Inceptionv3-LSTM-COV: A multi-label framework for identifying adverse reactions to COVID medicine from chemical conformers based on Inceptionv3 and long short-term memory
Due to the global COVID-19 pandemic, distinct medicines have been devel-oped for treating the coronavirus disease (COVID). However, predicting and identifying potential adverse reactions to these medicines face significant chal-lenges in producing effective COVID medication. Accurate prediction of a...
Saved in:
| Main Authors: | Pranab Das, Dilwar Hussain Mazumder |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Electronics and Telecommunications Research Institute (ETRI)
2024-12-01
|
| Series: | ETRI Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.4218/etrij.2023-0288 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Penerapan Blok SE-NET Pada Deep Learning Inceptionv3 untuk Meningkatkan Deteksi Penyakit Mpox pada Manusia
by: M. Bakhara Alief Rachman, et al.
Published: (2024-10-01) -
Integrating Deep Learning and Energy Management Standards for Enhanced Solar–Hydrogen Systems: A Study Using MobileNetV2, InceptionV3, and ISO 50001:2018
by: Salaki Reynaldo Joshua, et al.
Published: (2024-11-01) -
Enhancing Cervical Cancer Classification: Through a Hybrid Deep Learning Approach Integrating DenseNet201 and InceptionV3
by: Abhiram Sharma, et al.
Published: (2025-01-01) -
Association between penicillin allergy labels and serious adverse events in hospitalized patients: a systematic review and meta-analysis
by: Shipeng Zhang, et al.
Published: (2025-01-01) -
Association between the use of herbal medicines during pregnancy and adverse fetal outcomes among mothers in eastern Ethiopia, 2023
by: Tamirat Getachew, et al.
Published: (2024-12-01)