Neural Network Analysis Method Research Articles

The content analysis of regional entrepreneurship

Subject. The study determines directions for developing the business activity in the Russian regions. Methods. The study is based on the systems approach and methods of statistical, neural network and cluster analysis. Results. I review the current aspects of the regional business development when business rules are changed due to the COVID-19 pandemic. The article scrutinizes the concept of entrepreneurship in terms of historical development and legal status. Having conducted the content analysis of the business activity in a lagging region of the Russian Federation, I found its highest concentration, with small businesses (and microbusinesses) taking the lead and maintaining the employment. Businesses tend to trade in goods of other producers, thus decreasing the average headcount for smaller businesses, including microbusinesses. Studying the hierarchy of economic activity types small businesses engage in by share of investment in the capital stock (in terms of new and imported fixed assets), I note the unprofitable investment n such foreign trade activities, such as Agriculture, forestry, hunting, fisheries and fish farming, and Construction. The dynamism of small businesses drops earlier, longer and deeper if compared to sole proprietors. In a lagging region, smaller business forms can evolve only if they deal with trade. Conclusions and Relevance. The content analysis of the Russian regional business gave an understanding of its development limits from perspectives of a lagging region, which determines the growth rates of business activity in Russia. The content analysis reveals high growth rates of trade in goods that smaller businesses sell, without being their producers. The findings contribute to the knowledge and competence of specialists working for the Russian Ministry of Economic Development to make consistent managerial decisions concerning the distribution and redistribution of investment resources.

Problems of Analyzing Socio-Political Content of Internet Resources Based on Neural Network Technologies

Ensuring information security requires identifying undesirable information content of Internet resources. Semantic and lexicological diversity of Internet content requires improvement of methods of neural network analysis of natural language texts (NLP). The problem is complicated by the presence of” information garbage”, which is a specific information noise that complicates the task of classifying texts. Well-known NLP technologies using artificial neural networks (ANN) include substantiation of the structure and construction of a subject-oriented database of text data bodies, frequency analysis and construction of dictionaries. To identify semantic content and latent threats, a dense vector representation of the analyzed texts in a multidimensional space (embedding) is justified. The authors substantiate a modified NLP approach to identifying sociocultural and cyber threats, contained in the information content of Internet resources. To justify and research the ANN architecture and hyperparameters focused on the socio-political content under study, the ANN family was built in Python 3. The ANN architecture included combinations of fully connected, convolutional, and/or recurrent layers. The number of neurons of the recognizing fully connected layer with the “softmax [[CHECK_DOUBLEQUOT_ENT]] activation function (or sigmoid in multiple classification) was taken by the number of classes marked in the text corpus.

Research on Text Sentiment Analysis Based on Neural Network and Ensemble Learning

In view of the fact that a single sentiment classification model may be unstable in classification, this paper attempts to propose a joint neural network and ensemble learning sentiment analysis method. After data preprocessing such as word segmentation on the text, combined with document vectorization method for feature extraction, we then use four basic classifiers including long short-term memory network, convolutional neural network, a serial model combining convolutional neural network and long short-term memory network, and support vector machine to train model, respectively. Finally, the integration is carried out by stacking ensemble learning. The experimental results show that the integrated model significantly improves the accuracy of text sentiment analysis and it can effectively predict the sentiment polarity of the text.

Analyzing the "Strategy of Socio-Economic Development of the Chuvash Republic until 2035" content

Subject. The article addresses a strategy for socio-economic development of the region. Objectives. The purpose is to define contradictions and opportunities to achieve the expected results of the "Strategy of Socio-economic Development of the Chuvash Republic until 2035". Methods. The study rests on the systems approach, using the methods of statistical, neural network, and cluster analysis. Results. The statistical analysis of trends in expected outcomes of the Strategy implementation enabled to build a median hierarchy of their growth rates, where the indicator of an increase in the number of visits to cultural institutions is a priority in setting the goals for the region’s development. The neural network analysis demonstrates the importance of the growth rate of real accrued wages of employees of organizations and the insignificant value of the increase in the number of visits to cultural institutions for effective achievement of all objectives of the Strategy. The cluster analysis shows the importance of growth rates of indicators of the proportion of organizations engaged in technological innovations, and the proportion of shipped innovative products. The analysis of growth rates of GRP and expenditures of the consolidated budget of the Chuvash Republic reveals a decrease in the cyclical lag of the first dynamic pattern from the second one. Conclusions. For the Chuvash Republic, a strategic priority is to overcome the GRP growth limit through the innovative development of backbone areas of economic activity.

Analyzing the dynamics of administrative indicators in Russia

Subject. The article discusses the dynamics of the Russian indicators of administration. Objectives. I identify constraints that determine administrative criteria. Methods. The study is based on the systems approach and methods of statistical, cluster and neural network analysis. Results. The article spotlights the stewardship of Russia’s economy today and theoretical considerations on general administrative criterion. I analyzed trends in the Russian administrative indicators, referring to six general aspects of management (Worldwide Governance Indicators, World Bank Group). Using the cluster analysis of growth rates of the Russian administrative indicators, I found major and crucial clusters. Conducting the neural network analysis, I understood the hierarchy of priorities, with the governmental efficiency being the most important one. The supremacy of law, political stability and no violence/terrorism were found to of the least significance. Having evaluated the asymmetry of trends in the Russian administrative indicators against the average, I identified the priority, that us the governmental efficiency, which turns to be a determining criterion of management. Conclusions and Relevance. As a result of the study, I understood what hampers the dynamics of the Russian administrative indicators by determining administrative criteria. I especially point out the possibility of raising the governmental efficiency to maintain the political stability and prevent violence/terrorism by neglecting the supremacy of law, regulatory quality and simulating the anti-corruption activity. The findings contribute to the necessary scope of governmental authorities’ competence to make administrative decisions on the effective stewardship of Russia.

The substance of the contemporary Central Bank of the Russian Federation

Subject. The article focuses on the substance of the Central Bank of the Russian Federation. Objectives. The study analyzes trends in aggregated financial and economic indicators of today’s Russia, and reveals the substance of the Central Bank. Methods. The study is based on the systems approach and methods of statistical, neural network and cluster analysis. Results. I demonstrate the positioning of the Central Bank and its function. The Central Bank was found to depend on the market, being, on the one hand, its vehicle, and, on the other hand, its product (as part of monetary relations). Therefore, the substance of the Central Bank shows up in the monetary relations. The article illustrates the substance of the Central Bank, which is to ensure the stable growth in the accounting balance notwithstanding a drop in the national exchange rate, finance the cash ratio, maintain a growth in the interbank interest rate and yield on government bonds. The above functions shape the monetary relations. The official inflation targeting policy of the Central Bank should be viewed through the real effective exchange rate. Conclusions and Relevance. Understanding the contemporary substance of the Central Bank unveils the mechanism of monetary relations and opportunities for maintaining the stability of the Russian capitalism within the dynamics of aggregated financial and economic indicators. The findings contribute to the existing knowledge and advance the competence of public authorities to make administrative decisions on the regulatory responsibility of the Central Bank of the Russian Federation for its performance.

Analysis of the Russian Federation constituent regions’ sustainable development goals achievement

The article analyzes the Russian Federation constituent regions’ sustainable development goals (SDGs) achievement. Identified are the determinant Russian regions, the driving force and the reason for their coherence in achieving sustainable development goals. The study is based on a systematic approach using the method of statistical, neural network and cluster analysis. Results. The study has revealed a contradiction expressed in priority of “SDG 7. Fixed capital investments by areas of investment in terms of “Production of electricity from renewable energy sources, including generated by solar, wind, geothermal power plants, as well as activities to ensure their performance”” line of business and underperformance in terms of “SDG 16. Russia’s position in the rating of the World Bank’s Doing Business project in terms of the “Getting credit” indicator. The work shows priority in relation to “SDG 3. Life expectancy at birth”, which is implemented through national projects. Analysis of the large Russian clusters regional importance identified the Moscow region by “SDG 1. Proportion of the population living below the national poverty line” and “SDG 3. Life expectancy at birth”. The analysis of interregional ties revealed determinant regions, the existence of which is supported by the dynamics of the developing Russian market, which is far behind countries with developing economies.

Taking AI Neural Network to Analyze Labor Rights and Labor Disputes

Exploring labor rights on the cognition of relationship with labor dispute is not only from the direct role of workers and HR, but also from the fight for labor rights of college students. However, many labor disputes in labor process are mainly related to labor rights. Therefore, we explore the relationship between labor rights and labor disputes, use neural network analysis and questionnaire survey method, and execute AI combining with big data analysis tools to collect first-hand data from two aspects of college students, workers in the industry, HR and labor dispatch personnel. Finally, the results show that: (1) there is a negative correlation between social security and labor disputes; (2) there is a negative correlation between labor safety and labor disputes; (3) there is a negative correlation between wages and labor disputes; (4) there is a negative correlation between labor contract signing and labor disputes; (5) there is a positive correlation between rights awareness and labor disputes.

Comparison of Classification Performances of Mathematics Achievement at PISA 2012 with the Artificial Neural Network, Decision Trees and Discriminant Analysis

This study aims to compare the performances of the artificial neural network, decision trees and discriminant analysis methods to classify student achievement. The study uses multilayer perceptron model to form the artificial neural network model, chi-square automatic interaction detection (CHAID) algorithm to apply the decision trees method and linear discriminant analysis. The performance of each method has been investigated in different sample sizes when classifying into different numbered subgroups. The study has revealed that the artificial neural network has the best performance in large, medium and small sample sizes when classifying into six, three and two subgroups. In the very small sample size, which has homogeneous variance-covariance matrices, the discriminant analysis performs the best, while in the very small sample size, which does not have homogeneous variance-covariance matrices, it is the discriminant analysis which performs the best when classifying into six subgroups and the artificial neural network performs the best when classifying into two and three subgroups. Considering the performances of the methods with respect to sample size, it can be concluded that as the sample size gets smaller, the performance of the decision trees method gets worse, whereas the performance of the discriminant analysis method improves. No correlation of this kind has been found with regard to the artificial network method.

Determining the conditions for sustainable development of the Russian economy

Subject. The article addresses conditions for sustainable development of the Russian economy. Objectives. The purpose is to link the limit of sustainability of the Russian economy with required conditions. Methods. The study draws on the systems approach, using the methods of statistical, neural network, and cluster analysis. Results. I describe scientific ideas about sustainable development of economy, which have been developed in theoretical and practical activities. The World Bank data on environment, social affairs and governance on 17 key sustainable development topics, covering environmental, social and management categories, are used to determine conditions for sustainability of the Russian economy. The neural network analysis of the importance of indicators’ rate of increment shows a hierarchy of priorities. The cluster analysis of indicators’ rate of increment identifies significant clusters. The analysis of key priorities of sustainable development of the Russian economy enables to reveal significant deviations in political stability and in access to electricity. Conclusions. Conditions for sustainable development of the Russian economy are determined by their proximity to the United Nations Sustainable Development Goals. Identifying these conditions in the globalizing world creates an understanding of the real limit and opportunities of the country's development.

Financial basis for the modern Russian economy

Subject. The article investigates the financial basis of the economy of contemporary Russia. Objectives. The focus is on identifying the financial basis of the modern Russian economy, the limits of economic activity, and conditions for economic development. Methods. The study rests on the systems approach, using the methods of statistical, neural network, and cluster analysis. Results. The paper unveils the need to study the financial basis of the modern Russian economy, which is caused by the duration of the transition from the socialist to capitalist socio-economic formation. The assessment of the growth rate of a wide range of statistical data reveals the financial basis of the modern Russian economy, i.e. direct investment, debt instruments, securities other than shares included in broad money, international liquidity, aggregate reserves, money supply. The existing financial basis limits the growth of gross value added, the increase in gross capital formation, etc. It also determines conditions for economic development, like high price of WTI crude oil and low price of gold, high gold price and the strength of the United States dollar. In the current structure of the Russian economy, both options lead to lower final consumption expenditure of the private sector and to limitation of stocks. Conclusions. The understanding of intermediate results of the long transition from socialist to capitalist socio-economic formation enables to define the boundary of the existence of the modern Russian economy. This boundary is generated by its raw material structure and financial basis.

The financial and economic substance of the contemporary Russian capitalism

Subject. I herein focus on the capitalism in Russia today. Objectives. The study is an attempt to determine what distinguishes the financial and economic substance of the Russian capitalism today. Methods. The study relies upon the systems approach and methods of statistical, neural network and cluster analysis. Results. The article reveals the difference of trends in general indicators of financial and business activities in capitalist Russia, socialist China and the imperialist USA. I discover development distinctions of the Russian economy, such as high growth rates of money supply, considerable simultaneous drop in foreign exchange rates, moderate debts of the Russian households and minimum wages, low unemployment rate, low growth rate of the current account balance to GDP as compared with the devaluation rate of the national currency. Having conducted the neural network analysis of growth rates of the money supply, solvency ratios and profitability of entities, financial results of the Russian credit institutions, I found median values of growth rates. Negative values were captured for the return on assets, return on sale of goods, products, work, services, and the equity-to-total assets ratio. Positive ones were found for total profit derived by operational credit institutions, losses and the percentage of unprofitable credit institutions. As the cluster analysis of growth rates of households’ consumption spending shows, there is a palpable correlation between food products and alcohol-free beverages, alcohol beverages, tobacco, clothes, shoes, communications. Conclusions and Relevance. The financial and economic substance of the contemporary Russian capitalism helps financial institutions and legislature consistently set up the concept and principles for retaining capitalism in Russia, considering the marginal proportions of the money supply growth to the unemployment rate, minimal wages, debt and consumption spending of households, which are adjusted within the possible divergence of the foreign exchange rate and current account balance to GDP. The findings constitute the necessary scope of governmental authorities’ competence to make managerial decisions for keeping the capitalist vector of the Russian society and economy.

Position of Russia in economic globalization

Subject. The article considers the position of Russia in economic globalization. Objectives. I focus on determining the position of Russia in economic globalization. Methods. The study rests on the systems approach, using the methods of statistical, cluster, and neural network analysis. Results. A slowdown in globalization has caused changes in the existing world economic relations. Russia has taken an active position among developed countries, amid a decline in the role of Chinese exports in the world. I revealed that in the context of the US trade war with China and the strengthening of protectionism, developing countries assumed the risks of loss of capital and recession. The position of Russia in economic globalization is connected with the place of BRICS in the new world order. The new conditions of globalization made it necessary for Russia to initiate the creation of new geopolitical communities, in addition to BRICS, which should include Denmark, Singapore and Slovenia. The findings form new competencies of State authorities for making managerial decisions to maintain a stable position and image of Russia in the process of economic globalization. Conclusions. To maintain a stable position and image of Russia in the context of economic globalization, the modern objective reality requires both Russia’s participation in and initiation of various geopolitical communities. The said participation will ensure that Russia’s certain position and image is maintained in the economic globalization.

Predicting areas with ecotourism capability using artificial neural networks and linear discriminant analysis (case study: Arasbaran Protected Area, Iran)

In this study, the common systematic approach in Iran as well as a multilayer perceptron neural network were used to evaluate the ecological capability of the area for ecotourism. The performance of the artificial neural network (ANN) and linear discriminant analysis (LDA) method in the prediction and ranking of areas with ecotourism capability were also compared. Based on the results obtained, the ANN with an overall accuracy of 97% outperformed LDA (overall accuracy of 86%) in terms of the prediction and classification of recreational areas. Therefore, for each class, the ANN with an accuracy, precision, and sensitivity of 98%, 94.33%, and 86.67%, respectively, outperformed the LDA with the corresponding values of 90.67%, 55.33%, and 40.33%, respectively. Based on the ANN-modeled map, 0.17%, 10.09%, and 89.74% of the area were shown to belong to intensive recreation class 2, extensive recreation class 2, and the not suitable for recreation class, respectively. Therefore, the ANN functions well with higher accuracy for modeling and classification of areas with ecotourism capability compared to LDA.

Performance Analysis of Bentonite-PVA Fiber Cement-Based Composites for Building Based on BP Neural Network

The paper will use BP neural network analysis method to study the thermal conductivity of bentonite and its influencing factors as a system. The heat conduction of bentonite was used as the output of the system, and its influencing factors were used as the system input to simulate. The corresponding simulation model was established to verify the thermal conductivity data. In addition, the analysis of the mechanical properties of the bentonite-PVA fiber cement-based composite materials for construction has not only laid a theoretical and realistic foundation for the prediction and simulation of the thermal conductivity of bentonite, but also has opened up the mechanical properties of the bentonite-PVA fiber cement-based composite materials a new path.

Transceiver error reduction by design prototype system based on neural network analysis method

<p><span>Code words traditional can be decoding when applied in artificial neural network. Nevertheless, explored rarely for encoding of artificial neural network so that it proposed encoder for artificial neural network forward with major structure built by Self Organizing Feature Map (SOFM). According to number of bits codeword and bits source mentioned the dimension of forward neural network at first then sets weight of distribution proposal choosing after that algorithm appropriate using for sets weight initializing and finally sets code word uniqueness check so that matching with existing. The spiking neural network (SNN) using as decoder of neural network for processing of decoding where depending on numbers of bits codeword and bits source dimension the spiking neural network structure built at first then generated sets codeword by network neural forward using for train spiking neural network after that when whole error reached minimum the process training stop and at last sets code word decode accepted. In tests simulation appear that feasible decoding and encoding neural network while performance better for structure network neural forward a proper condition is achieved with γ node output degree. The methods of mathematical traditional can not using for decoding generated Sets codeword by encoder network of neural so it is prospect good for communication security. </span></p>

The Forecasting Sales Volume and Satisfaction of Organic Products through Text Mining on Web Customer Reviews

The purpose of this study was to predict the online sales volume for organic products, identify important factors for selling organic products, and suggest web marketing strategies for organic product sales. Through the review of organic products on Taobao’s platform, the emotional analysis method is used to divide the review of crawling organic products into positive reviews and negative reviews. Using the Latent Dirichlet Allocation (LDA) method, extracting keywords, identifying important factors for selling organic products, using online survey methods and regression analysis methods, obtaining customers’ purchase intentions, and suggesting web marketing strategies for organic product sales, and by collecting data on organic products’ price, current price, free delivery, sales volume, number of customer reviews, customer reviews, organic labeling, and product fans on Taobao’s platform, the neural network analysis method is used to predict the online sales volume for organic products. This study found that packaging design, nutritional information, food quality, delivery risk, freshness, and source risk are the important online factors in the buying of organic products and the products’ fans, price discount, and number of customer reviews affected the sales volume. Therefore, the promotion of online services and logistics can be used to increase the sales of organic products. This research has an important role in promoting the sale of organic products and improving consumer satisfaction, providing consumers with safe and reliable products, and at the same time has important significance for promoting sustainable development.

Prediction of Short-term PV output power Based on PCA-Stacking under different weather conditions

With the continuous expansion of photovoltaic scale, the accurate prediction of photovoltaic power generation is increasingly important for grid dispatching and grid optimization operations. In this paper, the photovoltaic power generation mainly uses meteorological factors and historical data as the input and output of the neural network. The input quantity is large, the data is redundant, and the network is difficult to converge, which always has a great adverse effect on the accuracy of photovoltaic output prediction. Firstly, different weather types are classified according to the trend graphs of different weather types. Principal components analysis (PCA) is used to analyze less comprehensive features from multiple meteorological factors and reduce the input of predictive models. At the same time, aiming at the problem that the prediction accuracy of a single prediction model such as the existing neural network and wavelet analysis method is limited, the idea and method of integrated learning are introduced, and a short-term prediction method based on Stacking method combined with SVM and Xgboost is proposed. Compared with the single model of SVM and Xgboost, the results show that the proposed method has a significant improvement compared with the accuracy of a single prediction model.

Conditions and possibilities for symbiosis of Russia with developed countries

Subject. The article considers the symbiosis of Russia with developed countries. Objectives. The purpose is to identify conditions and possibilities for the symbiosis of Russia with advanced economies to establish a process of concentrated internationalization of financial capital for the market economy development completion, formation of full-fledged capitalism with stable dynamics of productive forces. Methods. The study rests on the systems approach, using the methods of descriptive statistics, neural network, nonparametric and cluster analysis. Results. The study reveals favorable conditions for the symbiosis of Russia with developed countries. They appear due to low requirements to the volume and connectivity of attracted high-tech capital. This enables to use the potential of small countries, motivating them to satisfy their ‘resource hunger’. Russia's transition to the said symbiosis is hindered by the policy of smooth devaluation of the national currency, which is used to increase the growth rate of total government expenditure and current GDP, and adversely affects the growth rate of gross national saving. Conclusions. The grounds for emergence of conditions and opportunities for the symbiosis of Russia with developed countries include the high values of financial development and low economic growth in Russia against the declining growth rates of the world economy and weakening of globalization process. The basis of Russia's symbiosis with developed countries is the mutually agreed necessity to internationalize capital in the form of investment financial institutions, which increase its concentration in production for the development of productive forces in conditions of the increasing risk of global recession.

Analyzing the impact of the financial system on the consistency of sustainable development goals in the Russian economy

Subject. The article reviews the modern financial system in Russia. Objectives. The study determines how the financial system currently influences the consistency of sustainable development goals in Russia. Methods. The study is based on the systems approach and methods of descriptive statistics, cluster, neural network and non-parametric analysis. Results. The article provides the rationale for addressing the impact of the financial system on the consistency of sustainable development goals in the Russian economy. We consider the set of financial system functions in terms of inherent abilities of finance to coordinate goals and tasks of the predicted development of economic, social and environmental systems. Evaluating the impact of the modern financial system on the consistency of sustainable development goals in the Russian economy, we discover that the Russian financial system operates in a metronome mode. The metronome model helps keep the speculative income stable notwithstanding stock exchange trading in Euro against ruble by changing the volume of circulating cash and high rate of return, federal loan bonds. In the Russian economy, the consistency of sustainable development is influenced by a growth in GDP per capita, foreign direct investment, forest and coal rent, the number of adolescents missing out of school, percentage of job places occupied in national parliament by women, and a growth in the urban population. Conclusions and Relevance. Nowadays, Russia’s financial system can coordinate how sustainable development goals are attained, since there is an excess liquidity of the speculative income from stock exchange trade and high rate of return of federal loan bonds. The findings enrich the knowledge of the way the financial system influences the economic development, raising new competencies for managerial decision-making on global goals of sustainable development and improvement of the people’s wellbeing and protection of the planet.