Journal of Artificial Intelligence and Deep Learning

 

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Artificial Intelligence means ‘making computational models of human behavior’. It is the study of how computer systems can simulate intelligent processes such as learning, reasoning, and understanding symbolic information in context. Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. Deep learning is used in the research community and in the industry to help solve many big data problems such as computer vision, speech recognition, and natural language processing.

Journal of Artificial Intelligence and Deep learning’  acts as a medium for scientists and professionals for sharing their research results and report new advances and applications in artificial intelligence and deep learning in various fields such as  medical diagnosis, industries, telecommunications, gaming, stock trading, robot control, law, remote sensing and scientific discovery.

We welcome scholars across the globe to submit their original research papers fulfilling the requisite criterion. The journal also accepts review, short communication, editorial, expert review and letter to the editor. All the manuscripts published by ‘Journal of Artificial Intelligence and Deep Learning’ undergo rapid, quality and quick  review processing by eminent editorial and review team maintaining high standards and ethics of publishing.

The scholarly content published online will be freely available to every reader anywhere in the world to read, download, copy, reuse and distribute, provided that the original work is properly cited.

Authors may submit their valuable work either via the online submission form or via email to the editor’s office at submissions@asterpublications.com

Scope of the journal

Journal of Artificial Intelligence and Deep learning’ covers the following topics (including but not limited to the following fields).

  • Automated Reasoning
  • Knowledge representation
  • Automated planning and scheduling
  • Machine learning
  • Natural language processing
  • Computer vision
  • Robotics
  • General intelligence, or strong AI
  • Expert systems
  • Neural networks
  • Fuzzy logic systems
  • Data Mining
  • Game theory and Strategic planning.
  • Constraint processing
  • Heuristic search
  • Intelligent interfaces
  • Cognitive aspects of AI
  • Intelligent robotics
  • Multiagent systems
  • Image and speech recognition