Applications of artificial intelligence in battling against covid-19: A literature review
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DOI: 10.1016/j.chaos.2020.110338
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- Bonacini, Luca & Gallo, Giovanni & Patriarca, Fabrizio, 2020. "Drawing policy suggestions to fight Covid-19 from hardly reliable data. A machine-learning contribution on lockdowns analysis," GLO Discussion Paper Series 534, Global Labor Organization (GLO).
- Matthew A. Cole & Robert J R Elliott & Bowen Liu, 2020.
"The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach,"
Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 553-580, August.
- Matthew A Cole & Robert J R Elliott & Bowen Liu, 2020. "The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach," Discussion Papers 20-09, Department of Economics, University of Birmingham.
- Shiqi Ou & Xin He & Weiqi Ji & Wei Chen & Lang Sui & Yu Gan & Zifeng Lu & Zhenhong Lin & Sili Deng & Steven Przesmitzki & Jessey Bouchard, 2020. "Machine learning model to project the impact of COVID-19 on US motor gasoline demand," Nature Energy, Nature, vol. 5(9), pages 666-673, September.
- Arora, Parul & Kumar, Himanshu & Panigrahi, Bijaya Ketan, 2020. "Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
- Toraman, Suat & Alakus, Talha Burak & Turkoglu, Ibrahim, 2020. "Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
- Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
- A. S. Albahri & Jameel R. Al-Obaidi & A. A. Zaidan & O. S. Albahri & Rula A. Hamid & B. B. Zaidan & A. H. Alamoodi & M. Hashim, 2020. "Multi-Biological Laboratory Examination Framework for the Prioritization of Patients with COVID-19 Based on Integrated AHP and Group VIKOR Methods," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(05), pages 1247-1269, August.
- Davide Ferrari & Jovana Milic & Roberto Tonelli & Francesco Ghinelli & Marianna Meschiari & Sara Volpi & Matteo Faltoni & Giacomo Franceschi & Vittorio Iadisernia & Dina Yaacoub & Giacomo Ciusa & Eric, 2020. "Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia—Challenges, strengths, and opportunities in a global health emergency," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
- Yousefpour, Amin & Jahanshahi, Hadi & Bekiros, Stelios, 2020. "Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
- Shiqi Ou & Xin He & Weiqi Ji & Wei Chen & Lang Sui & Yu Gan & Zifeng Lu & Zhenhong Lin & Sili Deng & Steven Przesmitzki & Jessey Bouchard, 2020. "Author Correction: Machine learning model to project the impact of COVID-19 on US motor gasoline demand," Nature Energy, Nature, vol. 5(12), pages 1051-1052, December.
- Pathan, Refat Khan & Biswas, Munmun & Khandaker, Mayeen Uddin, 2020. "Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
- Torrealba-Rodriguez, O. & Conde-Gutiérrez, R.A. & Hernández-Javier, A.L., 2020. "Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
- Melike Bildirici & Nilgun Guler Bayazit & Yasemen Ucan, 2020. "Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM," Energies, MDPI, vol. 13(11), pages 1-18, June.
- Peng, Yaohao & Nagata, Mateus Hiro, 2020. "An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
- Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H., 2020. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
- Arias Velásquez, Ricardo Manuel & Mejía Lara, Jennifer Vanessa, 2020. "Forecast and evaluation of COVID-19 spreading in USA with reduced-space Gaussian process regression," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
- Panwar, Harsh & Gupta, P.K. & Siddiqui, Mohammad Khubeb & Morales-Menendez, Ruben & Singh, Vaishnavi, 2020. "Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
- Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
- Guokang Zhu & Jia Li & Zi Meng & Yi Yu & Yanan Li & Xiao Tang & Yuling Dong & Guangxin Sun & Rui Zhou & Hui Wang & Kongqiao Wang & Wang Huang, 2020. "Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-8, May.
- da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
- Yadav, Milind & Perumal, Murukessan & Srinivas, M, 2020. "Analysis on novel coronavirus (COVID-19) using machine learning methods," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
- Ribeiro, Matheus Henrique Dal Molin & da Silva, Ramon Gomes & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
- Editorial, 2020. "Covid-19 and Climate Change," Journal, Review of Agrarian Studies, vol. 10(1), pages 5-6, January-J.
- Alakus, Talha Burak & Turkoglu, Ibrahim, 2020. "Comparison of deep learning approaches to predict COVID-19 infection," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
- Kavadi, Durga Prasad & Patan, Rizwan & Ramachandran, Manikandan & Gandomi, Amir H., 2020. "Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
- Zeroual, Abdelhafid & Harrou, Fouzi & Dairi, Abdelkader & Sun, Ying, 2020. "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
- Behrouz Pirouz & Sina Shaffiee Haghshenas & Sami Shaffiee Haghshenas & Patrizia Piro, 2020. "Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligenc," Sustainability, MDPI, vol. 12(6), pages 1-21, March.
- Sovesh Mohapatra & Prathul Nath & Manisha Chatterjee & Neeladrisingha Das & Deepjyoti Kalita & Partha Roy & Soumitra Satapathi, 2020. "Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-13, November.
- Behnood, Ali & Mohammadi Golafshani, Emadaldin & Hosseini, Seyedeh Mohaddeseh, 2020. "Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA)," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
- Altan, Aytaç & Karasu, Seçkin, 2020. "Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
- Wang, Peipei & Zheng, Xinqi & Li, Jiayang & Zhu, Bangren, 2020. "Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
- Islam, A.K.M. Najmul & Laato, Samuli & Talukder, Shamim & Sutinen, Erkki, 2020. "Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
- Malki, Zohair & Atlam, El-Sayed & Hassanien, Aboul Ella & Dagnew, Guesh & Elhosseini, Mostafa A. & Gad, Ibrahim, 2020. "Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
- Melin, Patricia & Monica, Julio Cesar & Sanchez, Daniela & Castillo, Oscar, 2020. "Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
- Swapnarekha, H. & Behera, Himansu Sekhar & Nayak, Janmenjoy & Naik, Bighnaraj, 2020. "Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
- Mohamed Abd Elaziz & Khalid M Hosny & Ahmad Salah & Mohamed M Darwish & Songfeng Lu & Ahmed T Sahlol, 2020. "New machine learning method for image-based diagnosis of COVID-19," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-18, June.
- Kırbaş, İsmail & Sözen, Adnan & Tuncer, Azim Doğuş & Kazancıoğlu, Fikret Şinasi, 2020. "Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
- Farooq, Junaid & Bazaz, Mohammad Abid, 2020. "A novel adaptive deep learning model of Covid-19 with focus on mortality reduction strategies," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
- Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
- Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H, 2020. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
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Keywords
Artificial intelligence; Machine learning; Covid-19; SARS-CoV-2; Coronavirus; Epidemiology; Drug discovery; Vaccine development; Artificial neural networks; Evolutionary algorithms; Deep learning; Deep neural networks; Convolutional neural networks;All these keywords.
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