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Articles

Research Article

Published: 23 Aug 2024

Challenging Conventions Towards Reliable Robot Navigation Using Deep Reinforcement Learning

Volume 1

• Amjad Yousef Majid

  

  Amjad Yousef Majid
  amjad.y.majid@gmail.com

  

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• Tomas van Rietbergen

  

  Tomas van Rietbergen
  t.l.vanrietbergen@student.tudelft.nl

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• R Venkatesha Prasad

  

  R Venkatesha Prasad
  r.r.venkateshaprasad@tudelft.nl

  

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Effective indoor navigation in the presence of dynamic obstacles is crucial for mobile robots. Previous research on deep reinforcement learning (DRL) for robot navigation has primarily focused on expanding neural network (NN) architectures and optimizing hardware setups. However, the impact of other critical factors, such as backward motion enablement, frame stacking buffer size, and the design of the behavioral reward function, on DRL-based navigation remains relatively unexplored. To address this..

Journal: Computing&AI Connect

Research Article

Published: 29 Jul 2024

Mirror, Mirror on the Wall: Automating Dental Smile Analysis with AI in Smart Mirrors

Volume 1

• Mariia Baidachna

  

  Mariia Baidachna
  2828197b@student.gla.ac.uk

  

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• Haneen Fatima

  

  Haneen Fatima
  h.fatima.1@research.gla.ac.uk

  

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• Rahaf Omran

  

  Rahaf Omran
  rahaf.omran@hotmail.com

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• Nour Ghadban

  

  Nour Ghadban
  nour.ghadban@glasgow.ac.uk

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• Muhammad Ali Imran

  

  Muhammad Ali Imran
  muhammad.imran@glasgow.ac.uk

  

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• Ahmad Taha

  

  Ahmad Taha
  ahmad.taha@glasgow.ac.uk

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• Lina Mohjazi

  

  Lina Mohjazi
  lina.mohjazi@glasgow.ac.uk

  

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Journal: Computing&AI Connect

Review Article

Published: 13 Jun 2024

Hierarchical Autoencoder-Based Lossy Compression for Large-Scale High-Resolution Scientific Data

Volume 1

• Hieu Le

  

  Hieu Le
  hieult@tamu.edu

  

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• Jian Tao

  

  Jian Tao
  

  

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Lossy compression has become an essential technique to reduce data size in many domains. This type of compression is especially valuable for large-scale scientific data, whose size ranges up to several petabytes. Although Autoencoder-based models have been successfully leveraged to compress images and videos, such neural networks have not widely gained attention in the scientific data domain. Our work presents a neural network that not only significantly compresses large-scale scientific..

Journal: Computing&AI Connect

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