Srivastav, Bhanu (2021): The novel Artificial Neural Network assisted models: A review.
Preview |
PDF
MPRA_paper_106499.pdf Download (360kB) | Preview |
Abstract
Neural networks are one of the methods of artificial intelligence. It is founded on an existing knowledge and capacity to learn by illustration of the biological nervous system. Neural networks are used to solve problems that could not be modeled with conventional techniques. A neural structure can be learned, adapted, predicted, and graded. The potential of neural network parameters is very strong prediction. The findings are more reliable than standard mathematical estimation models. Therefore, it has been used in different fields. This research reviews the most recent advancement in utilizing the Artificial neural networks. The reviewed studies have been extracted from Web of Science maintained by Clarivate Analytics in 2021. We find that among the other applications of ANN, the applications on Covid-19 are on the rise.
Item Type: | MPRA Paper |
---|---|
Original Title: | The novel Artificial Neural Network assisted models: A review |
Language: | English |
Keywords: | ANN; Covid-19; Dust; Gas; Organic richness |
Subjects: | I - Health, Education, and Welfare > I1 - Health I - Health, Education, and Welfare > I1 - Health > I10 - General Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q49 - Other Y - Miscellaneous Categories > Y8 - Related Disciplines > Y80 - Related Disciplines |
Item ID: | 106499 |
Depositing User: | Bhanu Srivastav |
Date Deposited: | 15 Mar 2021 07:16 |
Last Modified: | 15 Mar 2021 07:16 |
References: | M. O. Okwu and L. K. Tartibu, “Artificial Neural Network,” in Studies in Computational Intelligence, 2021. L. Gatys, A. Ecker, and M. Bethge, “A Neural Algorithm of Artistic Style,” J. Vis., 2016. M. Majumder, “Artificial Neural Network,” 2015 B. Yegnanarayana, “Artificial neural networks for pattern recognition,” Sadhana, 1994. S. J. Kwon, Artificial neural networks. 2011. A. Intelligence, “Fundamentals of Neural Networks Artificial Intelligence Fundamentals of Neural Networks Artificial Intelligence,” Fundam. Neural Networks AI Course Lect. 37 – 38, notes, slides, 2010. T. G. Clarkson, “Introduction to neural networks,” Neural Netw. World, 1996. S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” in Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017, 2018. M. I. Jordan and C. M. Bishop, “Neural networks,” in Computer Science Handbook, Second Edition, 2004. Y. Goldberg, “A primer on neural network models for natural language processing,” J.Artif. Intell. Res., 2016. P. M. Buscema, G. Massini, M. Breda, W. A. Lodwick, F. Newman, and M. AsadiZeydabadi, “Artificial neural networks,” in Studies in Systems, Decision and Control, 2018. M. H. Hassoun, “Fundamentals of Artificial Neural Networks,” Proc. IEEE, 2005 B. L. Yoon, “Artificial neural network technology,” ACM SIGSMALL/PC Notes, 1989. P. Tino, L. Benuskova, and A. Sperduti, “Artificial neural network models,” in Springer Handbook of Computational Intelligence, 2015. M. E. Yalçın, T. Ayhan, and R. Yeniçeri, “Artificial neural network models,” in SpringerBriefs in Applied Sciences and Technology, 2020. O. Rahmati et al., “Hybridized neural fuzzy ensembles for dust source modeling and prediction,” Atmos. Environ., vol. 224, p. 117320, 2020. J. Wang et al., “Design and Application of Mixed Natural Gas Monitoring System Using Artificial Neural Networks,” Sensors, vol. 21, no. 2, p. 351, 2021. A. Barham, M. S. Ismail, M. Hermana, E. Padmanabhan, Y. Baashar, and O. Sabir, “Predicting the maturity and organic richness using artificial neural networks (ANNs): A case study of Montney Formation, NE British Columbia, Canada,” Alexandria Eng. J., vol. 60, no. 3, pp. 3253–3264, 2021. M. S. Nawaz, P. Fournier-Viger, A. Shojaee, and H. Fujita, “Using artificial intelligence techniques for COVID-19 genome analysis,” Appl. Intell., pp. 1–18, 2021. N. N. Hamadneh, M. Tahir, and W. A. Khan, “Using Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and Mexico,” Mathematics, vol. 9, no. 2, p. 180, 2021. N. Shatnawi and H. Abu-Qdais, “Assessing and predicting air quality in northern Jordan during the lockdown due to the COVID-19 virus pandemic using artificial neural network,” Air Qual. Atmos. Heal., pp. 1–10, 2021. Q. Guo and Z. He, “Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence,” Environ. Sci. Pollut. Res., pp. 1–11, 2021. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/106499 |