Application Of Bayesian Networks Research Articles

Application of Bayesian regularization back propagation neural network in sensorless measurement of pump operational state

Under the strategic framework of global carbon emission reduction, it is urgent to reduce the energy consumption of centrifugal pumps. Monitoring pump’s operational states can effectively help people to understand system’s behavior, and achieve the optimal energy management on pump equipment. In view of installation space, cost, reliability and other factors, it is necessary to find an alternative to the traditional sensor monitoring scheme. Therefore, it is significant to develop a prediction method for the operational state of centrifugal pump using sensorless technology. In this paper, a neural network estimation model based on Bayesian regularization back propagation (BRBP) algorithm is proposed. Compared with the traditional QP estimation model, the new model has better nonlinearity and higher precision. Using power and rotational frequency as input neurons, BRBP model realizes the output of flow rate and head through neuronal calculations. By comparing the predicted flow rate and head values of a multistage centrifugal pump with the measured values at different rotational frequencies, it is found that the mean relative error of flow rate of BRBP model is in the range of 1% to 4%, and the mean relative error of head is in the range of 1% to 2%. The prediction accuracy of BRBP neural network model is obviously higher than that of QP model, which meets the requirements of industrial application.

Application of Bayesian network in risk assessment for website deployment scenarios

Abstract—The rapid development of web-based systems in the digital transformation era has led to a dramatic increase in the number and the severity of cyber-attacks. Current attack prevention solutions such as system monitoring, security testing and assessment are installed after the system has been deployed, thus requiring more cost and manpower. In that context, the need to assess cyber security risks before the deployment of web-based systems becomes increasingly urgent. This paper introduces a cyber security risk assessment mechanism for web-based systems before deployment. We use the Bayesian network to analyze and quantify the cyber security risks posed by threats to the deployment components of a website. First, the deployment components of potential website deployment scenarios are considered assets, so that their properties are mapped to specific vulnerabilities or threats. Next, the vulnerabilities or threats of each deployment component will be assessed according to the considered risk criteria in specific steps of a deployment process. The risk assessment results for deployment components are aggregated into the risk assessment results for their composed deployment scenario. Based on these results, administrators can compare and choose the least risky deployment scenario. Tóm tắt—Sự phát triển mạnh mẽ của các hệ thống trên nền tảng web trong công cuộc chuyển đổi số kéo theo sự gia tăng nhanh chóng về số lượng và mức độ nguy hiểm của các cuộc tấn công mạng. Các giải pháp phòng chống tấn công hiện nay như theo dõi hoạt động hệ thống, kiểm tra và đánh giá an toàn thông tin mạng được thực hiện khi hệ thống đã được triển khai, do đó đòi hỏi chi phí và nhân lực thực hiện lớn. Trong bối cảnh đó, nhu cầu đánh giá rủi ro an toàn thông tin mạng cho các hệ thống website trước khi triển khai thực tế trở nên cấp thiết. Bài báo này giới thiệu một cơ chế đánh giá rủi ro an toàn thông tin mạng cho các hệ thống website trước khi triển khai thực tế. Chúng tôi sử dụng mạng Bayes để phân tích và định lượng rủi ro về an toàn thông tin do các nguồn đe dọa khác nhau gây ra trên các thành phần triển khai của một website. Đầu tiên, các thành phần triển khai của các kịch bản triển khai website tiềm năng được mô hình hoá dưới dạng các tài sản, sao cho các thuộc tính của chúng đều được ánh xạ với các điểm yếu hoặc nguy cơ cụ thể. Tiếp đó, các điểm yếu, nguy cơ của từng thành phần triển khai sẽ được đánh giá theo các tiêu chí rủi ro đang xét tại mỗi thời điểm cụ thể trong quy trình triển khai. Kết quả đánh giá của các thành phần triển khai được tập hợp lại thành kết quả đánh giá hệ thống trong một kịch bản cụ thể. Căn cứ vào kết quả đánh giá rủi ro, người quản trị có thể so sánh các kịch bản triển khai tiềm năng với nhau để lựa chọn kịch bản triển khai ít rủi ro nhất.

Application of bayesian networks in the evaluation of projects of corporate uniform

The article reveals the problem of making a decision on the design of a corporate uniform in the context of the contradictory requirements for uniformity of clothing for employees and the needs of participants in the dress code for self-expression. The combination of the above factors creates specificity in the assessment of uniform projects, since it becomes necessary to predict the stability of the dress code policy, the acceptance of its requirements by all interested parties. To solve this problem, a method is proposed for assessing the design decisions of a uniform from the standpoint of a balanced reflection in its design of the values of corporate culture and the possibilities of personal self-identification of participants in the dress code. The article considers an example of the development and testing of a semantic map, reflecting the probabilistic relationship of design parameters, in the assessment and refinement of models-proposals of a corporate uniform. The evaluation criteria presented in the semantic map format allow reflecting both the marketing role of the uniform in the promotion of the company′s services and the actual needs of the dress code participants identified during the expert discussions.

Bayesian networks as a novel tool to enhance interpretability and predictive power of ecological models

In today's world, it is becoming increasingly important to have the tools to understand, and ultimately to predict, the response of ecosystems to disturbance. However, understanding such dynamics is not simple. Ecosystems are a complex network of species interactions, and therefore any change to a population of one species will have some degree of community level effect. In recent years, the use of Bayesian networks (BNs) has seen successful applications in molecular biology and ecology, where they were able to recover plausible links in the respective systems they were applied to. The recovered network also comes with a quantifiable metric of interaction strength between variables. While the latter is an invaluable piece of information in ecology, an unexplored application of BNs would be using them as a novel variable selection tool in the training of predictive models. To this end, we evaluate the potential usefulness of BNs in two aspects: (1) we apply BN inference on species abundance data from a rocky shore ecosystem, a system with well documented links, to test the ecological validity of the revealed network; and (2) we evaluate BNs as a novel variable selection method to guide the training of an artificial neural network (ANN). Here, we demonstrate that not only was this approach able to recover meaningful species interactions networks from ecological data, but it also served as a meaningful tool to inform the training of predictive models, where there was an improvement in predictive performance in models with BN variable selection. Combining these results, we demonstrate the potential of this novel application of BNs in enhancing the interpretability and predictive power of ecological models; this has general applicability beyond the studied system, to ecosystems where existing relationships between species and other functional components are unknown.

Who Packed the Drugs? Application of Bayesian Networks to Address Questions of DNA Transfer, Persistence, and Recovery from Plastic Bags and Tape.

When DNA from a suspect is detected in a sample collected at a crime scene, there can be alternative explanations about the activity that may have led to the transfer, persistence and recovery of his/her DNA. Previous studies have shown that DNA can be indirectly transferred via intermediate surfaces and that DNA on a previously used object can persist after subsequent use of another individual. In addition, it has been shown that a person’s shedder status may influence transfer, persistence, prevalence, and recovery of DNA. In this study we have investigated transfer persistence and recovery on zip-lock bags and tape, which are commonly encountered in drug cases and how the shedder status of the participants influenced the results. A probabilistic framework was developed which was based on a previously described Bayesian network with case-specific modifications. Continuous modelling of data was used to inform the Bayesian networks and two case scenarios were investigated. In the specific scenarios only moderate to low support for Hp was obtained. Applying a continuous model based on the profile quality can change the LRs.

Bayesian network-based missing mechanism identification (BN-MMI) method in medical research

BackgroundTraditional approaches to identify missing mechanisms are usually based on the hypothesis test and confronted with both theoretical and practical challenges. It has been proved that the Bayesian network is powerful in integrating, analyzing and visualizing information, and some previous researches have verified the promising features of Bayesian network to deal with the aforementioned challenges in missing mechanism identification. Based on the above reasons, this paper explores the application of Bayesian network to the identification of missing mechanisms for the first time, and proposes a new method, the Bayesian network-based missing mechanism identification (BN-MMI) method, to identify missing mechanism in medical research.MethodsThe procedure of BN-MMI method consists three easy-to-implement steps: estimating the missing data structure by the Bayesian network; assessing the credibility of the estimated missing data structure; and identifying the missing mechanism from the estimated missing data structure. The BN-MMI method is verified by simulation research and empirical research.ResultsThe simulation study verified the validity, consistency and robustness of BN-MMI method, and indicated its outperformance in contrast to the traditional logistic regression method. In addition, the empirical study illustrated the applicability of BN-MMI method in the real world by an example of medical record data.ConclusionsIt was confirmed that the BN-MMI method itself, together with human knowledge and expertise, could identify the missing mechanisms according to the probabilistic dependence/independence relations among variables of interest. At the same time, our research shed light upon the potential application of BN-MMI method to a broader range of missing data issues in medical studies.

A 20‐year mapping of Bayesian belief networks in software project management

In recent years, more software engineering researchers have focussed on methods, techniques, tools, and processes to support software project management (SPM) addressing quality, cost, and time constraints. In this direction, the use of Bayesian belief networks (BBNs) has gained attention for providing a powerful mechanism for cause–effect analysis with both qualitative and quantitative data to support decision making under uncertainty. This work aims to provide an overview of the first 20 years of research in BBNs applied to SPM, and by so doing it contributes to the structuring of this research topic and the identification of new research opportunities. We conducted a systematic mapping (SM) study on the use of BBN as a decision-support tool for SPM issues. We analysed over 109 relevant publications, from 1999 to 2018 (i.e., 20 years), to understand the motivations for using Bayesian networks (why), the problem domain and model scope addressed by researchers (what), the stage of the life cycle in which BBNs are being used (when), the venues of the publications (where), and the tools used to model the Bayesian networks (how). We draw the following conclusions from the results of our SM: (1) The application of Bayesian networks in SPM has been an active research topic since 1999. (2) Prediction and planning are the most common purposes in 60% of selected papers. (3) Software quality (55%) is the problem domain most investigated. (4) Most of the surveyed studies embrace the project (47%) and product (37%) scopes. And finally, (5) the development phase (86%) is when Bayesian networks have been more used. As future work, we highlight the need to further investigate methodologies that enable the use of BBNs in real software development contexts.

Bayesian networks application to forecast the national economies development taking into account the water factor

The influence of this factor on the development of national economies is studied. This study uses the Bayesian network, which illustrates the interaction of indicators of water supply and water use and includes nodes that are formally represented as vectors. The most valuable result of the modelling is not the general forecast obtained with the help of the model, but the structure of the network itself, which allows to identify connections within the model and explain the reason for the emerging interdependencies. modelling using Bayesian networks confirmed the fact that there is a direct relationship between GDP and water consumption and drainage. The obtained results confirm the possibility of achieving an increase in the overall GDP of the country with an increase in the amount of water resources used for production needs. However, this should be implemented in combination with a simultaneous reduction in the volume of return (wastewater) discharged into surface water bodies.

Effectiveness of the Preventive Intervention of Chromosomal Disorders of Iran's Community Genetics Program: Application of Bayesian Network.

Background: Evidence-based policymaking for the genetic preventive interventions at the community level requires information on the effectiveness of interventions in the operational areas taking into account the characteristics of health system and customer behaviour. These information are limited in many low- and middle-income countries. In this study, we estimated the effectiveness of preventive interventions for chromosomal disorders using the conceptual framework of Iran’s community genetics program (ICGP) using a Bayesian Network as a modeling method in limited access situation to the complete and accurate observational data. Methods: Expert elicitation method based on global and national scientific evidences was applied to determine the structure of the Bayesian Network (BN) and to quantify the probability of nodes. The nomological and face validity of the network was checked. Also, a sensitivity analysis against the sources of uncertainty of probabilities was conducted. Results: By ICGP interventions, 63% (95% CI, 0.55-0.71) of all chromosomal disorders can be prevented, which is responsible for 80% (95% CI, 0.76-0.84) and 38% (95% CI, 0.31-0.45) reduction of expected baseline birth prevalence of trisomis and other autosomal disorders, respectively. Improving the access to and the uptake of screening service can also result in a 12% and 11% increase in effectiveness, respectively. Conclusion: Effectiveness of ICGP’s intervention is between the same interventions’ effectiveness in Western Europe and the Eastern Mediterranean region. Opportunities for increasing the uptake of and the access to the interventions are strengthening the public genetic literacy and implantation of a system of laboratory sample transfer at the side of the utilization of telehealth for delivering the counseling services at remote areas.

Prediction of Wind Power Generation with Modern Artificial Intelli-gence Technology

<div><p class="4">In view of the continuous growth of energy demand and interest in environmental protection, the use of clean energy to replace fossil fuels is a global trend. Wind energy is the fastest growing renewable energy in the world in recent years. However, in the case of Mexico, there are still some difficulties in promoting its use in some areas of the national territory. One difficulty is knowing in advance how much energy can be injected into the grid. This paper introduces the development of artificial intelligence technology for wind power generation prediction based on multi-year meteorological information. In particular, the potential application of Bayesian network in these prediction applications is studied in detail. A weather forecasting method based on Dynamic Bayesian network (RBD) is proposed. The forecasting system was tested using meteorological data from the regional wind energy technology center (CERT) of the National Institute of Electricity and Clean Energy (INEEL) in Oaxaca, Mexico. The results are compared with the time series prediction results. The results show that dynamic Bayesian network is a promising wind power generation prediction tool.</p></div>

Application of Relational Bayesian Networks to Vector Control in New York City

We propose a novel way of rapidly formulating hypotheses describing the efficacy of West Nile virus (WNV) control campaigns using relational Bayesian Networks (RBN). RBN takes advantage of both quantitative information and expert opinion. We used multiyear, geo-referenced, and temporal data about dead birds, mosquitoes and humans, to infer probable relationships using proprietary software (CleverSet® Modeler) and discover the RBN that best fit the data for our initial models. Additional expert knowledge was later utilized for augmenting subsequent models. The final RBM was created after relating all the variables of interest that correlate to the occurrence of human cases, positive birds, and positive mosquitoes. The findings of this research suggest that WNV positive birds are valuable indicators of WNV activity. The most probable core organisms are bird-feeding Culex and ground-dwelling birds. WNV preventive measures such as public education, source reduction, and larviciding may be supplemented by adult mosquito control by ground spraying. To effectively interrupt the chain of transmission, careful effort must be devoted to ensuring that fewer mosquitoes remain the day after spray.

Application of Bayesian network and regression method in treatment cost prediction

Charging according to disease is an important way to effectively promote the reform of medical insurance mechanism, reasonably allocate medical resources and reduce the burden of patients, and it is also an important direction of medical development at home and abroad. The cost forecast of single disease can not only find the potential influence and driving factors, but also estimate the active cost, and tell the management and reasonable allocation of medical resources. In this paper, a method of Bayesian network combined with regression analysis is proposed to predict the cost of treatment based on the patient's electronic medical record when the amount of data is small. Firstly, a set of text-based medical record data conversion method is established, and in the clustering method, the missing value interpolation is carried out by weighted method according to the distance, which completes the data preparation and processing for the realization of data prediction. Then, aiming at the problem of low prediction accuracy of traditional regression model, this paper establishes a prediction model combined with local weight regression method after Bayesian network interpretation and classification of patients' treatment process. Finally, the model is verified with the medical record data provided by the hospital, and the results show that the model has higher prediction accuracy.

Bayesian Network Applications for Sustainable Holistic Water Resources Management: Modeling Opportunities for South Africa.

Anthropogenic transformation of land globally is threatening water resources in terms of quality and availability. Managing water resources to ensure sustainable utilization is important for a semiarid country such as South Africa. Bayesian networks (BNs) are probabilistic graphical models that have been applied globally to a range of water resources management studies; however, there has been very limited application of BNs to similar studies in South Africa. This article explores the benefits and challenges of BN application in the context of water resources management, specifically in relation to South Africa. A brief overview describes BNs, followed by details of some of the possible opportunities for BNs to benefit water resources management. These include the ability to use quantitative and qualitative information, data, and expert knowledge. BN models can be integrated into geographic information systems and predict impact of ecosystem services and sustainability indicators. With additional data and information, BNs can be updated, allowing for integration into an adaptive management process. Challenges in the application of BNs include oversimplification of complex systems, constraints of BNs with categorical nodes for continuous variables, unclear use of expert knowledge, and treatment of uncertainty. BNs have tremendous potential to guide decision making by providing a holistic approach to water resources management.

Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks

Infrastructure systems, such as wind farms, are prone to various human-induced and natural disruptions such as extreme weather conditions. There is growing concern among decision makers about the ability of wind farms to withstand and regain their performance when facing disruptions, in terms of resilience-enhanced strategies. This paper proposes a probabilistic model to calculate the resilience of wind farms facing disruptive weather conditions. In this study, the resilience of wind farms is considered to be a function of their reliability, maintainability, supportability, and organizational resilience. The relationships between these resilience variables can be structured using Bayesian network models. The use of Bayesian networks allows for analyzing different resilience scenarios. Moreover, Bayesian networks can be used to quantify resilience, which is demonstrated in this paper with a case study of a wind farm in Arctic Norway. The results of the case study show that the wind farm is highly resilient under normal operating conditions, and slightly degraded under Arctic operating conditions. Moreover, the case study introduced the calculation of wind farm resilience under Arctic black swan conditions. A black swan scenario is an unknowable unknown scenario that can affect a system with low probability and very high extreme consequences. The results of the analysis show that the resilience of the wind farm is significantly degraded when operating under Arctic black swan conditions. In addition, a backward propagation of the Bayesian network illustrates the percentage of improvement required in each resilience factor in order to attain a certain level of resilience of the wind farm under Arctic black swan conditions.

A multi-layer Bayesian network method for supply chain disruption modelling in the wake of the COVID-19 pandemic

ABSTRACT While the majority of companies anticipated the negative and severe impacts of the COVID-19 pandemic on the supply chains (SC), most of them lacked guidance on how to model disruptions and their performance impacts under pandemic conditions. Lack of such guidance resulted in delayed reactions, incomplete understanding of pandemic impacts, and late deployment of recovery actions. In this study, we offer a method of modelling and quantifying the SC disruption impacts in the wake of a pandemic. We develop a multi-layer Bayesian network (BN) model that can be used to identify SC disruption triggers and risk events amid the COVID-19 pandemic and quantify the consequences of pandemic disruptions. The unique features of BN, such as forward and backward propagation analysis, are utilised to simulate and measure the impact of different triggers on SC financial performance and business continuity. In this way, we combine resilience and viability SC perspectives and explicitly account for the pandemic settings. The outcomes of this research open a novel theoretical lens on application of BNs to SC disruption modelling in the pandemic setting. Our results can be used as a decision-support tool to predict and better understand the pandemic impacts on SC performance.

Analysis of Demand Disruption on Product Availability in the Retail Industry

The customer behavior in shopping were changing the demand disruptions when the COVID-19 pandemic attacked the countries. Retail industries are one of business sectors which were directly impacted the availability of item products. The purpose of this study is to understand the level of demand disruptions of COVID-19 pandemic using Bayesian Network (BN). BN method is powerful method to assess and decide the uncertainly of demand and risk. Based on relevant literature and interviews, the hierarchy of BN were developed and stock out data to represent the product of availability in 5 case study were collected in case study. Finally, the analysis to understand the level of demand disruptions each item products, product family and categories have been performed. This paper provides a new evidence by changing of shopping behavior when the COVID-19 pandemic attached in Indonesia and presents the BN application could be used to handle risk assessment based on stock out data systematically and comprehensively.

Специфіка застосування математико-статистичних методів в українських дослідженнях благополуччя особистості

Personal well-being has been actively studied over the past two decades regarding the social practice demands. However, it is an extremely complex multi-concept which is difficult to verify. The article analyzes the mathematical and statistical methods used in Ukrainian studies of the phenomenon of personal well-being over the past five years. Firstly, the study shows that in domestic publications (especially articles) the importance is not given to the mathematical and statistical methods. Secondly, typical tasks in the study of personal well-being are the description of primary data, the study of similarity (Student’s t-test, Mann-Whitney’s U-test, Kraskal-Wallace’s H-test, Wilcoxon’s T-test, ANOVA) and the study of dependencies (Pearson’s correlation coefficient, Spearman’s rank-order correlation coefficient). Thirdly, correlation analysis is still one of the main analysis statistical tools over the past five years according the study of the links between well-being and other psychological phenomena. Since well-being is a highly complex system characterized by the presence of recurrent relationships and nonlinearity, it is important to use the synergetic paradigm and modern mathematical and statistical means of data analysis (Data mining), which allow to characterize systems characterized by ambiguity and uncertainty. Fourthly, application of new modern statistical methods is not yet widespreaded. When studying the phenomenon of personal well-being, the practice of processing small databases within the framework of applied statistics continues. Structural modeling, which makes it possible to confirm multidimensional models of well-being, is not widespreaded. Application of Bayesian networks of trust is quite promising, which are based on the concept of subjective probability, thus it is focused not on the study of objective reality, but on clarifying the individual’s ideas about it. Key words: psychological well-being, mathematical and statistical analysis, subjective well-being, modeling, personal well-being.

Application of neural and bayesian networks in diesel engines under the flaw detection method

The identification of premature faults in Internal Combustion Engines has become determinant to guarantee suitable operation. Therefore, this study focuses on the implementation of fault diagnostic methodology by using advanced algorithms such as Back Propagation neural networks and Bayesian networks. Results indicated that the proposed methodology serves as a robust tool to identify different fault conditions in a wide operational spectrum with an reliability of nearly 73%. Moreover, the Backpropagation network diagnostic methodology presented an reliability of 18%, which is 3% higher than Bayesian networks. Overall, the implemented methodology counterbalanced interference conditions and noise signals while providing versatility to operate for different types of engines. In conclusion, this study can be extrapolated to different fields of physics to assist in identifying flaws in experimental test benches.

Application of Bayesian Networks of Genotype by Environment Interaction Evaluation Under Plant Disease, Soil Types and Climate Condition-using Bayesia Lab

Genotype by environment interaction (GEI) linked to plant disease, soil properties and climate conditions add potential value for a breeding program to underpin decision making. In understanding genotype x environment interaction, the most challenging factors are the identification of genetic variation for a range of traits and their responsiveness to the climate change factors. In order to study the complex relationships with genetic and non-genetic factors, the application of Bayesian network tools will help understand and accelerate plant breeding progress and improve the efficiency of crop production. In this study, we proposed the application of Bayesian networks (BNs) to evaluate genotype by environment interaction under plant diseases, soil type, and climate variables. An adapted to simulate multiple environmental trial (MET) data of maize (corn) was used to examine the performance of the BN predictive modeling using BayesiaLab for deriving knowledge and graphical structure for exploring GEI diagnosis and analysis. The results highlighted that genotypes have the same probability and the frequentist of rainfall, temperature, soil type, and disease type occurred as <=88 (46%), 35 (37%), clay (27%), and MB (47%) respectively, which have to monitor reflects in each discretization. This study provided a roadmap to knowledge modeling of GEI using BayesiaLab software. On a broader scale, this study helps predict the yield of crop varieties by understanding agronomic and environmental factors under farm conditions rather than conducting long-term agricultural testing under well-controlled conditions of the on-station trials. Future improvements of BNs application of METs should consider working on a larger and more detailed soil and irrigation system linked to agro system.

Bibliometric Analysis and Visualization of Bayesian Network Application in Safety Field

Bayesian network (BNs) has been widely used in uncertain knowledge modeling since it was proposed, especially in safety assessment and risk prediction. In order to fully understand the application status of Bayesian network in the field of safety, 3111 sample documents were collected from the web of science core collection database. Through CiteSpace technology text mining and visual analysis software, document output, keywords, author cooperation, organization cooperation, citation and journal distribution were analyzed. The results show that: FAISAL KHAN, MOHAMED ABDELATY, HELAI HUANG, TAREK SAYED, JAEYOUNG LEE, etc. published Bayesian networks The most widely used literature in the field of safety has made an important contribution to the development of this field; institutional cooperation is mainly based on Univ Central Florida, MEM Univ Newfoundland, Univ British Columbia, Tongji Univ, Texas A & M Univ, cents Univ, Delft Univ technology and other universities; journal distribution of literature is mainly based on the core functions of Bayesian network, and mainly distributed in ACCIDENT ANALYSIS AND PREVENTION, TRANSPORTATION RESEARCH RECODE, RELIABILITY ENGINEERING SYSTEM SAFETY and SAFETY SCIENCE.