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Predicting driver emotion to reduce accidents using CNN-LSTM and DCGAN

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Material type: Text

TextPublication details: Islamabad (unpublished) 2024Description: x, 53 p. : ill.,Col. ; 30 cm. +CDSubject(s):

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Dissertation note: Thesis (MS)--Riphah International University, 2024

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Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Thesis	City Campus	005.8 SHA (Browse shelf(Opens below))	1	NOT for LOAN		53767

Previous	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	Next
Previous	005.8 SAJ Enhancing patient care and support through large language models	005.8 SAN Word sens disambiguation of urdu text using transformers	005.8 SHA Comparative analysis of lightweight cryptographic algorithms for industrial control systems	005.8 SHA Predicting driver emotion to reduce accidents using CNN-LSTM and DCGAN	005.8 SHA Insecure software development and threat mapping via security frameworks	005.8 SHO Developing and implementing al-based multi-level classification system for identification of crop diseases	005.8 SOH Towards neural-symbolic coherence in Urdu language visual question answering	Next

Submitted in the fulfillment of the requirements for the degree of Master of Science in Data Science to the Faculty of Computing.

Thesis supervisor: Dr. Adeel Zafar
Co- Supervisor: Dr. Atta Ur Rahman

Includes bibliographical references.

Thesis (MS)--Riphah International University, 2024

English

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