Integrated Intelligent Computing Models for Cognitive-Based Neurological Disease Interpretation in Children: A Survey

Archana Tandon; Bireshwar Dass Mazumdar; Manoj Kumar Pal

doi:10.4108/eetpht.10.5541

Authors

Archana Tandon United University Prayagraj
Bireshwar Dass Mazumdar Bennett University
Manoj Kumar Pal United University Prayagraj

DOI:

https://doi.org/10.4108/eetpht.10.5541

Keywords:

Cognitive-based Neurological Diseases, Deep Learning, Natural Language Processing, Speech Recognition, Brain Imaging, Intelligent Computing Model

Abstract

INTRODUCTION: This piece of work provides the description of integrated intelligent computing models for the interpretation of cognitive-based neurological diseases in children. These diseases can have a significant impact on children's cognitive and developmental functioning.

OBJECTIVES: The research work review the current diagnosis and treatment methods for cognitive based neurological diseases and discusses the potential of machine learning, deep learning, Natural language processing, speech recognition, brain imaging, and signal processing techniques in interpreting the diseases.

METHODS: A survey of recent research on integrated intelligent computing models for cognitive-based neurological disease interpretation in children is presented, highlighting the benefits and limitations of these models.

RESULTS: The significant of this work provide important implications for healthcare practice and policy, with strengthen diagnosis and treatment of cognitive-based neurological diseases in children.

CONCLUSION: This research paper concludes with a discussion of the ethical and legal considerations surrounding the use of intelligent computing models in healthcare, as well as future research directions in this area.

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References

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