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...
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| Format: | Article |
| Language: | English |
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Electronics and Telecommunications Research Institute (ETRI)
2024-12-01
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| Series: | ETRI Journal |
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| Online Access: | https://doi.org/10.4218/etrij.2023-0288 |
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| _version_ | 1846106841944162304 |
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| author | Pranab Das Dilwar Hussain Mazumder |
| author_facet | Pranab Das Dilwar Hussain Mazumder |
| author_sort | Pranab Das |
| collection | DOAJ |
| description | 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 adverse reactions to COVID medications is crucial for ensuring patient safety and medicine success. Recent advancements in computational models used in pharmaceutical production have opened up new possibilities for detecting such adverse reactions. Due to the urgent need for effective COVID medication development, this research presents a multi-label Inceptionv3 and long short-term memory methodology for COVID (Inceptionv3-LSTM-COV) medicine development. The presented experimental evaluations were conducted using the chemical conformer image of COVID medicine. The features of the chemi-cal conformer are denoted utilizing the RGB color channel, which is extracted using Inceptionv3, GlobalAveragePooling2D, and long short-term memory (LSTM) layers. The results demonstrate that the efficiency of the Inceptionv3-LSTM-COV model outperformed the previous study’s perfor-mance and achieved better results compared to MLCNN-COV, Inceptionv3, ResNet50, MobileNetv2, VGG19, and DenseNet201 models. The proposed model reported the highest accuracy value of 99.19% in predicting adverse reactions to COVID medicine. |
| format | Article |
| id | doaj-art-4c271565b24f465588efa2caeeab5d63 |
| institution | Kabale University |
| issn | 1225-6463 2233-7326 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Electronics and Telecommunications Research Institute (ETRI) |
| record_format | Article |
| series | ETRI Journal |
| spelling | doaj-art-4c271565b24f465588efa2caeeab5d632024-12-27T04:47:02ZengElectronics and Telecommunications Research Institute (ETRI)ETRI Journal1225-64632233-73262024-12-014661030104610.4218/etrij.2023-0288Inceptionv3-LSTM-COV: A multi-label framework for identifying adverse reactions to COVID medicine from chemical conformers based on Inceptionv3 and long short-term memoryPranab DasDilwar Hussain MazumderDue 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 adverse reactions to COVID medications is crucial for ensuring patient safety and medicine success. Recent advancements in computational models used in pharmaceutical production have opened up new possibilities for detecting such adverse reactions. Due to the urgent need for effective COVID medication development, this research presents a multi-label Inceptionv3 and long short-term memory methodology for COVID (Inceptionv3-LSTM-COV) medicine development. The presented experimental evaluations were conducted using the chemical conformer image of COVID medicine. The features of the chemi-cal conformer are denoted utilizing the RGB color channel, which is extracted using Inceptionv3, GlobalAveragePooling2D, and long short-term memory (LSTM) layers. The results demonstrate that the efficiency of the Inceptionv3-LSTM-COV model outperformed the previous study’s perfor-mance and achieved better results compared to MLCNN-COV, Inceptionv3, ResNet50, MobileNetv2, VGG19, and DenseNet201 models. The proposed model reported the highest accuracy value of 99.19% in predicting adverse reactions to COVID medicine.https://doi.org/10.4218/etrij.2023-0288adverse medicine reactionscovid medicine developmentinceptionv3lstmmulti-label |
| spellingShingle | Pranab Das Dilwar Hussain Mazumder 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 ETRI Journal adverse medicine reactions covid medicine development inceptionv3 lstm multi-label |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | adverse medicine reactions covid medicine development inceptionv3 lstm multi-label |
| url | https://doi.org/10.4218/etrij.2023-0288 |
| work_keys_str_mv | AT pranabdas inceptionv3lstmcovamultilabelframeworkforidentifyingadversereactionstocovidmedicinefromchemicalconformersbasedoninceptionv3andlongshorttermmemory AT dilwarhussainmazumder inceptionv3lstmcovamultilabelframeworkforidentifyingadversereactionstocovidmedicinefromchemicalconformersbasedoninceptionv3andlongshorttermmemory |