An Application of Deep Belief Networks in Early Warning for Cerebrovascular Disease Risk

To reduce the incidence of cerebrovascular disease and mortality, identifying the risks of cerebrovascular disease in advance and taking certain preventive measures are significant. This article was aimed to investigate the risk factors of cerebrovascular disease (CVD) in the primary prevention, and to build an early warning model based on the existing technology. The authors use the information entropy algorithm of rough set theory to establish the index system suitable for early warning model. Then, using the limited Boltzmann machine and direction propagation algorithm, the depth trust network is established by building and stacking RBM, and the back propagation is used to fine-tune the parameters of the network at the top layer. Compared with the LM-BP early-warning model, the deep confidence network model is more effective than traditional artificial neural network, which can help to identify the risk of cerebrovascular disease in advance and promote the primary prevention.

AntTrust: An Ant-Inspired Trust Management System for Peer-to-Peer Networks

In P2P networks, self-organizing anonymous peers share different resources without a central entity controlling their interactions. Peers can join and leave the network at any time, which opens the door to malicious attacks that can damage the network. Therefore, trust management systems that can ensure trustworthy interactions between peers are gaining prominence. This paper proposes AntTrust, a trust management system inspired by the ant colony. Unlike other ant-inspired algorithms, which usually adopt a problem-independent approach, AntTrust follows a problem-dependent (problem-specific) heuristic to find a trustworthy peer in a reasonable time. It locates a trustworthy file provider based on four consecutive trust factors: current trust, recommendation, feedback, and collective trust. Three rival trust management paradigms, namely, EigenTrust, Trust Network Analysis with Subjective Logic (TNA-SL), and Trust Ant Colony System (TACS), were tested to benchmark the performance of AntTrust. The experimental results demonstrate that AntTrust is capable of providing a higher and more stable success rate at a low running time regardless of the percentage of malicious peers in the network.

Big Data Mining Method of Marketing Management Based on Deep Trust Network Model

Through big data mining, enterprises can deeply understand the consumer preferences, behavior characteristics, market demand and other derived data of customers, so as to provide the basis for formulating accurate marketing strategies. Therefore, this paper proposes a marketing management big date mining method based on deep trust network model. This method first preprocesses the big data of marketing management, including data cleaning, data integration, data transformation and data reduction, and then establishes a big data mining model by using deep trust network to realize the research on the classification of marketing management data. Experimental results show that the proposed method has 99.08% accuracy, the capture rate reaches 88.11%, and the harmonic average between the accuracy and the recall rate is 89.27%, allowing for accurate marketing strategies.

ZbSR: A Data Plane Security Model of SR-BE/TE based on Zero-Trust Architecture

Facing the untrusted threats of network elements and PKI/CA faced by SR-BE/TE(Segment Routing-BE/TE) data plane in the zero-trust network environment, firstly, this paper refines it into eight specific security issues. Secondly, an SR-BE/TE data plane security model ZbSR(ZTA-based SR) based on zero-trust architecture is proposed, which reconstructs the original SR control plane into a "trust-agent" two-layer plane based on 4 components of the controller, agent, cryptographic center and information base. On one hand, we distinguish between the two segment list generation modes and proposes corresponding data exchange security algorithms, by introducing north-south security verification based on identity authentication, trust evaluation, and key agreement before the terminal device establishes an east-west access connection, so reliable data exchange between terminal devices can be realized. On the other hand, for the network audit lacking SR-BE/TE, a network audit security algorithm based on solid authentication is proposed. By auditing the fields, behaviors, loops, labels, paths, and SIDs of messages, threats such as stream path tampering, SID tampering, DoS attacks, and loop attacks can be effectively detected. Finally, through the simulation test, the proposed model can provide security protection for the SR data plane with a 19.3% average incremental delay overhead for various threat scenarios.

APPLICATION OF THE BAYESIAN TRUST NETWORK AND MULTINOMIAL LOGISTIC REGRESSION TO PREDICT THE DEGREE OF CONTAMINATION OF AGRICULTURAL LAND

Исследованы возможности применения Байесовской сети доверия (БСД) и мультиномиальной логистической регрессии (МНЛР) для прогнозирования степени засоренности земель сельскохозяйственного назначения. Рассчитана вероятность превышения экономического порога вредоносности (ЭПВ) при участии обеих моделей. Выполнено моделирование влияния природных и антропогенных факторов с использованием БСД, а также осуществлен прогноз превышения ЭПВ по категориям с помощью МНЛР. Для обучения моделей использованы данные длительного многофакторного полевого опыта Кемеровского НИИСХ – филиала СФНЦА РАН. Учитывая особенности статистической выборки, определены основные предикторы моделей, влияющие на засоренность земель. Выбранными предикторами были агротехнические приемы (системы обработки почвы, предшественники) и агрометеорологические ресурсы (суммы активных температур воздуха, осадки). Объясненная часть дисперсии по мере Нэйджелкерка, составляет 80,9 %, что говорит о высоких прогностических возможностях применения МНЛР. Прогнозные результаты обеих моделей совпали в 79 % случаев, что указывает на достижение высоких показателей меры близости прогнозов по БСД и МНЛР. Обе модели показали достаточно высокую достоверность при верификации на эмпирических данных за прошлые годы и могут быть использованы в качестве инструмента для прогноза. Следующим этапом работы станет совместное использование БСД и МЛНР, которое может способствовать усилению достоинств обоих подходов и устранению недостатков отдельных из них. The possibilities of using the Bayesian Network of Trust (BSD) and multinomial logistic regression (MNLR) to predict the degree of contamination of agricultural land are investigated. The probability of exceeding the economic threshold of harmfulness (ETH) with the participation of both models is calculated. Modeling of the influence of natural and anthropogenic factors using BSD was carried out, and the forecast of the excess of ETH by category was carried out using MNLR. To train the models, data from a long–term multifactorial field experience of the Kemerovo Research Institute of Agricultural Sciences - branch of the SFSCA RAS were used. Taking into account the features of the statistical sample, the main predictors of the models affecting land contamination are determined. The selected predictors were agrotechnical techniques (tillage systems, precursors) and agrometeorological resources (sums of active air temperatures, precipitation). The explained part of the variance with the Nagelkerk measure is 80.9%, which indicates high prognostic possibilities of using MNLR. The forecast results of both models coincided in 79% of cases, which indicates the achievement of high indicators of the measure of proximity of forecasts for BSD and MNLR. Both models have shown sufficiently high reliability when verified on empirical data from previous years and can be used as a tool for forecasting. The next stage of the work will be the joint use of BSD and MDR, which can contribute to strengthening the advantages of both approaches and eliminating the shortcomings of some of them.

Prediction of nitrate nitrogen content in soil using machine learning

The possibilities and feasibility of using the Bayesian network of trust and logistic regression to predict the content of nitrate nitrogen in the 0-40 cm soil layer before sowing have been investigated. Data from long-term multifactor field experience at the Siberian Research Institute of Farming and Agricultural Chemization of SFSCA RAS for 2013-2018 were used to train the models. The experiment was established on leached chernozem in the central forest-steppe subzone in 1981 in the Novosibirsk region. Considering the characteristics of the statistical sample (observation and analysis data), the main predictors of the models affecting nitrate nitrogen content in soil were identified. The Bayesian trust network is constructed as an acyclic graph, in which the main (basic) nodes and their relationships are denoted. Network nodes are represented by qualitative and quantitative plot parameters (soil subtype, forecrop, tillage, weather conditions) with corresponding gradations (events). The network assigns a posteriori probability of events for the target node (nitrate-nitrogen content in the 0-40 cm soil layer) as a result of experts completing the conditional probability table, taking into account the analysis of empirical data. Two scenarios were analyzed to test the sustainability of the network and satisfactory results were obtained. The result of the logistic regression is the coefficients characterizing the closeness of the relationship between the dependent variable and the predictors. The coefficient of determination of the logistic regression is 0.7. This indicates that the quality of the model can be considered acceptable for forecasting. A comparative assessment of the predictive capabilities of the trained models is given. The overall proportion of correct predictions for the Bayesian confidence network is 84%, for logistic regression it is 87%.

Social Capital, Joint Responsibility Systems, And Empowerment Of Women In Msmes : A Conceptual Framework

Empowering women, especially in entrepreneurship, is often faced with limited accessibility, especially in meeting capital through loans at formal financial institutions and structural constraints in their environment. Women's cooperatives are a bridge to overcome the problem of women's entrepreneurial capital to increase their business, because basically woman have potential in its business activities. Social capital that has elements of Trust, Network and norms attached to women's personalities will be a guarantee in obtaining loans with a joint responsibility system. The opportunity to develop a business through social capital and joint responsibility has an impact on opening up opportunities to empower micro, small and medium entreprise (MSMEs) business actors to further increase their business and continue to be in a sustainable entrepreneurial position.Sustainable Entrepreneurship for female entrepreneurs offers women professional development and limited flexibility to balance work and family commitments.

Network marketing in network society: humanistic and psychological aspects of the problem

The article carries out both theoretical and empirical analysis of some humanistic and psychological aspects of the functioning of network marketing in modern network society. In fact, the humanistic vector of research is represented in the importance of formulating and understanding of the problem of human search for a harmonious individual identity and ways of effective self-realization in a globalized and informatized society, in a massive environment of consumption and services; psychological and socio-psychological vectors consist of the study of individual characteristics of the Ukrainian population’s attitude to their own material status and some potential opportunities for its improvement. In particular, the analysis touches the phenomenon of «social psychology of poverty» as a lifestyle, which is often justified by the relevant philosophy of life and morality. The ambivalent components of the image of network marketing in modern society, as well as social and personal factors of attitude formation and personal involvement in this type of employment are revealed. According to the results of a pilot empirical study, it was stated that the dominant majority of online Ukrainian respondents (it was used the methods of «convenient sampling» and «snowball») has been self-identified as «average in their material status». At the same time, almost half consider wealth as mainly the result of their own persistence and activity. The dominant majority of respondents are at least partially informed about the network business, and a little over a third – «tried themselves» in this type of business. The most frequently identified associations with «network marketing» were «sale / sales» and «Internet», fit into his general denotative interpretations. However, about one in five respondents has some distrust of this method of earning, associated with «pyramidal» connotations and fear of «being deceived». And the same number of people trust network marketing instills concrete success and results of relevant activities.