Skip to main content
SpringerLink
Log in
Springer
Cognitive Computation
Publishing model:
Hybrid

Cognitive Computation - Call for Papers: Generative AI for Cognitive Computation

Aim and Motivation:

Generative AI refers to the application of machine learning techniques to generate new and original content, such as images, text, and even entire narratives. Cognitive computation involves the development of computational models that simulate human cognitive abilities, such as perception, reasoning, learning, and problem-solving. While cognitive computing systems have made significant advancements in recent years, there is a growing interest in harnessing the power of generative AI to enhance their capabilities. Generative AI models such as ChatGPT and DALL-E have gained attention due to their ability to generate original and creative outputs and images, which can augment the intelligence of cognitive computing systems.


Generative AI algorithms, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), have demonstrated remarkable capabilities in generating realistic and diverse content. GANs consist of a generator network and a discriminator network. The generator network generates synthetic data samples, while the discriminator network tries to distinguish between real and generated samples. VAEs are generative models that learn latent representations of data. They consist of an encoder network, a decoder network, and a latent space. VAEs have been widely used for tasks such as image generation, text generation, and anomaly detection. These algorithms can be leveraged to enhance cognitive computing systems by enabling them to generate creative outputs, such as artwork, music, storytelling, etc., with an endless number of possibilities. This opens new possibilities for human-computer collaboration and co-creation. Generative AI can also be applied to personalize the assistance provided by cognitive computing systems. By analyzing user preferences and behavior, generative AI algorithms can generate tailored recommendations and suggestions. This enhances the user experience and allows cognitive systems to adapt to individual needs more effectively.


Therefore, this special issue aims to serve as a comprehensive documentation and foundation for researchers and developers to submit their works on the topic of Generative AI for Cognitive Computation. The authors are expected to report state-of-the-art developments and results in this field. They are welcome to submit unpublished novel papers (not currently under review at any other venue, conference, or journal) in the
following relevant areas:

Topics:

  • Generative AI algorithms and techniques for cognitive computation
  • Cognitive architectures and neural networks for generative AI
  • Integrating generative AI with cognitive computing systems
  • Explainable generative AI models for cognitive computation
  • Enhancing creative outputs and co-creation through generative AI
  • Deep reinforcement learning for generative AI in cognitive tasks
  • Cognitive robotics and generative AI
  • Transfer learning and lifelong learning in generative AI for cognitive computation
  • Personalization and recommendation systems using generative AI in cognitive computing
  • Human-AI collaboration in generative cognitive systems
  • Ethical considerations in generative AI for cognitive computation
  • Cognitive biases and challenges in generative AI models
  • Cross-modal generative models for multi-sensory cognitive computation
  • Computational creativity and generative AI in cognitive systems
  • Generative models for decision-making and problem-solving in cognitive computing
  • Generative AI for cognitive assistive technologies and personalized learning


Guest Editors:

  • Vinay Chamola, BITS Pilani, Pilani campus India, E-mail: vinay.chamola@pilani.bits-pilani.ac.in
  • Vikas Hassija, KIIT University, Bhubaneswar, India, Email: Vikas.hassijafcs@kiit.ac.in
  • Kaizhu Huang, Duke Kunshan University, China, E-mail: kaizhu.huang@dukekunshan.edu.cm
  • Mufti Mahmud, Nottingham Trent University, UK, E-mail: mufti.mahmud@ntu.ac.uk
  • Fatemeh Afghah, Clemson University, USA, fafghah@clemson.edu
  • Sherali Zeadally, University of Kentucky, USA, E-mail: szeadally@uky.edu
  • Biplab Sikdar, National University of Singapore, Singapore , bsikdar@nus.edu.sg


Deadlines:

SI submissions deadline: January 2024 (however, submissions submitted earlier will be processed on a rolling basis)
First notification of acceptance: February 2024
Submission of revised papers: March 2024
Final notification to authors: April 2024
Publication of SI: Rolling basis (Late 2024)


Submission Instructions:

Prepare your paper in accordance with the Journal guidelines: www.springer.com/12559 (this opens in a new tab).
Submit manuscripts at: http://www.editorialmanager.com/cogn/ (this opens in a new tab) . Select “SI: Generative AI for Cognitive Computation” for the special issue under “Additional Information.” Your paper must contain significant and original work that has not been published nor
submitted to any journals. All papers will be reviewed following standard reviewing procedures of the Journal.
 

Search

Navigation