the Query Generation Agent, and the Reasoning Insight Agent. CausalAgent enables in-depth mining of accident causation mechanisms and provides scientific, robust and interpretable intelligent support for data-driven risk assessment and emergency decision-making. Experiments on real-world accident datasets demonstrate that CausalAgent achieves a 100.0% query execution rate and an 87.3% reasoning accuracy, outperforming the SOTA baseline by 45.2% in terms of absolute accuracy. Full article
(This article belongs to the Special Issue Intelligent Information Processing Methods in Interdisciplinary)
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