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ML4Educaion 2021 : Special Issue on Machine Learning methods for Cloud-based IoT applications in Intelligent E-learning and Educational systems

Submission Deadline: Dec 20, 2020

About

Today, engineering education organizations have been addressing new professional challenges, guided by general concerns, such as teamwork abilities, argumentation and persuasion abilities in multiple social contexts, creativity, complexity handling, and leadership or strong work ethics. The intelligent services have become a novel topic for interesting researchers and developers in academic area e-learning and engineering education. It is foreseeable that smart devices are deployed at the cloud-based services. The development of the educational system is fundamental for sustainable technological change using the smart devices and Internet of Things (IoT) applications. In other hand, machine learning methods can apply and improve the teaching practices in e-learning and education through many ways and that is the main reason that we need to further examine the involvement of intelligence techniques for education procedures in IoT environment. This special issue will be developing methods and applications of intelligent services for e-learning and education in some new challenges of cloud-based IoT applications. Also, This Special Issue focuses on machine learning methods in e-learning and education, regarding any type of cloud-based IoT applications to evaluation of healthcare education, education in smart city, e-learning and technical concepts of educational environments in IoT. This special issue invites researchers to publish selected original articles presenting intelligent trends and systems to solve new challenges of engineering education and learning problems.

Call For Paper

Topics are as below but are not limited to:

  • Smart teaching in IoT platform
  • Evaluating IoT smart devices in engineering education
  • Intelligent services in e-learning and education
  • Mobile assessing for students in engineering education
  • Cloud-based architectures for e-learning and education in IoT
  • Data mining methods for educational management in IoT
  • Machine learning methods for public healthcare systems in IoT
  • Deep learning on industrial equipment in IoT
  • Intelligent evaluations on virtual reality in teaching environments
  • Data mining on learning-assisted environments in IoT
  • Machine learning method for educational healthcare systems in IoT
  • Knowledge-based system for evaluating educational IoT environments
  • Image processing and pattern recognition for educational IoT environments
  • Machine learning on customer relationship management in IoT environments
  • Soft computing techniques for educational IoT environments
  • Educational Big Data analytics for IoT systems
  • New innovations of educational systems for smart city in IoT
  • Fuzzy logic and methods for learning assisted systems in IoT applications
  • Security and privacy for e-learning and educational systems in IoT applications

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