Using CNN-LSTMs and Transformer RNNs for COVID19 Impact Prediction-Reference-Cited by-同舟云学术

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Using CNN-LSTMs and Transformer RNNs for COVID19 Impact Prediction

Published:2023-09-01 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Network, Multimedia and Information Technology (NMITCON)
language:
Short-container-title:

Author:

Bharadi Vinayak Ashok¹,Alegavi Sujata²,Nemade Bhushan³

Affiliation:

1. Finolex Academy of Management and Technology, Mumbai University,Department of Information Technology,India

2. Thakur College of Engineering and Technology, Mumbai University,Department of IoT,Mumbai,Maharashtra,India

3. Mukesh Patel School of Technology Management and Engineering, NMIMS University,Department of Information Technology,Mumbai,India

Funder

NVIDIA

Mumbai University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10275783/10275784/10275886.pdf?arnumber=10275886

Reference25 articles.

1. Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries

2. Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries

3. Speech emotion recognition using deep 1D & 2D CNN LSTM networks

4. Using Luong and Bahdanau Attention Mechanism on the Long Short Term Memory Networks - A COVID-19 Impact Prediction Case Study;bharadi;Smart Computing and Self-Adaptive Systems,0

5. Deep learning: RNNs and LSTM;di pietro;Med Image Comput Comput Assist Interv,2019

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