Assessment of the level of business readiness for digitalization using marketing and neural network technologies

Yasynska, Nadiia; Fomichenko, I.; Voloshinа, О.; Byvsheva, L.; Krikunenko, Ekaterina

doi:10.21511/im.15(3).2019.04

Cited by 10 publications

(6 citation statements)

References 6 publications

(2 reference statements)

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“…In terms of practical applications, although the number of projects on the entrepreneurial project information platform continues to increase, causing serious information overload to users, the application of the entrepreneurial project recommendation system has not kept up and only stays in the stage of non-personalized recommendation. Therefore, the research problem of this paper is not only the theoretical value but also has certain practical significance [ 21 – 25 ].…”

Section: Related Workmentioning

confidence: 99%

Research on the Evaluation of Reformation and Revolution System for Universities Based on Neural Network

Ma¹,

2022

Computational Intelligence and Neuroscience

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Accurately evaluating the working conditions of college revolution and reformation system personnel is currently a hot issue in the field of revolution and reformation system research. Based on the neural network architecture, this paper constructs a model for the system and obtains relevant quality evaluation indicators through simulation experiment analysis. According to the quality evaluation index framework of the revolution and reformation system, the experimental platform selects 7 universities in the region, 6 of which are samples, and 1 (S University) is the research target, and the scores of each index are calculated using MATLAB auxiliary software. The simulation process starts from the characteristics of the actual neural network model; selects 17 evaluation impact indicators, including 3 basic indicators and 14 technical indicators based on historical data; and uses the factor recursive method to improve the neural network and establish an evaluation model. Then use the collected specific data to train a BP neural network with a structure of 21 × 11 × 1, continuously adjust the weights and thresholds of the neural network until the standard error requirements are met, finally verify the scientificity of the evaluation model, and compare the actual output value with the expected output value for comparison. The experimental results show that the model input data redundancy rate is reduced to 0.136, and the network training time delay is reduced to 413 ms, which improves the computing power of the network model of the reformation and revolution system in colleges and universities. The use of neural network data to reduce dimensionality effectively promotes the reformation and revolution system in universities.

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Section: Related Workmentioning

confidence: 99%

Research on the Evaluation of Reformation and Revolution System for Universities Based on Neural Network

Ma¹,

2022

Computational Intelligence and Neuroscience

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“…A possible way to overcome these difficulties is the choice of other methods based on the available reliable information. Such methods are used in [4], which shows that the development of the digital economy by improving marketing tools and monitoring their effectiveness for business needs are not well understood. It is recognized that the expected positive results from the digitalization of the global economy lie in operational efficiency; reducing the number of staff; transformation time; gaining competitive advantages; standardization; improved analytics.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Explication of the role of digital technologies in marketing management of a modern company

Dunayev

Hromov

Tymchenko

et al. 2022

EEJET

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This study considers the use of digitalization technologies in the management of marketing of a commercial company. As a result, the problem of bringing empirical knowledge about the importance of digitalization technologies in the company's marketing management to a theoretical model has been solved. Digitalization technologies for implementation in marketing activities were selected and the decomposition of marketing management in a commercial company was performed. On this basis, a theoretical model for assessing the impact of digitalization technologies on the marketing management of a commercial company has been developed. The results of an empirical study based on the developed theoretical model proved the truth of the hypothesis about the positive impact of the spread of the use of digitalization technologies on the marketing management components of commercial companies. The choice of specific digitalization technologies is explained by the essence of marketing as a management activity. Structural decomposition of the marketing management process is explained by traditional management functions and clarifies the criteria for choosing technologies. The results of calculations according to the formed theoretical model are explained by the development of the platform economy and ecosystems, which involve the studied big data technologies, cloud computing and social media, as well as blockchain. The methodical approach to substantiating the role of digitalization technologies proposed in this study is characterized by the use of fundamental provisions on cybernetic systems, management theory, and modern marketing concepts. The obtained results make it possible to find out the presence and direction of the effects of the spread of the use of digitalization technologies in the company's marketing management and make informed decisions about the business digitalization strategy

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“…Modulation information is reflected in the fluctuation of these instantaneous parameters [9]. The characteristics of some signals often exist in the changes of the spectrum, so the characteristics can also be found by observing the spectrum to complete modulation recognition [10].…”

Section: Common Communication Signal Identification Featuresmentioning

confidence: 99%

Target Signal Recognition Method for 5G Communication Supporting Machine Learning

2022

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The identification of 5G communication target signal type may be the premise and foundation of modulation mode identification, signal demodulation and other links. Therefore, the identification of 5G communication target signal type has become an indispensable technical link in the intelligent signal processing system. In order to solve the shortcomings of existing agricultural machine vision system research, this paper discusses the composition and key technologies of agricultural machine vision system and machine learning algorithm, and then simply discusses the hardware selection and software development environment of the system in which the algorithm proposed in this paper is applied. And the overall results of agricultural machine vision monitoring system are designed and discussed. RCNN and K-means algorithms in machine learning are used to study the identification and classification of seedlings in images. Finally, through the experimental analysis of selected samples, it is known that the accuracy of RCNN and K-means algorithm in image recognition detection in agricultural machine vision monitoring system is up to 94.25%. Therefore, it is verified that machine learning algorithm has high practical value in agricultural machine vision system.

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Assessment of the level of business readiness for digitalization using marketing and neural network technologies

Cited by 10 publications

References 6 publications

Research on the Evaluation of Reformation and Revolution System for Universities Based on Neural Network

Research on the Evaluation of Reformation and Revolution System for Universities Based on Neural Network

Explication of the role of digital technologies in marketing management of a modern company

Target Signal Recognition Method for 5G Communication Supporting Machine Learning

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