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Tarek Z, Shams MY, Towfek SK, Alkahtani HK, Ibrahim A, Abdelhamid AA, Eid MM, Khodadadi N, Abualigah L, Khafaga DS, Elshewey AM. An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction. Biomimetics (Basel) 2023; 8:552. [PMID: 37999193 PMCID: PMC10669113 DOI: 10.3390/biomimetics8070552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 11/05/2023] [Accepted: 11/14/2023] [Indexed: 11/25/2023] Open
Abstract
The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction.
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Affiliation(s)
- Zahraa Tarek
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35561, Egypt;
| | - Mahmoud Y. Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt;
| | - S. K. Towfek
- Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA;
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt;
| | - Hend K. Alkahtani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
| | - Abdelaziz A. Abdelhamid
- Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt;
- Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
| | - Marwa M. Eid
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt;
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt
| | - Nima Khodadadi
- Department of Civil and Architectural Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, FL 33146, USA;
| | - Laith Abualigah
- Computer Science Department, Al al-Bayt University, Mafraq 25113, Jordan;
- College of Engineering, Yuan Ze University, Taoyuan 32003, Taiwan
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
- MEU Research Unit, Middle East University, Amman 11831, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
- School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
- School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Ahmed M. Elshewey
- Computer Science Department, Faculty of Computers and Information, Suez University, Suez 43512, Egypt;
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Theocharopoulos PC, Tsoukala A, Georgakopoulos SV, Tasoulis SK, Plagianakos VP. Analysing sentiment change detection of Covid-19 tweets. Neural Comput Appl 2023; 35:1-11. [PMID: 37362564 PMCID: PMC10230484 DOI: 10.1007/s00521-023-08662-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 05/10/2023] [Indexed: 06/28/2023]
Abstract
The Covid-19 pandemic made a significant impact on society, including the widespread implementation of lockdowns to prevent the spread of the virus. This measure led to a decrease in face-to-face social interactions and, as an equivalent, an increase in the use of social media platforms, such as Twitter. As part of Industry 4.0, sentiment analysis can be exploited to study public attitudes toward future pandemics and sociopolitical situations in general. This work presents an analysis framework by applying a combination of natural language processing techniques and machine learning algorithms to classify the sentiment of each tweet as positive, or negative. Through extensive experimentation, we expose the ideal model for this task and, subsequently, utilize sentiment predictions to perform time series analysis over the course of the pandemic. In addition, a change point detection algorithm was applied in order to identify the turning points in public attitudes toward the pandemic, which were validated by cross-referencing the news report at that particular period of time. Finally, we study the relationship between sentiment trends on social media and, news coverage of the pandemic, providing insights into the public's perception of the pandemic and its influence on the news.
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Affiliation(s)
| | - Anastasia Tsoukala
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | | | - Sotiris K. Tasoulis
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Vassilis P. Plagianakos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
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Karyukin V, Mutanov G, Mamykova Z, Nassimova G, Torekul S, Sundetova Z, Negri M. On the development of an information system for monitoring user opinion and its role for the public. JOURNAL OF BIG DATA 2022; 9:110. [PMID: 36465138 PMCID: PMC9684810 DOI: 10.1186/s40537-022-00660-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 10/20/2022] [Indexed: 06/17/2023]
Abstract
Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages, such as the Russian and Kazakh languages in social media, are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users' opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. The experimental part is devoted to building machine learning models for sentiment analysis on the Russian and Kazakh datasets. Then the performance of the models is evaluated with accuracy, precision, recall, and F1-score metrics. The models with the highest scores are selected for implementation in the OMSystem. Then the OMSystem's social analytics module is used to thoroughly analyze the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease. The analysis allowed us to discover the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system's study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people's moods and attitudes to the Government's policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust in vaccination, etc.) and assessment of the social mood.
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Affiliation(s)
| | | | - Zhanl Mamykova
- Al-Farabi Kazakh National University, Almaty, 050040 Kazakhstan
| | | | - Saule Torekul
- Al-Farabi Kazakh National University, Almaty, 050040 Kazakhstan
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