Important Dates
Early Bird Registration :18th May 2027
Paper Submission Deadline :23rd May 2027
Last Date of Registration : 2nd June 2027
Date of Conference : 17th - 18th June 2027
Conference Session Tracks
Aligned with
UN Sustainable Development Goals
This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals.
SDG 3 — Good Health and Well-being
SDG 4 — Quality Education
SDG 9 — Industry, Innovation and Infrastructure
SDG 10 — Reduced Inequalities
SDG 11 — Sustainable Cities and Communities
Track 01
Neurocybernetics and Health Applications
This track focuses on the intersection of neurocybernetics and health, exploring innovative applications in medical diagnostics and treatment. Researchers are invited to present their findings on how neurocybernetic systems can enhance patient care and rehabilitation.
Track 02
Advancements in Neuroevolutionary Algorithms
This session will delve into the latest advancements in neuroevolutionary algorithms and their applications in solving complex problems. Contributions that demonstrate the efficacy of these algorithms in real-world scenarios are particularly encouraged.
Track 03
Neuroscience-Inspired Robotics
This track aims to explore the integration of neuroscience principles into robotic systems, focusing on how biological insights can inform robotic design and functionality. Papers that discuss the implications of neural mechanisms for robotic learning and adaptation are welcome.
Track 04
Computational Intelligence in Neurology
This session will highlight the role of computational intelligence techniques in advancing neurological research and treatment. Submissions that showcase novel applications of machine learning and artificial intelligence in neurology are encouraged.
Track 05
Evolutionary Robotics: Design and Implementation
This track invites contributions on the design and implementation of evolutionary robotics, emphasizing the methodologies and technologies that enable autonomous robotic systems. Papers that present case studies or experimental results are particularly sought after.
Track 06
Artificial Neural Networks in Medical Diagnostics
This session will focus on the application of artificial neural networks in the field of medical diagnostics, exploring their potential to improve accuracy and efficiency. Researchers are encouraged to share their findings on network architectures and training methodologies.
Track 07
Robotic Learning and Adaptation Mechanisms
This track will examine the mechanisms of robotic learning and adaptation, highlighting how robots can learn from their environments and experiences. Contributions that explore novel learning algorithms and their applications in dynamic settings are welcome.
Track 08
Developmental Robotics and Human Interaction
This session will explore the field of developmental robotics, focusing on how robots can develop skills through interaction with humans and their environments. Papers that investigate the implications of developmental approaches for social robotics are encouraged.
Track 09
Artificial Life and Its Applications in Medicine
This track will discuss the concept of artificial life and its potential applications in medical research and healthcare. Submissions that explore the implications of artificial life systems for understanding biological processes are particularly welcome.
Track 10
Intelligent Machines and Neuroengineering
This session will focus on the development of intelligent machines that incorporate principles of neuroengineering, emphasizing their potential to enhance human capabilities. Contributions that discuss the ethical implications and societal impacts of these technologies are encouraged.
Track 11
Genetic Algorithms in Neural System Optimization
This track will explore the use of genetic algorithms for optimizing neural systems, focusing on their applications in both theoretical and practical contexts. Researchers are invited to present innovative approaches that demonstrate the effectiveness of genetic algorithms in neural engineering.