Collection

Generative AI for Cognitive Computation

Submission status
Closed

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 They are welcome to submit unpublished novel papers (not currently under review at any other venue, conference, or journal) in the relevant areas.

View the full Call for Papers here.

Editors

  • Vinay Chamola Vinay Chamola , 

    Vinay Chamola

    BITS Pilani, Pilani campus India, E-mail: vinay.chamola@pilani.bits-pilani.ac.in

  • Vikas Hassija Vikas Hassija , 

    Vikas Hassija

    KIIT University, Bhubaneswar, India, Email: Vikas.hassijafcs@kiit.ac.in

  • Kaizhu Huang Kaizhu Huang , 

    Kaizhu Huang

    Duke Kunshan University, China, E-mail: kaizhu.huang@dukekunshan.edu.cm

  • Mufti Mahmud Mufti Mahmud , 

    Mufti Mahmud

    Nottingham Trent University, UK, E-mail: mufti.mahmud@ntu.ac.uk

  • Fatemeh Afghah Fatemeh Afghah , 

    Fatemeh Afghah

    Clemson University, USA, fafghah@clemson.edu

  • Sherali Zeadally Sherali Zeadally  &

    Sherali Zeadally

    University of Kentucky, USA, E-mail: szeadally@uky.edu

  • Biplab Sikdar Biplab Sikdar

    Biplab Sikdar

    National University of Singapore, Singapore, bsikdar@nus.edu.sg

Articles (4 in this collection)