Special Session: Emerging Machine Learning and Deep Learning Models: Theory and Applications


Jungpil SHIN (Univ. of Aizu, Japan)

Among the research community recently, there has been an increasing interest in using machine learning (ML) and deep learning (DL) methods to solve many real-world problems in different fields. Advances in machine learning always provide new challenges and solutions to various complex problems encountered in applications, technologies, and theories. Deep learning is part of a broader family of machine learning methods that is based on learning hierarchical representation of input data by using multiple non-linear layers. Over the last few years, DL techniques have found widespread applications and implementations in several fields and have been extensively used by experts and researchers to achieve better results. This special session aims to attract researchers and developers working in machine learning and deep learning to publish original and innovative ideas. Both theoretical contributions and exciting applications for handling various types of complex data using machine and deep learning algorithms will be entertained. Potential topics include, but are not limited to, the following:

  • Machine learning models
  • Deep learning (CNN, RNN, etc.) and its applications
  • Feature extraction and selection
  • Image analysis (Segmentation, Classification, Retrieval, Generation)
  • Human action/activity recognition
  • Gesture analysis and recognition
  • Intelligent interfaces (User-friendly Man Machine Interface)
  • Sensor-based activity recognition and interaction
  • Handwriting-based activity analysis
  • Applications in healthcare, bioinformatics, computer vision.

Submission System

Submit your paper online via EDAS:


Please read the submission instructions.