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Your search keyword '"Gafarov, Fail"' showing total 3 results
Start Over Author "Gafarov, Fail" Topic machine learning
3 results on '"Gafarov, Fail"'

1. Research and forecasting of educational process parameters by using artificial intelligence

Author

Galimyanov, Anis F., Gafarov, Fail, Khairullina, Liliya, and Muzafarova, Alina

Subjects
Big data, ROC-curve, machine learning, educational data mining, Science, neural, neural networks, artificial intelligence
Abstract

In this paper we present the results of an interdisciplinary research based on the application of big data, date science, artificial intelligence and machine learning methods in educational analytics. Artificial intelligence techniques applied for the analysis of depersonalized data stored in the information and analytical system "E-education in the Republic of Tatarstan" from 2015 to 2020. BigData technologies were used in this work to perform high-performance computing related to initial preprocessing of raw data in computation cluster. By using the methods of artificial intelligence, we modelled one of the most important stages in the formation of the educational trajectories of schoolchildren, associated with the fact that after the 9th grade, schoolchildren either continue their studies in high school (grades 10-11), or move to the professional educational organizations. As the input data for neural network training, we used a vector containing the average marks for all quarters of pupils, obtained by using high-performance Dask-based cluster data processing system from initial raw data. We concluded that multi-layer neural network with two hidden layers was able to predict the pupil’s pass to 10th grade, and achieved the best performance with classification accuracy exceeding 70%. Also, the performance of trained neural network had been analyzed by visualization of Receiver Operator Characteristic (ROC)-curve and by calculation of recall, precision, specificity and area covered by the ROC-curve (AUX) parameters.

Published
2022

2. Identification of Key Factors in the Formation of an Individual Trajectory of Teacher Professional Development in Digital Environment Based on Big Data.

Author

Gafarov, Fail M. and Galimyanov, Anis F.

Subjects
*PROFESSIONAL education, *DIGITAL technology, *EDUCATION, *BIG data, *MACHINE learning
Abstract

The relevance of the study is determined by the need to build new methods for the study of educational activities. Currently, electronic document management has become a daily occurrence and all documents, including diaries, class journals and annual reports, exist in electronic form. These digital data occupy a very large memory and are constantly updated both in volume and in nomenclature. There are special methods for processing unstructured and partially structured data called big data. With proper processing, this data can be used in the further optimal design of the educational process. In this regard, this article is aimed at revealing the features of the use of big data methods in the educational process, as well as the rationale for the use of machine learning methods and neural networks to study the hidden patterns in educational process. The use of big data and machine learning methods will help determine the key factors in the formation of the individual trajectory of the teacher's professional development in the digital environment. [ABSTRACT FROM AUTHOR]

Published
2020
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3. Bio-plausible simulation of three monoamine systems to replicate emotional phenomena in a machine.

Author

Leukhin, Alexey, Talanov, Max, Vallverdú, Jordi, and Gafarov, Fail

Subjects
EMOTION recognition, MONOAMINE transporters, THREE-dimensional imaging, COMPUTER simulation, MACHINE learning
Abstract

Abstract In this paper we present the validation of the three-dimensional model of emotions by Hugo Lövheim the "cube of emotion" via neurosimulation in the NEST. We also present the extension of original "cube of emotion" with the bridge to computational processes parameters. The neurosimulation is done via re-implementation of dopamine (DA), serotonin (5-HT) and noradrenaline (NA) subsystems of a rat brain to replicate 8 basic psycho-emotional states according to the "cube of emotion". Results of neu-rosimulations indicate the incremental influence of DA and NA over computational resources of a psycho-emotional state while 5-HT decreases the computational resources used to calculate a psycho-emotional state. This way we indicate the feasibility of the bio-plausible re-implementation of psycho-emotional states in a computational system. This approach could be useful extension of decision making and load balancing components of modern artificial agents as well as intelligent robotic systems. [ABSTRACT FROM AUTHOR]

Published
2018
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