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7th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2021

作者： Andriyanov, Nikita Korovin, Dmitry Financial University under the Government of the Russian Federation Data Analysis and Machine Learning Department Moscow Russia

ISBN: (纸本)9781665432177

The study focuses on the statistical analysis of information about diseases spreading. To obtain data, specialized application programming interfaces (APIs) in the Python language are used. data from Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) and data from the Oxford dataset OxCGRT were considered as data sources. On the basis of the data obtained, such indicators as detected cases, the proportion of deaths and the proportion of recovery are investigated. The analysis is for countries around the world. At the same time, data for TOP-9 countries are visualized. In addition, the work is devoted to the study of the impact of measures taken by the Government to contain the spread of the infection. data for the Russian Federation, France and the USA are being processed. Finally, a simulation model of migration and spread of the disease with open and closed borders between cities is considered. The analysis showed that the countermeasures taken, although they are tough and restrictive, have an effective effect on reducing the rate of spread of the infection. © 2021 IEEE.

关键词： Time series analysis

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Using Control Charts with Variable Control Limits in Statistical Process Control 5

Using Control Charts with Variable Control Limits in Statist...

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5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Zhuravlyova, Marina Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350309126

The procedure of building control charts with variable control limits based on nonparametric bootstrap percentile method is proposed. It makes possible to construct control chart of any statistic with unknown continuous distribution and involves a large number of calculations. The order of building one of the scalar statistics to be monitored for the group of mutually dependent quality characteristics is considered. Formulas for obtaining variable control limits based on percentile method are shown. The issue of identifying the structure of relationships between quality characteristics is raised. Proposed control chart, Hotelling T2 and Shewhart usual control charts and their modifications for variable control limits have been constructed using actual data of two characteristics of the rolled steel manufacturing process. It is concluded that the development of the proposed approach can be useful in statistical process control. © 2023 IEEE.

关键词： Control charts

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Determining the Genre of Music from a Melody Using machine learning and Parallel data Processing

Determining the Genre of Music from a Melody Using Machine L...

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2023 Seminar on Information Computing and Processing, ICP 2023

作者： Solodennikov, Danila A. Makoveichuk, Krystina A. Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350369830

This article describes the author's methodology for determining the genre of music from a melody using machine learning and parallel data processing. For determination is used dataset formed by the author, on the basis of which the model is trained and the genre is predicted on the test data. Yandex technologies, parallel programming, and Python data processing models are used in the work. There are also examples of training and test samples, model operation, data cleaning and dataset analysis methods. The work built neural network to identify genres and output results. The results vary from 35% to 50% and it should be noted that the dataset is rather small, so with bigger dataset we can await much higher result. © 2023 IEEE.

关键词： machine learning

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Improving Customer Relationship Management based on Intelligent analysis of User Behavior Patterns 13

Improving Customer Relationship Management based on Intellig...

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13th International Conference Management of Large-Scale System Development, MLSD 2020

作者： Monastyrskaya, Maria M. Soloviev, Vladimir I. Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9781728110943

A set of regression, cluster analysis, and association rule mining models, is proposed to search for patterns in user behavior regarding marketing campaigns taking into account user characteristics and financially significant metrics. Using the example of two organizations, it was possible to accurately predict the cost of customer acquisition (CPA, cost-per-Action). The use of the models proposed allows organizations to improve advertisement settings to increase online advertising efficiency. © 2020 IEEE.

关键词： Cluster analysis

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Rational Pricing of Fruit-Harvesting Robots 13

Rational Pricing of Fruit-Harvesting Robots

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13th International Conference Management of Large-Scale System Development, MLSD 2020

作者： Kuznetsova, Anna A. Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9781728110943

A methodology for evaluating the economic efficiency of fruit harvesting robots and rational pricing for such robots, so that they would be in demand by horticulturists, is presented. It is shown that the main obstacle to the broad implementation of robotic technology for fruit crop harvesting is the overpricing of existing fruit-picking robot prototypes. © 2020 IEEE.

关键词： Economic efficiency

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Estimating Object Coordinates Using Convolutional Neural Networks and Intel Real Sense D415/D455 Depth Maps 8

Estimating Object Coordinates Using Convolutional Neural Net...

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8th International Conference on Information Technology and Nanotechnology, ITNT 2022

作者： Andriyanov, Nikita Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

The paper considers the problem of estimating the coordinates of objects detected from a video sequence of images from an Intel Real Sense cameras. An object detection system was developed by using the YOLOv5 convolutional neural network. An algorithm for estimating detected objects coordinates in three-dimensional space was implemented based on the joint processing of information from depth maps and the pixel coordinates of objects in the image. The conducted comparative analysis with the method of instrumental measurements showed a low root mean square error of the proposed algorithm. In addition, comparative studies have been conducted for the Intel Real Sense D415 series and D455 series cameras. © 2022 IEEE.

关键词： Cameras

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Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation 36

Pitfalls of Epistemic Uncertainty Quantification through Los...

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36th Conference on Neural Information Processing Systems, NeurIPS 2022

作者： Bengs, Viktor Hüllermeier, Eyke Waegeman, Willem Germany Munich Center for Machine Learning Germany Department of Data Analysis and Mathematical Modeling Ghent University Belgium

ISBN: (纸本)9781713871088

Uncertainty quantification has received increasing attention in machine learning in the recent past. In particular, a distinction between aleatoric and epistemic uncertainty has been found useful in this regard. The latter refers to the learner's (lack of) knowledge and appears to be especially difficult to measure and quantify. In this paper, we analyse a recent proposal based on the idea of a second-order learner, which yields predictions in the form of distributions over probability distributions. While standard (first-order) learners can be trained to predict accurate probabilities, namely by minimising suitable loss functions on sample data, we show that loss minimisation does not work for second-order predictors: The loss functions proposed for inducing such predictors do not incentivise the learner to represent its epistemic uncertainty in a faithful way. © 2022 Neural information processing systems foundation. All rights reserved.

关键词： Probability distributions

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Efficient image representation for object recognition via pivots selection

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Frontiers of Computer Science 2015年第3期9卷 383-391页

作者： Bojun XIE Yi LIU HuiZHANG Jian YU Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071000 China

Patch-level features are essential for achieving good performance in computer vision tasks. Besides well- known pre-defined patch-level descriptors such as scalein- variant feature transform （SIFT） and histogram of oriented gradient （HOG）, the kernel descriptor （KD） method [1] of- fers a new way to ＂grow-up＂ features from a match-kernel defined over image patch pairs using kernel principal compo- nent analysis （KPCA） and yields impressive results. In this paper, we present efficient kernel descriptor （EKD） and efficient hierarchical kernel descriptor （EHKD）, which are built upon incomplete Cholesky decomposition. EKD au- tomatically selects a small number of pivot features for gener- ating patch-level features to achieve better computational effi- ciency. EHKD recursively applies EKD to form image-level features layer-by-layer. Perhaps due to parsimony, we find surprisingly that the EKD and EHKD approaches achieved competitive results on several public datasets compared with other state-of-the-art methods, at an improved efficiency over KD.

关键词： efficient kernel descriptor efficient hierarchi-cal kernel descriptor incomplete Cholesky decomposition,patch-level features image-level features

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A machine learning Approach to Exploring Ecological Determinacy of Socially Significant Diseases in Moscow 16

A Machine Learning Approach to Exploring Ecological Determin...

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Marunko, Anna Zolotova, Tatiana Financial University under the Government of the Russian Federation Faculty of Information Technology and Big Data Analysis Moscow Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350337907

A study of the relationship between some selected environmental indicators and the presence of socially significant diseases in the population of Moscow was carried out. A statistically significant correlation was confirmed. machine learning models were built during the regression analysis. A web interface was developed to automate the processes of predicting the population's morbidity. © 2023 IEEE.

关键词： Ecology

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Using Generative Models to Improve Fire Detection Efficiency 10

Using Generative Models to Improve Fire Detection Efficiency

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10th International Conference on Information Technology and Nanotechnology, ITNT 2024

作者： Andriyanov, Nikita Kim, Alexandr Fao, Xenin Data Analysis And Machine Learning Department Financial University Under The Government Of The Russian Federation Moscow Russia Data Science Department Huazhong University Of Science And Technology Huazhong China

ISBN: (纸本)9798350372939

The paper discusses generative artificial intelligence technologies used to improve the efficiency of fire detection in satellite images. Different detector architectures are proposed and compared in terms of accuracy. It is found that the use of generative models to expand the training sample is on average 1-2% more effective than the use of standard augmentations. The combined method of expanding the training image base can improve the accuracy by 4-5%. In addition, the right approach in generative prompts can also improve the accuracy metrics of the models. © 2024 IEEE.

关键词： Generative adversarial networks

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