Applications of Machine Learning

Chair: Dr. Daniel Eduardo Hernández Morales

Machine Learning has become an extremely popular approach for solving complex problems in different domains. The ability to process large amounts of data and extract meaningful insight to create predictive models, in order to enhance decision-making and optimize processes, has revolutionized industries such as healthcare, finance, retail, manufacturing, transportation, and more. This session aims to bring together researchers, practitioners, and industry experts to discuss and showcase the latest advancements in the development of new algorithms or improvements over existing ones, and innovative applications of Machine Learning in various fields.

 

Topics of interest include (but are not limited to):

  • Real-world application:
    • Social challenges and issues
    • Manufacturing and Industry 4.0
    • Energy and Sustainability
    • Healthcare and Medical Diagnosis
    • Finance and Economics
    • Retail and E-commerce
    • Transportation and Autonomous Systems
    • Natural Language Processing and Text Mining
    • Computer Vision and Image Processing
    • Social Media and Web Analytics
    • Process optimization
  • Theoretical developments of ML algorithms
  • Evolutionary machine learning
  • Optimization of machine learning algorithms
  • Search-based software engineering
  • Hybrid models
  • Ensemble models
  • Reinforcement learning, Transfer learning and Deep learning
  • Interpretability of machine learning models
  • Leaning with unbalanced or missing data
  • Feature extraction, reduction and selection

 

Part of the special session will be the workshop "Workshop on Generative Artificial Intelligence with AWS" in cooperation with Amazon Web Services (AWS).

Contact: Dr. Daniel Eduardo Hernández Morales daniel.hernandezm @ tectijuana.edu.mx

 

 

 

Daniel Hernández is a professor at the Tecnológico Nacional de México/ IT de Tijuana, in Tijuana, BC, Mexico. His research interests include several data science and artificial intelligence topics such as: machine learning, feature engineering, evolutionary computation and computer vision. He received his Ph.D. in Computer Science the from Centro de Investigación Científica y de Educación Superior de Ensenada, B.C., (CICESE), México. He is a member of the National Network of Researchers (SNI Level I)