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Formation of a knowledge base to analyze the issue of transport and the environment | ||
| Caspian Journal of Environmental Sciences | ||
| دوره 18، شماره 5، اسفند 2020، صفحه 615-621 اصل مقاله (546.02 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22124/cjes.2020.4494 | ||
| نویسندگان | ||
| Ilyas Idrisovich Ismagilov* 1؛ Aynur Ayratovich Murtazin1؛ Dina Vladimirovna Kataseva2؛ Alexey Sergeevich Katasev2؛ Anastasia Olegovna Barinova2 | ||
| 1Department of Economic Theory and Econometrics, Institute of Management, Economics and Finance of Kazan (Volga Region) Federal University, Kazan, Russia | ||
| 2Department of Information Security Systems, Institute of Computer Technologies and Information Security, Kazan National Research Technical University, Kazan, Russia | ||
| چکیده | ||
| The environmental impact of transport is significant because transport is a significant user of energy, and burns most of the world's petroleum. This issue creates air pollution, including nitrous oxides and particulates, and is a substantial contributor to global warming through emission of carbon dioxide. This article analyzes the Issue of Transport and the Environment, then solves the evaluation problem of the functional state of vehicle drivers based on the formation and use of a fuzzy knowledge base. The provided the classification of human functional state types. The expediency of using pupillometry as an objective method to analyze the pupillary reaction of a human eye to illumination change is pointed out to assess its functional state. The Analysis of the neural network approach is carried out to determine the functional state of a person's intoxication. It points out its main drawback associated with the impossibility of interpreting the solution obtained using a neural network. To eliminate this drawback and improve the efficiency of decision support to assess the functional state of vehicle drivers, it is proposed to use the mathematical apparatus of fuzzy neural networks to form fuzzy knowledge bases and provide their use in inference mechanisms. In this case, the solution to the problem will be a binary answer ("drunk", "not drunk") with the interpretation of the solution obtained in the form of a set of fuzzy rules written in a natural language understandable to humans. The tasks are set for the formation of a knowledge base to assess the functional state of drivers. The scheme of pupillogram initial data collection is described, as well as the stages of their preparation for Analysis. Pupillogram parameters that significantly characterize the pupillary response of a person to illumination change were identified by an expert method using the methods of correlation analysis: the minimum diameter of the pupil, the diameter of its half constriction, the amplitude of constriction and the time of half expansion. The structure of the generated data sample with the volume of 1000 records is described. A knowledge base was formed after their Analysis, consisting of 2632 fuzzy production rules. To assess the accuracy of determining the functional state of a person based on the knowledge base, a balanced test sample of 400 records (200 records of each class of functional state) was compiled. The test results showed that the number of type 1 errors was 1%, and the number of type 2 errors was 3%. The overall accuracy of determining the functional state of a person based on the generated knowledge base was 96%. The generated fuzzy knowledge base can be effectively used in decision support systems to assess the functional state of vehicle drivers when they undergo a pre-trip medical examination. | ||
| کلیدواژه‌ها | ||
| Environment؛ Functional state؛ Transport safety؛ Knowledge base؛ Fuzzy production rule؛ Decision support؛ Pupillometry؛ Pupillary response | ||
| مراجع | ||
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Abro, AG, Mohamad Saleh, J, Bin Masri, S 2011, Features selection for training generator excitation neuro controller using statistical methods. Communications in Computer and Information Science, 179 CCIS(PART 1): 353-364. Agranovich, YYu, Kontsevaya, NV, Podvalny, SL, Khatskevich, VL 2014, A synthesis of statistical and deterministic methods in problem of smoothing for time series. Automation and Remote Control, 75: 971-976. Akhmetvaleev, AM, Katasev, AS, 2018, Neural network model of human intoxication functional state determining in some problems of the transport safety solution. Computer Research and Modeling, 10: 285-293. Alekseev, A, Katasev, A, Kirillov, A, Khassianov, A, Zuev, D 2020, Prototype of the classifier for the decision support system of legal documents. CEUR Workshop Proceedings, 2543: 328-335. Chernorizov, AM, Isaychev, SA, Zinchenko, YP, Gradoboeva, ON, Galatenko, VV 2016, Psychophysiological methods for the diagnostics of human functional states: New approaches and perspectives. Psychology in Russia: State of the Art, 9: 23-36. Chibisov, SM, Dementiev, MV, Meladze, ZA, Skorik, AS, Neborak, EV 2016, Evaluation of A 24-hour blood pressure profile in persons with high intensity of work and shift schedule based on the pre-trip medical check-ups. Research Journal of Pharmaceutical, Biological and Chemical Sciences, 7: 2208-2213. Chupin, MM, Katasev, AS, Akhmetvaleev, AM, Kataseva, DV 2019, Neuro-fuzzy model in supply chain management for objects state assessing. International Journal of Supply Chain Management, 8: 201-208. Dagaeva, M, Garaeva, A, Anikin, I, Makhmutova, A, Minnikhanov, R 2019, Big spatiotemporal data mining for emergency management information systems. IET Intelligent Transport Systems, 13: 1649-1657. Gerike, R, de Nazelle, A, Wittwer, R & Parkin, J 2019, Special Issue “Walking and Cycling for better Transport, Health and the Environment”. Transportation research Part A: Policy and practice, 123. Ismagilov, II, Mustafin, AN, Shleymovich, MP, Katasev, AS, Lyasheva, SA, Kataseva, DV 2019, Methods and algorithms for solving problems in the automatic recognition of license plates. Journal of Advanced Research in Dynamical and Control Systems, 11(8 Special Issue): 1732-1736. Ji, K, Shen, Y 2020, Dyadic wavelet transform and signal extraction of GNSS coordinate time series with missing data. Cehui Xuebao/Acta Geodaetica et Cartographica Sinica, 49: 537-546. Katasev, AS 2019, Neuro-fuzzy model of fuzzy rules formation for objects state evaluation in conditions of uncertainty. Computer Research and Modeling, 11: 477-492. Kysil, S 2017, The issue of transport infrastructure organization for storage of electric vehicles in the urban environment of the largest cities. Lomakin, N, Shokhnekh, A, Sazonov, S, Lukyanov, G, Gorbunova 2019, Hadoop and Deductor based digital ai system for predicting the cost of innovative products in conditions of digitalization of the economy. ACM International Conference Proceeding Series: 3373810. Mazhari, M & Ferguson, J 2018, Bacterial responses to environmental herbicide pollutants (glyphosate and paraquat), Caspian Journal of Environmental Sciences, 16: 35-43 Mousavi, SN, Mozaffari, Z & Motamed, MK 2018, The effect of higher fuel price on pollutants emission in Iran, Caspian Journal of Environmental Sciences, 16: 1-10. Perfilieva, IG, Yarushkina, NG, Afanasieva, TV, Romanov, AA 2016, Web-based system for enterprise performance analysis on the basis of time series data mining. Advances in Intelligent Systems and Computing, 450: 75-86. Shleymovich, MP, Dagaeva, MV, Katasev, AS, Lyasheva, SA, Medvedev, MV 2018, The Analysis of images in control systems of unmanned automobiles on the base of energy features model. Computer Research and Modeling, 10: 369-376. Siergiejczyk, M, Pas, J & Rosinski, A 2016, Issue of reliability–exploitation evaluation of electronic transport systems used in the railway environment with consideration of electromagnetic interference. IET Intelligent Transport Systems, 10(9), pp.587-593. Smyl, S 2020, A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting, 36: 75-85. Varshney, N, Singh, JP 2020, Road transport safety: VANET using cluster identity and digital certification. International Journal of Advanced Science and Technology, 29: 6482-6489. Watanabe, T, Utsumi, T, Sugiyama, T, Sugasawa, J, Ikeda, T 2008, Analysis of pupillary light reflex alternation caused by artificial afferent defects using infrared video-pupillogram and recently developed reflex factors. Neuro-Ophthalmology Japan, 25: 260-265. Winkler, C & Mocanu, T 2017, Methodology and application of a German national passenger transport model for future transport scenarios. In Proceedings of the 45th European Transport Conference. Zhang, Q, Xia, D, Wang, G 2017, Three-way decision model with two types of classification errors. Information Sciences, 420: 431-453. Zheng, LJ, Mountstephens, J, Teo, J 2020, Four-class emotion classification in virtual reality using pupillometry. Journal of Big Data, 7: 43. | ||
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