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ZENODO
Article . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
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Generative Artificial Intelligence: Techniques, Applications, Challenges, and Future Directions

descriptionPublicationkeyboard_double_arrow_right Article 31 Mar 2026 English Publisher:Zenodo
Authors: Vaishnavi Sunil Shinde;

doi: 10.5281/zenodo.19344968 , 10.5281/zenodo.19344969

Generative Artificial Intelligence: Techniques, Applications, Challenges, and Future Directions

Abstract

A class of artificial intelligence models known as "generative AI" is able to produce original text, images, audio, video, and code. Advanced deep learning architectures such as Transformer-based large language models (LLMs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs) power it. This essay examines the foundational ideas, cutting-edge methods, applications in various industries, difficulties, moral dilemmas, and potential applications of generative artificial intelligence.

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green