Naive Bayesian Network Research Articles

Research on biomechanics integrated Bayesian network mental health diagnosis system

With the rapid economic development of various countries around the world and the acceleration of global networking, countries are striving to promote their own urbanization and industrialization progress. The side effect is that social pressure leads to the concentrated outbreak of various social contradictions. The main psychological health testing and evaluation method in society is still conducted through dialogue with psychologists. Doctors obtain information through dialogue and communication with patients, and diagnose their psychological status based on this information. Affected by factors such as communication style and the patient’s own mental state. The information obtained may result in omissions and biases, leading to inaccurate diagnostic results. Bayesian network is a probabilistic graphical model that derives the results through information calculation. It can analyze and calculate the finite and incomplete conditions, carry out corresponding reasoning, and obtain more rigorous results. This article applied the naive Bayesian algorithm to the research of mental health diagnosis systems, and compared it with mental health diagnosis systems that do not use algorithms. According to the mental health index of contemporary people, the algorithm achievement test experiment of mental health diagnosis system was carried out. After research and comparison, it was found that for the collected data, the maximum accuracy of the Naive Bayesian algorithm within a hundred calculations reached 99%, with a mean of 96.5%. The traditional paper-based psychological diagnosis method had a maximum accuracy of 89%, a minimum of 70%, and an average accuracy of 80.5%. Therefore, the application of naive Bayesian network to the development and research of mental health diagnosis system can effectively improve the efficiency, accuracy and diagnostic effect of mental diagnosis.

A novel Bayesian Pay-As-You-Drive insurance model with risk prediction and causal mapping

The modern vehicle insurance industry is increasingly adopting Pay-As-You-Drive (PAYD) insurance models, aligning premium costs with driving behavior. Our study introduces a Bayesian approach to PAYD insurance, leveraging the strengths of Naive Bayes classifiers and Bayesian Networks to handle uncertainty and integrate prior knowledge in risk assessment. The Naive Bayes model achieved an 87.5% accuracy in predicting risk partitions. With the Bayesian Network providing insights into causal relationships through a Directed Acyclic Graph (DAG), we also address the challenges of traditional actuarial models — low interpretability of intra-factor relationships and thus hard to plan for risk management for both provider and policyholder. Our research contributes to optimizing insurance pricing strategies. Still, the causal mapping also dismisses the meaningfulness of using geographic grouping in insurance pricing (discriminatory or not). It reassures the theoretical advantage of the PAYD model over the traditional model, facilitating access to affordable coverage for policyholders.

A Systematic Literature Survey on A Smart Multidisease Prediction and Doctor Recommendations using ML

-Smart multidisease prediction and doc- tor recommendations using machine learning is a growing field of research. The proposed methodol- ogy seeks to deliver precise illness prediction for several diseases, which may have important reper- cussions for enhancing the precision and timeli- ness of medical diagnostics and improving patient outcomes. Machine learning algorithms like Naive Bayesian networks,CNN, KNN are used to properly predict and classify diseases based on patient symp- toms and medical data analysis. Several studies have proposed models that can predict multiple diseases such as diabetes, heart disease, chronic kidney dis- ease, and cancer. The ultimate goal is to provide healthcare practitioners with the tools to make ed- ucated decisions and deliver improved patient care. This paper presents a survey of research papers and articles on the topic of smart multidisease prediction and doctor recommendations using machine learn- ing. Keywords-Machine Learning, CNN, Python- Flask

Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces. In tunneling practice, the rock mass quality is often assessed via a combination of qualitative and quantitative parameters. However, due to the harsh on-site construction conditions, it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction. In this study, a novel improved Swin Transformer is proposed to detect, segment, and quantify rock mass characteristic parameters such as water leakage, fractures, weak interlayers. The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%, 81%, and 86% for water leakage, fractures, and weak interlayers, respectively. A multi-source rock tunnel face characteristic (RTFC) dataset includes 11 parameters for predicting rock mass quality is established. Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset, a novel tree-augmented naive Bayesian network (BN) is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%. In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset. By utilizing the established BN, a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters, results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

Visual Relationship Detection for Workplace Safety Applications

Applications of object and visual relationship detection for safety and security applications are in its infancy. State-of-the-art computer vision research is largely focused on improving mean Average Precision and mean Average Recall performance on standard, general datasets such as the Verbs in COmmon objects in COntext and the Visual Genome dataset and rarely mention the potential of such models in safety and security scenarios. We propose to train and develop an object and visual relationship detection neural network to be used as part of the back-end model for a decision support system. We use a naive Bayesian Network to determine scenarios where our proposed object and visual relationship detection network is error-prone. We also release a graphical user interface which demonstrates how our back-end neural network and naive Bayesian Network can be used for hazardous workplace safety and security applications. All models and source code can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/th-truong/rd-gui</uri> for future research and applications of object and visual relationship detection models in the safety and security sector.

ASSOCIATION AND RISK PREDICTION OF PAIN AND FALL-RELATED INJURIES AMONG COMMUNITY-DWELLING OLDER ADULTS IN CHINA

Abstract Background Pain is common in older adults and has also been identified as a fall risk factor, whereas the impact and mechanism of fall-related injuries are still unknown. This study aims to examine the association and risk prediction of pain and fall-related injuries among Chinese older adults. Method This study enrolled 5,721 older adults from the China Health and Retirement Longitudinal Study (wave 2011-2015). Logistic regression was used to analyze the associations between pain characteristics and fall-related injuries. Furthermore, 2022 older adults with pain at baseline were further used to build a fall-related injury prediction model, using five machine learning algorithms (logistic regression, naive bayesian, random forest, adaptive boosting and artificial neural network) with 118 input variables as candidate features, and SHapley Additive exPlanations was used for the prediction model explanation. Results Logistic regression models showed that whether pain (OR=1.35) or pain severity (ORmoderate=1.37, ORsevere =1.48) and multisite pain (OR=1.41) independently predicted fall-related injuries. The Random forest model achieved the best performance among the prediction model with an area under the receiver operating characteristic curve of 0.764. Interpretable results showed that the top 10 significant predictors were: experience of falling, frailty, sex, short physical performance battery, received inpatient care, estimated glomerular filtration rate, lung function, pain quantity, White Blood Cell and Age. Conclusions Pain is associated with fall-related injuries among Chinese older adults. Fall-related injury prevention strategies for older adults with pain should specifically focus on fall history, frailty, and women.

Analysis on the Accuracy of Different Classification Prediction Models in the Field of the Impact of Online Classes on Students During the COVID-19

Some students are pleased, while others are sad, as a result of the introduction of online education. Different attitudes, levels of physical health, family economic conditions, and so on have resulted from online classes. Consequently, it is necessary to determine what has caused various students to have different states and whether or not parents of school-aged children took the appropriate response measures. In this paper, the author preprocesses relevant datasets from Kaggle, then uses naive Bayesian, random forest, K-neighbor, SVC, logistic regression, and neural networks to classify and predict the dataset, analyses their accuracy and confusion matrices to determine the best classification and prediction model, and conducts importance analysis based on the best classification and prediction model. Therefore, it is possible to determine the significance of each parameter and to make suggestions based on the significance of various parameters. This can be used to determine whether there is room for development and errors in the next plans implemented in the era of online classes, and in the future, it can be used to reduce the health problems of students caused by the next pandemic in the era of online classes.

Computer Image Scene and Object Information Extraction based on Bayesian Network Model

Abstract In order to better extract scene and object information from computer image, a construction object extraction algorithm based on Bayesian network is proposed. The algorithm is trained by multi-scene aerial images to build a grain dictionary and map the grain in the actual image to the grain dictionary to obtain the scene information of the image;Then naive Bayesian networks were used to model the constraints of the relationship between architectural targets and the spatial context of scene classes, and the extraction of architectural targets was converted into a posteriori probability problem for solving Bayesian network class nodes. The experimental results show that the proposed algorithm can effectively extract architectural objects from aerial images. The experiment result shows that:In this paper, the proportion of target pixels accurately extracted by the algorithm is taken as the standard to define the standard of target pixels accurately extracted by the algorithm to reach more than 90% of the building target pixels. The average time of training an image is 2 s, which is mainly spent on the convolution operation with the filter. After the training, the average time of processing a single test image is 0.5s. It is proved that Bayesian network model can effectively extract scene and object information from computer image.

Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies.

Breast cancer is a major public health concern, and early diagnosis and classification are critical for effective treatment. Machine learning and deep learning techniques have shown great promise in the classification and diagnosis of breast cancer. In this review, we examine studies that have used these techniques for breast cancer classification and diagnosis, focusing on five groups of medical images: mammography, ultrasound, MRI, histology, and thermography. We discuss the use of five popular machine learning techniques, including Nearest Neighbor, SVM, Naive Bayesian Network, DT, and ANN, as well as deep learning architectures and convolutional neural networks. Our review finds that machine learning and deep learning techniques have achieved high accuracy rates in breast cancer classification and diagnosis across various medical imaging modalities. Furthermore, these techniques have the potential to improve clinical decision-making and ultimately lead to better patient outcomes.

Multi-Disease Prediction Using Machine Learning Algorithm

Abstract: In the medical sector, disease diagnosis is an essential duty, and prompt and accurate diagnosis is crucial to effective management and therapy. Machine learning techniques, including Naive Bayesian networks, have shown promise in disease prediction and diagnosis. In this study, we present a machine learning-based multi-disease prediction system that uses Naive Bayesian networks. The proposed methodology seeks to deliver precise illness prediction for several diseases instantaneously. In addition to describing the methods adopted, which included dataset selection, preprocessing, feature selection, and the Naive Bayesian network algorithm, we also discuss the social relevance of this work, emphasizing the potential impact of accurate disease prediction in improving patient outcomes and bringing down healthcare costs. To evaluate the performance of the proposed model, we conducted experiments using a publicly available disease dataset. The results demonstrated that the proposed model achieved high accuracy of 91.2% and outperformed other state-of-the-art models for multi-disease prediction some of them are, Random Forest obtained 85.7% and Decision Tree obtained 81.3% respectively. In summary, the proposed system demonstrates the effectiveness of Naive Bayesian networks for multi-disease prediction and has the potential to improve disease diagnosis and management in the medical domain

Medical implementation practice and its medical performance evaluation of a giant makeshift hospital during the COVID-19 pandemic: An innovative model response to a public health emergency in Shanghai, China.

In confronting the sudden COVID-19 epidemic, China and other countries have been under great pressure to block virus transmission and reduce fatalities. Converting large-scale public venues into makeshift hospitals is a popular response. This addresses the outbreak and can maintain smooth operation of a country or region's healthcare system during a pandemic. However, large makeshift hospitals, such as the Shanghai New International Expo Center (SNIEC) makeshift hospital, which was one of the largest makeshift hospitals in the world, face two major problems: Effective and precise transfer of patients and heterogeneity of the medical care teams. To solve these problems, this study presents the medical practices of the SNIEC makeshift hospital in Shanghai, China. The experiences include constructing two groups, developing a medical management protocol, implementing a multi-dimensional management mode to screen patients, transferring them effectively, and achieving homogeneous quality of medical care. To evaluate the medical practice performance of the SNIEC makeshift hospital, 41,941 infected patients were retrospectively reviewed from March 31 to May 23, 2022. Multivariate logistic regression method and a tree-augmented naive (TAN) Bayesian network mode were used. We identified that the three most important variables were chronic disease, age, and type of cabin, with importance values of 0.63, 0.15, and 0.11, respectively. The constructed TAN Bayesian network model had good predictive values; the overall correct rates of the model-training dataset partition and test dataset partition were 99.19 and 99.05%, respectively, and the respective values for the area under the receiver operating characteristic curve were 0.939 and 0.957. The medical practice in the SNIEC makeshift hospital was implemented well, had good medical care performance, and could be copied worldwide as a practical intervention to fight the epidemic in China and other developing countries.

Идентификация угроз средствами обеспечения безопасности и выбор методами машинного обучения

The following tasks are considered: identifying the sources and nature of threats using the methods of naive Bayes and Bayesian trust networks to ensure security in the development, production and operation of on-board systems; selection of the optimal set of operations and measures of an organizational and technological na-ture, taking into account the expected costs or maximum security with restrictions on allowable costs. It is proposed to solve them by methods of machine learning and evolutionary optimization algorithms.

Dynamic Fresh Weight Prediction of Substrate-Cultivated Lettuce Grown in a Solar Greenhouse Based on Phenotypic and Environmental Data

The fresh weight of vegetables is an important index for the accurate evaluation of growth processes, which are affected by factors such as temperature and radiation fluctuation, especially in a passive solar greenhouse. Predicting dynamic growth indexed by fresh weight in a solar greenhouse remains a challenge. A novel method for predicting the dynamic growth of leafy vegetables based on the in situ sensing of phenotypic and environmental data of batches is proposed herein, enabling prediction of the dynamic fresh weight of substrate-cultivated lettuce grown in a solar greenhouse under normal water and fertilizer conditions. Firstly, multibatch lettuce cultivation experiments were carried out and batch datasets constructed by collecting growth environmental data and lettuce canopy images in real time. Secondly, the cumulative environmental factors and instantaneous fresh weights of the lettuce batches were calculated. The optimum response time in days was then explored through the most significant correlations between cumulative environmental factors and fresh weight growth. Finally, a dynamic fresh weight prediction model was established using a naive Bayesian network, based on cumulative environmental factors, instantaneous fresh weight, and the fresh weight increments of batches. The results showed that the computing time setpoint of cumulative environmental factors and instantaneous fresh weight of lettuce was 8:00 AM and the optimum response time was 12 days, and the average R2 values among samples from three batches reached 95.95%. The mean relative error (MRE) of fresh weight prediction 4 days into the future based on data from the current batch was not more than 9.57%. Upon introducing another batch of data, the prediction 7 days into the future dropped below 8.53% MRE; upon introducing another two batches, the prediction 9 days into the future dropped below 9.68% MRE. The accuracy was improved by the introduction of additional data batches, proving the model’s feasibility. The proposed dynamic fresh weight growth prediction model can support the automatic management of substrate-cultivated leafy vegetables in a solar greenhouse.

Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines

This paper presents the results of comparative studies on the implementation of machine learning methods in the damage intensity assessment of masonry buildings. The research was performed on existing residential buildings, subjected to negative impacts of the industrial environment induced by coal mining plants during their whole technical life cycle. The research was justified on the grounds of safety of use, as well as potential energy losses and CO2 emissions generated by the inefficient management of building materials resources resulting from poor planning of retrofitting. In this field, the research is in line with the global trends of large-scale retrofitting of existing buildings in European countries due to their thermal insulation parameters and seismic hazard. By combining this with the effects of material degradation throughout the technical lifecycle of buildings, the proposed methods allow for a more efficient approach to maintaining quality management of large groups of buildings, which is part of the sustainable development framework. Due to the multidimensionality of the undertaken problem and the necessity of mathematical representation of uncertainty, it was decided to implement a machine learning approach. The effectiveness of the following methods was analysed: probabilistic neural network, support vector machine, naive Bayes classification and Bayesian belief networks. The complexity of individual methods dictated the order of the adopted research horizon. Within such a research plan, both model parameters were learned, and model structure was extracted from the data, which was applied only to the approach based on Bayesian networks. The results of the conducted analyses were verified by assuming classification accuracy measures. Thus, a method was extracted that allows for the best realisation of the set research objective, which was to create a classification system to assess the intensity of damage to masonry buildings. The paper also presents in detail the characteristics of the described buildings, which were used as input variables, and assesses the effectiveness of the obtained results in terms of utilisation in practice.

Selected Artificial Intelligence Methods in the Risk Analysis of Damage to Masonry Buildings Subject to Long-Term Underground Mining Exploitation

This paper presents an advanced computational approach to assess the risk of damage to masonry buildings subjected to negative kinematic impacts of underground mining exploitation. The research goals were achieved using selected tools from the area of artificial intelligence (AI) methods. Ultimately, two models of damage risk assessment were built using the Naive Bayes classifier (NBC) and Bayesian Networks (BN). The first model was used to compare results obtained using the more computationally advanced Bayesian network methodology. In the case of the Bayesian network, the unknown Directed Acyclic Graph (DAG) structure was extracted using Chow-Liu’s Tree Augmented Naive Bayes (TAN-CL) algorithm. Thus, one of the methods involving Bayesian Network Structure Learning from data (BNSL) was implemented. The application of this approach represents a novel scientific contribution in the interdisciplinary field of mining and civil engineering. The models created were verified with respect to quality of fit to observed data and generalization properties. The connections in the Bayesian network structure obtained were also verified with respect to the observed relations occurring in engineering practice concerning the assessment of the damage intensity to masonry buildings in mining areas. This allowed evaluation of the model and justified the utility of the conducted research in the field of protection of mining areas. The possibility of universal application of the Bayesian network, both in the case of damage prediction and diagnosis of its potential causes, was also pointed out.

Probability Analysis of Construction Risk based on Noisy-or Gate Bayesian Networks

During construction risks’ probability assessment, it is challenging to obtain the joint probability distribution (JPD) of target risk systems, because every risk element's probability needs to be determined, known as the curse of dimensionality. This paper introduces a Noisy-or Gate Bayesian Network (NG-BN) model that integrates the Noisy-or Gate (NG) model and the Naive Bayesian Network (NBN) to address the problem. The NBN and the NG model's conditional independence assumptions’ gap is bridged by the Markov property. The proposed model requires only connection probabilities with high availability and reliability as the prior knowledge, thus substantially reduces the dimensionality of risk factors while retaining the ability of JPD reasoning. The model is illustrated and tested by a data analysis of the Zijingang Station construction project of Hangzhou Metro Line 5. The result demonstrates that the NG-BN can effectively accomplish the practical occurrence probability evaluation of construction risks. This study has a theoretical contribution as this model establishes a qualitative examination criterion of the Markov property. The proposed NG-BN performs better than the NBN on dimensionality reduction without diminishing the effectiveness of practical risk probability assessment. Its potential for reliability analysis in other engineering fields awaits further study.

A comparative analysis of text data classification accuracy and speed using neural networks, Bloom filter and naive Bayes

The object of research is the methods of fast classification for solving text data classification problems. The need for this study is due to the rapid growth of textual data, both in digital and printed forms. Thus, there is a need to process such data using software, since human resources are not able to process such an amount of data in full. A large number of data classification approaches have been developed. The conducted research is based on the application of the following methods of classification of text data: Bloom filter, naive Bayesian classifier and neural networks to a set of text data in order to classify them into categories. Each method has both disadvantages and advantages. This paper will reflect the strengths and weaknesses of each method on a specific example. These algorithms were comparatively among themselves in terms of speed and efficiency, that is, the accuracy of determining the belonging of a text to a certain class of classification. The work of each method was considered on the same data sets with a change in the amount of training and test data, as well as with a change in the number of classification groups. The dataset used contains the following classes: world, business, sports, and science and technology. In real conditions of the classification of such data, the number of categories is much larger than that considered in the work, and may have subcategories in its composition. In the course of this study, each method was analyzed using different parameter values to obtain the best result. Analyzing the results obtained, the best results for the classification of text data were obtained using a neural network.

A False Bank Accounts Detection Method Based on Naive Bayesian Network

At present, more and more cases of economic fraud are based on false bank accounts. Due to the limitation of the bank’s technology and ability to verify the true identity of customers, the online verification system of banks can only check the authenticity of the ID card, while the consistent verification of people and cards can only be completed manually by bank tellers. It is easy to produce errors if the consistency is judged by the teller’s manual naked eye. In this paper was proposed a method of constructing a naive Bayesian network classification model by automatically detect the authenticity of bank accounts to avoid the problem of economic fraud of false bank accounts, and verified the feasibility of this method through specific examples. The experimental results show that the method which has certain theoretical and practical significance, and provides reference for the detection of other false accounts.

An intelligent healthcare system for predicting and preventing dengue virus infection

Dengue is a mosquito-borne pandemic viral infection, which transmits to humans from Female Aedes albopictis or Aedes agypti mosquitoes. It progressively deteriorates the health of infected individuals and poses a high threat of human morbidity and mortality. This paper proposes an intelligent healthcare system which identifies, monitors, and alerts dengue virus (DeV) infected individuals and other stakeholders in real-time and control the DeV infection outbreak using cloud computing, internet of things and fog computing paradigms. The proposed system uses Naive Bayesian Network (NBN) for diagnosing the possibly DeV infected individuals and generating real-time alerts for suggesting and alerting the concerned stakeholders for taking on-time necessary actions at the fog subsystem. The proposed system also uses Social Network Analysis at the cloud subsystem, to provide Global Positioning Systems (GPS)-based global risk assessment of the DeV infection on Google Maps (Google-based web map service) and control DeV infection outbreak. The analysis of the experimental results acknowledges the efficiency of the NBN-based DeV infection diagnosis, alert generation, and GPS-based risk assessment functionality, of the proposed system, via various statistical measures and experimental approaches.

A Centralized and Dynamic Network Congestion Classification Approach for Heterogeneous Vehicular Networks

Network congestion-related studies consist mainly of two parts: congestion detection and congestion control. Several researchers have proposed different mechanisms to control congestion and used channel loads or other factors to detect congestion. However, the number of studies concerning congestion detection and going beyond into congestion prediction is low. On this basis, we decide to propose a method for congestion prediction using supervised machine learning. In this paper, we propose a Naive Bayesian network congestion warning classification method for Heterogeneous Vehicular Networks (HetVNETs) using simulated data that can be locally applied in a fog device in a HetVNET. In addition, we propose a centralized and dynamic cloud-fog-based architecture for HetVNET. The Naive Bayesian network congestion warning classification method can be applied in this architecture. Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Random Forest classifiers, which are popular methods in classification problems, are considered to generate congestion warning prediction models. Numerical results show that the proposed Naive Bayesian classifier is more reliable and stable and can accurately predict the data flow warning state in HetVNET. Moreover, based on the obtained simulation results, applying the proposed congestion classification approach can improve the network’s performance in terms of the packet loss ratio, average delay and average throughput, especially in the dense vehicular environments of HetVNET.