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Call For Papers

The ICMLDA bridges the gap between academia and industry by promoting research with practical applications. It provides a platform for professionals and researchers to share insights that drive real-world impact.

The conference focuses on Artificial Intelligence, Data Science, Machine Learning, encouraging applied research, case studies, and industry-driven innovations.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Machine learning applications in healthcare
  • Data analytics for clinical decision making
  • Predictive modeling for patient outcomes
  • AI in health informatics and data management
  • Ethical considerations in data analytics
  • Natural language processing for health data
  • Real-time analytics in patient monitoring
  • Machine learning for chronic disease prediction
  • Data visualization techniques in healthcare
  • AI-driven insights for population health
  • Impact of big data on healthcare delivery
  • Collaborative tools for data scientists
  • Machine learning for personalized treatment plans
  • Data privacy and security in healthcare
  • Future trends in machine learning and analytics
  • AI applications in telemedicine
  • Healthcare applications of deep learning
  • Data integration challenges in healthcare systems
  • Machine learning for health equity research
  • Interdisciplinary approaches to data science

Evaluation

Submissions will be evaluated based on applicability, innovation, and research contribution. Accepted papers will be presented and considered for publication in relevant journals and proceedings.

Registration

Complete your registration to participate in discussions that bridge academia and industry, and gain exposure to practical insights.

Publication

Selected papers will be considered for publication platforms that support academic and industry collaboration.

Submit Your Paper Now Confirm Your Participation Today
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Conference Alert

Due to heightened regional tensions and travel risks, the conference may be conducted in virtual-only mode. Updates regarding participation format will be communicated in advance.