Applied Artificial Intelligence Approach: Intelligent Data Processing and Mining with Online Behaviors

Dear Colleagues,

The COVID-19 outbreak has profoundly changed the way people work, live, and study in recent years. To maintain social distancing, a great deal of remote technology is used as a productivity tool. Large-scale offline activities are rapidly being transformed into online or hybrid events, which introduces opportunities as well as challenges.

From the point of view of information transmission, online activities have greatly extended the distance of communication while inevitably losing information of various modes. That is, although visual, auditory and other information can be transmitted in collaboration, there are still studies showing that a lot of potential perceptual information has not been well encoded in online platforms. Furthermore, the cognitive process of traditional interactions has also been changed in that online users acquire knowledge in a more personalized, customized, and behavioral approach. However, due to the lack of learning paradigms in these new situations, current models and platforms generally face difficulties such as cognitive trek and high dropout rates. Therefore, it is urgent to invent more comprehensive, personalized, and explainable AI methods to re-understand the patterns and cognitive processes of user profiling after the change.

In recent years, the advance of deep representation learning and other AI methods has introduced a new angle to this problem in many respects, such as graph neural networks, pretrained language and multimodal models, deep reinforcement learning, etc. In addition to the incredible achievements that have been made, scientists are still working towards revealing the essence of different learning process. From the perspective of AI, this intelligence-based approach provides the potential to make AI-based systems not only intelligent but also knowledgeable, which is also crucial in explainable artificial intelligence (XAI). Furthermore, machine intelligence-based models also help to recognize the psychological and cognitive patterns of users in their interaction process. We believe that fundamental innovation via the use of AI could immensely promote the understanding of user behavior, emotion, attention, and knowledge acquisition. Thus, we hope to invite submissions of research articles that focus on intelligent data processing and mining with online behaviors from the view of AI and machine learning.

This proposed Special Issue has the following sub-topics:

• AI theory for behavioral analysis technology:
  • Graph and network theory for the learning process;
  • New representation learning paradigms;
  • Explainable AI and its mechanism;
  • Contrastive analysis between machine learning and human learning;
  • Machine intelligence-driven psychological and cognitive pattern recognition.
• Intelligence driven methods for online behavioral data mining:
  • Computer version-based user attention and emotion tracing;
  • Multi-modal, multi-sourced heterogeneous information fusion;
  • Intelligence-guided reasoning and searching of knowledge concepts;
  • AI-powered learning object generation;
  • Recommendation and retrieval for online objects;
  • Knowledge-tracing techniques;
  • Neural network structures for application;
  • Explainable AI for user profiling;
• Applications with intelligence-driven technology:
  • Domain- and subject knowledge-specific application;
  • The use pf intelligent tools for large-scale application;
  • New human–computer interaction approaches;
  • Art driven by AI for intelligent simulation and digital creativity.
• Surveys on the above topics.

Prof. Dr. Haihong E
Dr. Yifan Zhu
Dr. Qika Lin
Dr. Kaize Shi
Guest Editors

Manuscript Submission Information

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• online mining
• behavior mining
• intelligent information processing