Dr. Javad Hassannataj Joloudari Website E-Mail: javad.hassannatajjoloudari@iau.ac.ir
Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran
Interests: Explainable artificial intelligence; machine learning; deep learning; data mining; healthcare informatics; image processing
Dr. Ali Abbaszadeh Sori Website E-Mail: abbaszadehsori@iau.ac.ir
Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran
Interests: Artificial intelligence; machine learning; deep learning; image processing; electronic commerce; fuzzy systems
Dr. Danial Sharifrazi Website E-Mail: s222619018@deakin.edu.au
Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
Interests: Artificial intelligence; data science; machine learning; deep learning; image processing
Dr. Namal Rathnayake Website E-Mail: namalr@jamstec.go.jp; namalhappy@gmail.com
Marine-Earth System Analytics Unit, Yokohama Institute for Earth Sciences (YES), Jamstec, Japan
Interests: Prediction and forecasting; rainfall runoff; tropical cyclones; soft computing; data assimilation; optimization; robotics; artificial intelligence; machine learning; fuzzy logic
Special Issue Information
The accelerating advancement of Artificial Intelligence (AI) is fundamentally reshaping how knowledge is generated, integrated, and applied across disciplines. As global challenges grow in complexity—from climate resilience and healthcare to autonomous systems and smart infrastructure—the need for interdisciplinary approaches is no longer optional. AI stands at the core of this transformation, enabling the convergence of scientific, engineering, social, and computational domains into cohesive, innovative systems.
This Special Issue focuses on AI-Driven Interdisciplinary Integration and Innovation, highlighting how AI catalyzes novel research paradigms through the fusion of traditionally siloed disciplines. By leveraging machine learning, neural networks, natural language processing, reinforcement learning, and symbolic reasoning, researchers are now embedding intelligence directly into diverse domains such as medicine, materials science, environmental monitoring, economics, and design.
A central goal of this issue is to showcase how AI facilitates not just technical advancement, but also epistemological shifts—redefining how problems are framed, how data is interpreted, and how knowledge is synthesized across fields. We are particularly interested in works that demonstrate practical implementations, theoretical frameworks, and system architectures that make interdisciplinary AI integration both scalable and sustainable.
We invite high-quality, original research articles, case studies, and critical reviews that address the following topics (but are not limited to):
- AI-enabled frameworks for interdisciplinary research and applications
- Human–AI collaboration in scientific discovery
- AI in socio-technical systems, ethics, and policy integration
- Cross-domain data fusion and modeling techniques
- Interoperability and knowledge representation in AI systems
- Domain-specific applications of AI in healthcare, climate science, energy, and beyond
- Challenges in validation, trust, and transparency across disciplines
- Emerging methodologies for AI-supported innovation ecosystems
We welcome contributions that provide insights into how AI can act not only as a tool, but as a transformative medium for interdisciplinary knowledge creation and application.
Keywords:
Artificial intelligence (AI); cross-disciplinary research; transdisciplinary methodology; interdisciplinary AI; multimodal data fusion; hybrid intelligence; explainable and trustworthy AI; AI-enabled digital twins; cross-domain collaboration; AI for sustainability; AI-driven decision support; human–AI co-design; open and reproducible AI; interoperability; innovation ecosystems; machine learning; knowledge fusion; AI applications; data-driven discovery; computational intelligence; intelligent systems; AI in science and engineering
Published Papers:
