Research Area

Computational Discovery and Intelligent Systems (CDIS)

Publisher: Scientific Innovation Research Group (SIRG)

Computational Discovery and Intelligent Systems (CDIS) publishes high-quality research that advances theory, methodology, and applications in computational intelligence, intelligent systems, and data-driven scientific discovery. The journal welcomes interdisciplinary contributions that integrate artificial intelligence, machine learning, mathematical modeling, and computational optimization with real-world scientific, industrial, and engineering challenges.

CDIS particularly encourages submissions in the following research areas:

1. Artificial Intelligence & Machine Learning

  • Deep learning architectures

  • Graph neural networks and graph computation

  • Reinforcement learning and decision-making systems

  • Generative AI and foundation models

  • Explainable and interpretable AI

  • Machine learning for small, sparse, or complex datasets

  • AI reliability, robustness, and model verification

2. Data Science, Analytics & Knowledge Discovery

  • Predictive analytics and pattern recognition

  • High-dimensional data analysis

  • Feature engineering and dimensionality reduction

  • Data fusion, integration, and preprocessing techniques

  • Automated machine learning (AutoML)

  • Uncertainty quantification and probabilistic modeling

3. Computational Modeling & Simulation

  • Scientific computing and numerical modeling

  • Multi-scale and multi-physics simulations

  • Computational chemistry and computational biology

  • Simulation-based optimization

  • Digital twins and virtual system modeling

4. Intelligent Systems & Autonomous Technologies

  • Intelligent control and adaptive systems

  • Autonomous robots, vehicles, and agents

  • Smart manufacturing and Industry 4.0 technologies

  • Human–machine interaction and intelligent interfaces

  • Cognitive systems and knowledge-based systems

5. Optimization, Operations Research & Decision Systems

  • Metaheuristics and evolutionary algorithms

  • Mathematical optimization and convex/non-convex methods

  • Multi-objective optimization

  • Scheduling, resource allocation, and logistics systems

  • Decision-support systems for complex environments

6. Computational Discovery in Physical, Biological, and Environmental Sciences

  • Drug discovery and computational pharmacology

  • Bioinformatics and health informatics

  • Materials discovery using AI and ML

  • Climate modeling and environmental analytics

  • Remote sensing, satellite data analysis, and geospatial computation

 

7. High-Performance Computing & Scalable AI

  • GPU/TPU-based acceleration

  • Distributed and parallel computing

  • Edge and fog computing for intelligent applications

  • Optimization of large-scale AI workloads

8. Security, Privacy & Trust in Intelligent Systems

  • AI for cybersecurity

  • Secure computation and privacy-preserving modeling

  • Blockchain for intelligent systems

  • Robustness against adversarial attacks

9. Interdisciplinary & Emerging Areas

  • AI for healthcare, education, economics, and social systems

  • Computational law, ethics, and responsible AI

  • Smart cities and intelligent infrastructure

  • Quantum machine learning and quantum optimization

    10. Cybersecurity, Cryptography & Intelligent Security Systems

    • Quantum cryptography and post-quantum cryptographic systems

    • AI-enhanced cryptographic protocols and secure communication

    • Intelligent threat detection and cyber defense systems

    • Privacy-preserving computation and secure data analytics

    • Machine learning for security, forensics, and intrusion detection

    11. Emerging Intelligent Computing Paradigms

    • Quantum computing and quantum-inspired algorithms

    • AI for quantum systems and hybrid quantum–classical models

    • Computational intelligence for next-generation communication systems

    • Trustworthy, ethical, and reliable intelligent systems