Dynamic Artificial Neural Networks as Basis for Medicine Revolution

Yasnitsky, Leonid N.; Dumler, Andrey; Черепанов, Ф. М.

doi:10.1007/978-3-030-02351-5_40

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…The ANN approach thus uses a probabilistic white-box technique, able to capture high-order relationships between sets of variables, with respect to classification. This method exhibits excellent discriminatory power, when compared with conventional methods (Abiodun et al, 2018;Yasnitsky et al, 2019;Rodríguez-Hernández et al, 2021).…”

Section: Discussionmentioning

confidence: 97%

Predictors of sustained physical activity: behaviour, bodily health, and the living environment

Diaconașu,

Stoleriu,

Câmpanu

et al. 2024

Front. Physiol.

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This study examined the determinants of sustained physical activity. Eighty-four participants undertook a 7-weeks walking regime (i.e., a 1-h biometrically-monitored walk, at least 5 days/week), with bioelectrical impedance (BIA) and total cholesterol capillary blood measurements performed before and after programme. To investigate behavioural habit formation, 7 weeks after walking termination, all participants were interviewed and (health) re-tested. Data were modelled with an artificial neural network (ANN) cascading algorithm. Our results highlight the successful prediction of continued physical activity by considering one’s physical fitness state, the environmental living context, and risk for cardiovascular disease. Importantly, those artificial neural network models also taking body mass index (BMI) and blood cholesterol as predictors excel at predicting walking continuation (i.e., predictions with 93% predictability). These results are first to highlight the type and importance of available physiological drivers in maintaining a sustained physical activity regime such as walking. They are discussed within the framework of habit formation and the nowadays health and/or wellbeing focus.

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Section: Discussionmentioning

confidence: 97%

Predictors of sustained physical activity: behaviour, bodily health, and the living environment

Diaconașu,

Stoleriu,

Câmpanu

et al. 2024

Front. Physiol.

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“…It should be added that in the practice of neural network modeling (e.g., [2][3][4][5][6][7][8][9]) the author's team repeatedly revealed regularities of different subject areas and made predictions that were explained and experimentally confirmed only some time after the publication of the results.…”

Section: Complexitymentioning

confidence: 99%

“…is objective cannot be solved by other methods due to the need to analyze a large number of parameters and factors while many of them have an insignificant impact on the diagnostic result. e same authors created a medical system based on neural network technologies [4][5][6]. Due to a complex mathematical formulation of the problem, this system allows not only for diagnosing cardiovascular diseases, but also for making disease development forecasts for many years to come and for selecting the optimal courses of disease treatment and prevention (https://en.kardionet.ru).…”

Section: Introductionmentioning

confidence: 99%

The Complex Neural Network Model for Mass Appraisal and Scenario Forecasting of the Urban Real Estate Market Value That Adapts Itself to Space and Time

Yasnitsky

Alekseev

2021

Complexity

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In the modern scientific literature, there are many reports about the successful application of neural network technologies for solving complex applied problems, in particular, for modeling the urban real estate market. There are neural network models that can perform mass assessment of real estate objects taking into account their construction and operational characteristics. However, these models are static because they do not take into account the changing economic situation over time. Therefore, they quickly become outdated and need frequent updates. In addition, if they are designed for a specific city, they are not suitable for other cities. On the other hand, there are several dynamic models taking into account the overall state of the economy and designed to predict and study the overall price situation in real estate markets. Such dynamic models are not intended for mass real estate appraisals. The aim of this article is to develop a methodology and create a complex model that has the properties of both static and dynamic models. Moreover, our comprehensive model should be suitable for evaluating real estate in many cities at once. This aim is achieved since our model is based on a neural network trained on examples considering both construction and operational characteristics, as well as geographical and environmental characteristics, along with time-changing macroeconomic parameters that describe the economic state of a specific region, country, and the world. A set of examples for training and testing the neural network were formed on the basis of statistical data of real estate markets in a number of Russian cities for the period from 2006 to 2020. Thus, many examples included the data relating to the periods of the economic calm for Russia, along with the periods of crisis, recovery, and growth of the Russian and global economy. Due to this, the model remains relevant with the changes of the international economic situation and it takes into account the specifics of regions. The model proved to be suitable for solving the following tasks: industrial economic analysis, company strategic and operational management, analytical and consulting support of investment, and construction activities of professional market participants. The model can also be used by government agencies authorized to conduct public cadastral assessment for calculating property taxes.

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“…2) the inadequacy of the efforts, resources and competencies necessary for the implementation of the changes and the offered remuneration 3) criticism of the planned methods of change implementation (while the essence of the changes may seem right and reasonable, their implementation methods may be questioned and criticised by employees) [31][32][33][34][35].…”

Section: Fatigue From Changes It Has Been Found Thatmentioning

confidence: 99%

Connection Between Resistance to Organizational Change and Psychological Resistance of an Individual

Prib¹,

Gromova²

2019

J. Intellect. Disabl. Diagn. Treat.

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Resistance to organizational change as a facet of psychological resistance of an individual is a widespread phenomenon through which countries, organizations and individuals suffer significant losses. The increasing amount of articles and scientific papers researching the phenomenon of change resistance published for the past 50 year serves as an indicator that the problem of people resisting the implementation of change has not yet been fully studied. Yet the relevance of the issue is far from decreasing as companies keep sustaining considerable losses with the desired changes failing to be implemented as suggested by multiple studies. We believe there is an insufficiently researched aspect of this issue. The purpose of the article is to establish a clear connection between resistance to organizational change and psychological resistance. We believe that such a connection attesting to change resistance being a psychological phenomenon will shift the focus of future research from the organizational or managerial standpoint to viewing change resistance in a psychological dimension. A thorough analysis of previously conducted studies serves as a basis for revealing commonalities between the reason for change resistance and psychological resistance. A chart representing own classification of the reasons for change resistance and psychological resistance illustrating the similarities of the two phenomena can be found in the article. The present study further explores the connections of various strengths that can be seen between the two phenomena and proposes a summary spreadsheet demonstrating the research findings. The article concluded a comparative classification of the reasons for PR and RC appearance, it was stated the connections differ in terms of strengths and permanence.

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Dynamic Artificial Neural Networks as Basis for Medicine Revolution

Cited by 8 publications

References 5 publications

Predictors of sustained physical activity: behaviour, bodily health, and the living environment

Predictors of sustained physical activity: behaviour, bodily health, and the living environment

The Complex Neural Network Model for Mass Appraisal and Scenario Forecasting of the Urban Real Estate Market Value That Adapts Itself to Space and Time

Connection Between Resistance to Organizational Change and Psychological Resistance of an Individual

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