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Development of an application for creation and learning of neural networks to utilize in environmental sciences | ||
Caspian Journal of Environmental Sciences | ||
دوره 18، شماره 5، اسفند 2020، صفحه 595-601 اصل مقاله (531.29 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22124/cjes.2020.4491 | ||
نویسندگان | ||
Ilgiz Rustamovich Sultanbekov* ؛ Irina Yurievna Myshkina؛ Larisa Yurievna Gruditsyna | ||
Department of Information Technologies and Energy Systems, Naberezhnye Chelny Institute (branch) of FSAEI HE KFU, Kazan Federal University,Kazan, Russia | ||
چکیده | ||
Machine learning methods originated from artificial intelligence and today are applied in several fields concerning environmental sciences. Thanks to their powerful nonlinear modelling capability, machine learning methods today are utilized in satellite data processing, general circulation models(GCM), weather and climate prediction, air quality forecasting, analysis and modelling of environmental data, oceanographic and hydrological forecasting, ecological modelling, and monitoring of snow, ice and forests. Currently, the popularity of neural networks is growing; their areas of application are constantly expanding. In these conditions, the task of choosing a convenient tool for utilizing in environmental science with neural networks becomes urgent. There are many tools for working with neural networks, but each of them has its own drawbacks. So most of the existing tools require users to have programming knowledge; there are no tools to help quickly select the optimal network structure for the problem being solved. The purpose of the research is to simplify the process of choosing the optimal structure of an artificial neural network by developing an application with a graphical user interface with a visual representation of the stages of creating and learning neural networks in environmental sciences. The object of research is artificial feed-forward neural networks. Research work on the study, comparison and analysis of existing tools for the creation, learning and use of artificial neural networks has been carried out. Based on the research results, an application with a graphical interface aimed at solving the assigned tasks has been developed. An application developed to achieve this goal works correctly, without failures, and allows creating and learning feed-forward neural networks without programming knowledge. | ||
کلیدواژهها | ||
Machine learning؛ Artificial neural networks؛ Gradient descent؛ Qt؛ Clean architecture؛ Environmental science | ||
مراجع | ||
Asanov, AZ, Myshkina, IYu 2017, Investigation of the possibility of using neural networks in solving the problem of selecting a team for the implementation of a project. Problems of Management, 1: 31-39. Ayzel, G, Heistermann, M, Sorokin, A, Nikitin, O & Lukyanova, O 2019, All convolutional neural networks for radar-based precipitation nowcasting. Procedia Computer Science, 150: 186-192. Elahi, E, Weijun, C, Zhang, H & Abid, M 2019, Use of artificial neural networks to rescue agrochemical-based health hazards: A resource optimisation method for cleaner crop production. Journal of Cleaner Production, 238: 117900. Goodfellow, I, Bengio, Y, Courville, A 2016, Deep learning, The MIT Press, 800 p. Kirkpatrick, J, Pascanu, R, Rabinowitz, N, Veness, J, Desjardins, G, Rusu, AA, Milan, K, Quan, J, Ramalho, T, Grabska-Barwinska, A & Hassabis, D 2017, Overcoming catastrophic forgetting in neural networks. Proceedings of the Russian National Academy of Sciences, 114: 3521-3526. Martin, R 2019, Clean architecture. A Craftsman's guide to software structure and design. St. Petersburg, Peter, Russia, 352 p. Myshkina, IYu, Asanov, AZ, Grudtsyna, LYu 2015, Evaluation and selection of personnel based on clear and fuzzy cognitive models. International Journal of Soft Computing, 10: 448-453. Nikolaeva, SG 2015, Neural networks. Implementation in Matlab: Textbook. Kazan: Kazan State Power Engineering University, Kazan, Russia, 92 p. Rasooli, SB, Bonyad, AE, Pir Bavaghar, M 2018, Forest fire vulnerability map using remote sensing data, GIS and AHP analysis (Case study: Zarivar Lake surrounding area). Caspian Journal of Environmental Sciences, 16: 369-377 Rojas, R 1996, Neural Networks: A Systematic Introduction. Springer, 552 p. Schlee, M 2018, Qt 5.10. Professional programming in C ++. St. Petersburg: BHV-Petersburg, Russia, 1072 p. Segler, MH, Preuss, M & Waller, MP 2018, Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 555 (7698): 604-610. Shakla, N 2019, Machine Learning and TensorFlow. St. Petersburg: Peter, 366 p. Stroustrup, B 2000, The C++ Programming Language: Special Edition. Addison-Wesley Professional, 1030 p. Zhang, Q, Bai, C, Liu, Z, Yang, LT, Yu, H, Zhao, J, Yuan, HC 2020, A GPU-based residual network for medical image classification in smart medicine. Information Sciences, 536: 91-100. | ||
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