The International Conference on AI, Data Science, and Intelligent Systems

✓ Indexed by Springer Seville • Nov 30, 2026

Uniting Global
Intelligence for
Science.

The leading international summit on AI, Data Science, and Intelligent Systems. All accepted papers will be published in the prestigious Springer Book Series.

Official Announcement

Call for Papers
AIDSI 2026

Advancing Research, Innovation, and Applications.

The International Conference on AI, Data Science, and Intelligent Systems (AIDSI) invites researchers, academics, industry professionals, and practitioners to submit original and high-quality research papers. The conference aims to provide a global platform for sharing innovative ideas, recent advances, and practical applications in Artificial Intelligence, Data Science, and Intelligent Systems.

🎯 Scope of the Conference

The conference covers a broad spectrum of topics, encouraging interdisciplinary research and collaboration across academia and industry. Contributions may include theoretical developments, methodological advancements, and real-world applications.

📝 Submission Guidelines

PAPERS MUST PRESENT ORIGINAL AND UNPUBLISHED RESEARCH
DOUBLE-BLIND PEER REVIEW PROCESS
INCLUSION IN SPRINGER CONFERENCE PROCEEDINGS
FOLLOW CONFERENCE FORMATTING TEMPLATE

📅 Important Dates

Paper Submission Deadline 15/10/2026
Notification of Acceptance 01/11/2026
Camera-Ready Papers 15/11/2026
Conference Dates 30/11/2026

🌍 Why Participate?

  • SHARE RESEARCH WITH AN INTERNATIONAL AUDIENCE
  • ENGAGE WITH LEADING EXPERTS
  • BUILD COLLABORATIONS AND RESEARCH NETWORKS
  • STAY UPDATED ON EMERGING TRENDS
Scientific Catalogue

Explore the Research Axes

Multidisciplinary tracks categorized into 11 master domains for high-precision scientific exchange.

📊

AI & Data Science in Finance, Economics, and Business[cite: 15]

  • Algorithmic Trading & Quantitative Finance[cite: 16]
  • Financial Forecasting & Risk Modeling[cite: 17]
  • Fraud Detection & Anti-Money Laundering[cite: 18]
  • Credit Scoring & Financial Decision Systems[cite: 19]
  • AI in Banking, Insurance & FinTech[cite: 20]
  • Economic Modeling & Computational Economics[cite: 21]
  • Market Microstructure & Behavioral Finance[cite: 22]
  • AI for Macroeconomic & Policy Analysis[cite: 23]
🏥

AI & Data Science in Healthcare & Medicine[cite: 24]

  • Medical Imaging & Diagnostic Systems[cite: 25]
  • Clinical Decision Support & Health Informatics[cite: 26]
  • Predictive Analytics for Healthcare Systems[cite: 27]
  • Personalized & Precision Medicine[cite: 28]
  • AI for Drug Discovery & Development[cite: 29]
  • Electronic Health Records (EHR) Analytics[cite: 30]
  • AI in Epidemiology & Public Health[cite: 31]
  • Wearable & Sensor Data Analytics[cite: 32]
📈

AI & Data Science in Statistics & Mathematics[cite: 33]

  • Statistical Learning & Inference[cite: 34]
  • Bayesian Methods & Probabilistic Modeling[cite: 35]
  • Causal Inference & Experimental Design[cite: 36]
  • Uncertainty Quantification & Risk Analysis[cite: 37]
  • High-Dimensional Statistics[cite: 38]
  • Optimization Methods for AI[cite: 39]
  • Monte Carlo & Simulation-Based Methods[cite: 40]
🤖

AI & Data Science in Robotics & Autonomous Systems[cite: 41]

  • Intelligent Robotics & Control Systems[cite: 42]
  • Autonomous Vehicles & Navigation[cite: 43]
  • Human-Robot Interaction[cite: 44]
  • Swarm Robotics & Collective Intelligence[cite: 45]
  • Learning-Based Control & Planning[cite: 46]
  • AI for Drones & Unmanned Systems[cite: 47]
🧬

AI & Data Science in Biology & Life Sciences[cite: 48]

  • Bioinformatics & Computational Biology[cite: 49]
  • Genomics, Proteomics & Multi-Omics Analysis[cite: 50]
  • Systems Biology & Network Modeling[cite: 51]
  • AI for Protein Structure & Function Prediction[cite: 52]
  • AI in Biomedical Signal Processing[cite: 53]
  • Synthetic Biology & Bio-Inspired Intelligence[cite: 54]
⚙️

AI & Data Science in Engineering & Physical Sciences[cite: 55]

  • AI for Materials Science & Discovery[cite: 56]
  • AI in Mechanical, Electrical & Civil Engineering[cite: 57]
  • Physics-Informed Machine Learning[cite: 58]
  • AI for Simulation, Modeling & Digital Twins[cite: 59]
  • Smart Sensors & Cyber-Physical Systems[cite: 60]
🧠

AI & Data Science in Social Sciences & Humanities[cite: 61]

  • Computational Social Science[cite: 62]
  • AI for Economics, Sociology & Political Science[cite: 63]
  • Natural Language Analysis for Social Data[cite: 64]
  • Misinformation, Opinion Mining & Social Media Analytics[cite: 65]
  • AI for Digital Humanities[cite: 66]
🌱

AI & Data Science in Environment, Energy & Sustainability[cite: 67]

  • Climate Modeling & Environmental Analytics[cite: 68]
  • AI for Renewable Energy Systems[cite: 69]
  • Smart Grids & Energy Forecasting[cite: 70]
  • AI for Disaster Prediction & Risk Management[cite: 71]
  • AI for Sustainable Development Goals (SDGs)[cite: 72]
🏭

AI & Data Science in Industry & Real-World Systems[cite: 73]

  • Predictive Maintenance & Industrial Analytics[cite: 74]
  • Supply Chain & Logistics Optimization[cite: 75]
  • Smart Manufacturing & Industry 4.0[cite: 76]
  • AI for Quality Control & Process Optimization[cite: 77]
⚖️

AI & Data Science in Security, Law & Governance[cite: 78]

  • AI for Cybersecurity & Threat Detection[cite: 79]
  • AI in Law, Regulation & Compliance[cite: 80]
  • Trustworthy AI in Critical Applications[cite: 81]
  • AI for Public Administration & Smart Governance[cite: 82]
🚀

Cross-Cutting & Emerging Applications (High Growth)[cite: 83]

  • Digital Twins for Complex Systems[cite: 84]
  • AI for Scientific Discovery & Research Automation[cite: 85]
  • AI for Space, Astronomy & Astrophysics[cite: 86]
  • AI + IoT Applications[cite: 87]
  • Multimodal & Human-Centered AI Applications[cite: 88]
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