Behavioural Modelling for Complex Systems: AI-Driven Evidence Generation from Heterogeneous Data
DETAILS
Call for Papers
Behavioural Modelling for Complex Systems: AI-Driven Evidence Generation from Heterogeneous Data
Journal: Applied Artificial Intelligence
Publisher: Taylor & Francis Group
Article Collection
Manuscript Submission Deadline: 31 March 2027
Open Access: This Article Collection will be published Open Access.
Applied Artificial Intelligence invites submissions for an Article Collection exploring AI-driven behavioural modelling in complex systems, with particular emphasis on transforming heterogeneous, multimodal, fragmented, and partially observed data into meaningful evidence for real-world decision-making.
About the Article Collection
Understanding behaviour in complex environments often requires more than a single dataset or data modality. Interactions among humans, technologies, environments, and socio-technical systems generate diverse forms of information that conventional predictive approaches may struggle to integrate effectively.
This Article Collection seeks innovative AI methods capable of combining structured and unstructured information to identify behavioural patterns, relationships, and decision-relevant evidence. Particular attention is given to multimodal AI, Generative AI, Large Language Models (LLMs), knowledge representation, graph-based AI, data integration, and intelligent decision-support systems.
Topics of Interest
Submissions may address, but are not limited to:
Behavioural modelling using heterogeneous data
Behavioural modelling using multimodal data
AI-driven evidence generation
Complex systems and behavioural analysis
Multimodal reasoning
Representation learning
Generative AI for behavioural analysis
Large Language Models (LLMs)
Graph-based and relational AI
Behavioural modelling in socio-technical systems
Cyber-physical systems
Hybrid human-system interactions
Explainable AI
Trustworthy AI
Knowledge graphs
Retrieval-Augmented Generation and retrieval-augmented systems
Synthetic behavioural evidence
Inferred behavioural evidence generation
AI-enabled decision-support systems
AI in data-incomplete environments
Machine learning for behavioural inference
Natural language and image processing
Knowledge representation and reasoning
Real-world AI deployment
Application Areas
Interdisciplinary research is particularly encouraged across areas such as:
Healthcare and health sciences
Sustainability
Environmental studies
Public policy
Psychology
Behavioural science
Sociology
Digital systems
Smart environments
Digital governance
Digital humanities
Human-computer interaction
Societal decision-making
Preferred Article Types
The Article Collection welcomes:
Original Research Articles
Review Papers
Methodological Studies
Application-Focused Contributions
Interdisciplinary Perspectives
Contributions presenting theoretical advances, interdisciplinary applications, and real-world deployments are particularly encouraged.
Keywords
Behaviour Modelling
Complex Systems and Decision Support
Heterogeneous and Multimodal Data
Generative AI and LLM
AI-Driven Evidence Generation
Submission Information
Manuscript Submission Deadline: 31 March 2027
All submissions will undergo the journal's standard desk assessment and peer-review process. Manuscripts outside the scope of Applied Artificial Intelligence may be rejected at the initial assessment stage.
The journal applies the Taylor & Francis Open Data Sharing Policy. Authors are required to make the data and materials supporting the results or analyses presented in their manuscripts freely available.
During submission, authors should:
Select “Yes” when asked whether the manuscript is for a specific Special Issue or Article Collection.
Select “Behavioural Modelling for Complex Systems: AI-Driven Evidence Generation from Heterogeneous Data” from the Article Collection menu.
Enter any applicable discount code on the final Services page during submission.
Submit an Article to Applied Artificial Intelligence
Instructions for Authors
Applied Artificial Intelligence – Journal Page
Open Access Discount Information
The Article Collection offers:
10% discount for all authors
A limited number of 20% discount codes for early submissions
Discount codes must be entered at the time of submission
Discounts cannot be applied retrospectively or combined; where applicable, only the higher discount will apply
For discount codes and Article Collection queries:
Christopher Montgomery
Commissioning Editor
Email: christopher.montgomery@tandf.co.uk
Article Collection Guest Advisors
Dr. Nonso Nnamoko
Edge Hill University, United Kingdom
Research interests include multimodal AI, heterogeneous data integration, machine learning, natural language and image processing, Generative AI, knowledge representation, behavioural inference, ethical AI, and AI-driven decision support.
Email: nnamokon@edgehill.ac.uk
Prof. Yannis Korkontzelos
Edge Hill University, United Kingdom
Research interests include Natural Language Processing, text mining, compositional semantics, term extraction, document classification, sentiment analysis, biomedical applications, social media, and scientific publications.
Email: korkonty@edgehill.ac.uk
Dr. Asfak Ali
Deep Duo Foundation, India
Research interests include computer vision, deep learning, intelligent embedded systems, medical imaging, biomedical signal processing, image restoration, video analytics, Edge AI, and IoT-enabled intelligent systems.
Email: asfakali@deepduofoundation.com
Posted on ServiceSetu Academics — Premier Platform for Academic Opportunities & Research Collaboration.
COMMENTS (0)
Sign in to join the conversation
SIGN IN TO COMMENT