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Special Session 2: Machine Learning and Deep Learning Models
 

This Special Session aims to explore recent advances, applications, and challenges in the development and deployment of machine learning (ML) and deep learning (DL) models across multidisciplinary domains. The session invites contributions that address both theoretical foundations and real-world implementations of supervised, unsupervised, and reinforcement learning algorithms. Special attention will be given to deep learning architecture such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and hybrid models.

 

Organizer: Dr. Christian Ovalle, Universidad Tecnologica del Perú, Perú

 

The topics of interest include, but are not limited to:

  • Supervised, unsupervised, and reinforcement learning algorithms
  • Deep learning architectures (CNNs, RNNs, LSTM, Transformers, etc.)
  • Explainable AI and interpretable machine learning models
  • Transfer learning and domain adaptation
  • Federated and distributed learning
  • Time-series forecasting and sequential data modeling
  • Computer vision and image recognition
  • Natural language processing and generative models
  • Data augmentation and synthetic data generation
  • ML/DL applications in healthcare, agriculture, finance, and education
  • Smart sensors and edge AI integration
  • Model optimization and efficiency (quantization, pruning, etc.)
  • Multimodal learning and data fusion
  • AI fairness, ethics, and bias mitigation
  • Benchmarking, performance evaluation, and real-world deployment

    Submission Guideline

    Please submit your manuscript via Electronic Submission System (account is needed). (Please remark the session number when you make the submission.)

     

    Important Dates

    • Submission of Full Papers: June 05, 2025
    • Notification of Review Result of Papers from Special Sessions: July 25, 2025
    • Registration Deadline: August 25, 2025