Call For Papers

Prospective authors are invited to submit manuscripts reporting original unpublished research and recent developments in the topics related to the conference.

The Springer template in Microsoft Word (US Letter) Click Here to Download

LaTeX format can be found at : Click Here to Download

Details can also be found in the Call for Papers : Click Here

GCAIA 2020 Paper Categories

  • Regular Paper – 10 pages maximum (3 additional pages allowed but at an extra charge)
  • Short Paper (Work-in-Progress) – 8 pages maximum (2 additional pages allowed but at an extra charge)

Regular papers should present novel perspectives within the general scope of the conference. Short papers (Work-in-Progress) are an opportunity to present preliminary or interim results. Posters are intended for ongoing research projects, concrete realizations, or industrial applications/projects presentations.

The Extended version of the few selected paper will be reviewed for potential acceptance and publication in the special issue of the following Journals:-

Topics and technical areas of interest include but are not limited to the following:

Artificial Intelligence Machine Vision Robotics Ambient Intelligence
Learning Techniques Human Computer Interaction Humanoid Robots Smart Cities
Neural Networks Pattern Recognition Space and underwater robots Internet of Things
Soft Computing Image/Video Processing Assistive Robots Ambient Assisted Living

 

Expert Systems Intrusion Detection Mobile Robots Smart Healthcare
Computational Intelligence Brain-Machine Interface Autonomous Robots

 

Intelligent Transportation
Natural Language Processing Geographic Information Systems Human-Robot Interaction Data Science
Data Mining Signal Processing Telerobotics Particle Swarm Optimization
Neuromorphic systems Medical Diagnosis Walking and Climbing Robots Neural Network Theory & Architecture
Biometrics Segmentation Techniques Robotic Automation Collective Intelligence
Sentiment Analysis Augmented/Virtual Reality Robot Localization and Map Building Rough Set and Rough Data Analysis
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