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International Conference on Management of Large-Scale System Development (MLSD)

作者： Inna Tulokhonova Svetlana Mikhaylova Soelma Danilova Department of Software Engineering and Artificial Intelligence East Siberia State University of Technology and Management Ulan-Ude Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

This article focuses on the problem of human impact on the natural environment and its solution by machine learning methods. The concept of carbon balance is key in assessing climate change on the planet, which is especially important in connection with global warming. The main causes of global warming are associated with anthropogenic emissions of carbon dioxide as a result of agricultural production, land use and burning of fossil fuels. One of the approaches to mitigate this environmental problem is to estimate the carbon balance based on neural network semantic segmentation of forests of terrestrial ecosystems. The proposed convolutional neural network (CNN) model is based on the U-Net architecture. This architecture surpasses traditional machine learning methods in solving computer vision and pattern recognition problems, and is also quite easy to learn. The input data of the model is a set of satellite images of the forest area. The images were obtained using the Sentinel-2 satellite, which provides high spatial resolution and a large number of spectral channels. A software prototype has been developed to implement the CNN model. Training experiments and calculations on changes in the carbon balance in forests led to the conclusion that the results of the study will contribute to the balanced management of reforestation and carbon deposition.

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Support System for Choosing an Individual Educational Trajectory at Vasyl' Stus Donetsk National University 9

Support System for Choosing an Individual Educational Trajec...

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9th IEEE International Conference on Problems of Infocommunications Science and Technology, PIC S and T 2022

作者： Chaikovskyi, Pavlo Vietrov, Oleh Baiev, Artem Teletska, Yana Strutovskyi, Maksym Vasyl' Stus Donetsk National University Educational and Practical Laboratory of Information Security Vinnytsia Ukraine Vasyl' Stus Donetsk National University Department of Applied Mathematics Vinnytsia Ukraine Vasyl' Stus Donetsk National University Interdepartmental Laboratory of Machine Learning and Intelligent Data Analysis Vinnytsia Ukraine

ISBN: (纸本)9798350398915

Members of Interdepartmental laboratory of machine learning and intelligent data analysis Vasyl' Stus Donetsk national university are developing a software that helps students choose minors and other additional subjects, as well as proper tooling for teachers and staff. Described process of development, its specifics and addressed direction and possible technologies that may serve the scaling of the system. © 2022 IEEE.

关键词： Gale-Shapley algorithm ICS individual educational trajectory

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Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multimodal data

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Signal Transduction and Targeted Therapy 2024年第9期9卷 4137-4148页

作者： Zifan Chen Yang Chen Yu Sun Lei Tang Li Zhang Yajie Hu Meng He Zhiwei Li Siyuan Cheng Jiajia Yuan Zhenghang Wang Yakun Wang Jie Zhao Jifang Gong Liying Zhao Baoshan Cao Guoxin Li Xiaotian Zhang Bin Dong Lin Shen Center for Data Science Peking UniversityBeijingChina Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Pathology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Radiology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina National Biomedical Imaging Center Peking UniversityBeijingChina Department of General Surgery Nanfang HospitalSouthern Medical UniversityGuangzhouChina Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor GuangzhouChina Department of Medical Oncology and Radiation Sickness Peking University Third HospitalBeijingChina National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Beijing International Center for Mathematical Research(BICMR) Peking UniversityBeijingChina Center for Machine Learning Research Peking UniversityBeijingChina

The sole use of single modality data often fails to capture the complex heterogeneity among patients,including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens,for the treatment of HER2-positive gastric cancer(GC).This modality deficit has not been fully considered in many ***,the application of artificial intelligence in predicting the treatment response,particularly in complex diseases such as GC,is still in its ***,this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive *** collected multi-modal data,comprising radiology,pathology,and clinical information from a cohort of 429 patients:310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors *** introduced a deep learning model,called the Multi-Modal model(MuMo),that integrates these data to make precise treatment response *** achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined ***,patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival(log-rank test,P<0.05).These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer,but also the potential and clinical value of our model.

关键词： immunotherapy HER2 cancer

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Stochastic Principles of Optimality in Games with Nature and Their Application in Investment Management 14

Stochastic Principles of Optimality in Games with Nature and...

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14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Gorelik, Victor Zolotova, Tatiana Moscow Pedagogical State University Department of Simulation Systems and Operations Research FRC CSC RAS Moscow Russia Financial University Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9781665412308

The principle of optimality in decision-making for games with nature, based on assessments of efficiency and risk, is proposed. In contrast to the traditional approach to the definition of a mixed strategy in game theory, this work considers the possibility of a correlation dependence of random values of payoffs for the original alternatives. © 2021 IEEE.

关键词： Game theory

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Using Convolutional Neural Network to Classify 2D EEG Scalp Topograms during Visual Task 5

Using Convolutional Neural Network to Classify 2D EEG Scalp ...

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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Korchagin, Sergey Maksimenko, Vladimir Hramov, Alexander Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia Institute of Information Technologies Mathematics and Mechanics Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

we developed an artificial neural network (ANN) classifier to analyze the cortical activity signals during visual information processing. We tested several ANN architectures and chose a convolutional neural network (CNN) with the Resnet50 topology. As a result, the trained CNN classifier achieved an accuracy of 74% on the data of newly recruited subjects. © 2021 IEEE

关键词： Convolutional neural networks

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Investor Risk Profile Determination Model

Investor Risk Profile Determination Model

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Victor Gorelik Tatiana Zolotova Department of Simulation Systems and Operations Research Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

An assessment of the investor’s risk profile is proposed as a risk coefficient in a model with a linear convolution of expected return and variance. The value of the risk coefficient is found from solving the optimization problem of the model with a limitation on profitability. The proposed method is implemented in the form of a software package using the example of the stock market.

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Dynamic Approach to the analysis of Financial Structure: Overcoming the Bank-Based vs Market-Based Dichotomy

Dynamic Approach to the Analysis of Financial Structure: Ove...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Konstantin Krinichansky Natalia Grineva Department of Financial Markets and Financial Engineering Institute of Financial Studies Financial University Under the Government of the Russian Federation Moscow Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

A conceptual approach to the typology of countries' financial development has been developed, which will allow government authorities to make more accurate macroeconomic forecasts and apply a strategic approach to economic development planning. Modeling was conducted to analyze the impact of macroeconomic indicators, dummy variables of economic crises, and the technological component variable on indicators of financial development and variables characterizing the structure of the financial system.

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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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Overview of Modern Forecasting Methods and Models 16

Overview of Modern Forecasting Methods and Models

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

作者： Ivanyuk, Vera Tsvirkun, Anatoliy Sunchalina, Anna Goroshnikova, Tatiana Sunchalin, Andrey Zholobova, Galina Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Bauman Moscow State Technical University Department of Higher Mathematics Moscow Russia V.A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Management of the Development of Large-Scale Systems Moscow Russia Financial University under the Government of the Russian Federation Faculty of International Economic Relations Moscow Russia Financial University under the Government of the Russian Federation Department of Mathematics Moscow Russia

The development of accurate forecasting models for financial time series is an essential area of research in finance and economics. The article describes modern models and forecasting methods. © 2023 IEEE.

ISBN: (纸本)9798350337907

关键词： Neural networks

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