Structural Optimization of Causal Driven Model Based on Bayesian Network in High-dimensional Data Classification

The intermittence and fluctuation of renewable energy bring significant uncertainty to the power system, which enormously increases the operational risks of the power system. The development of efficient interval prediction models can provide data support for decision making and help improve the economy and reliability of energy interconnection operation. The performance of Bayesian deep learning models and Bayesian shallow neural networks in short-term interval prediction of photovoltaic power is compared in this study. Specifically, an LSTM Approximate Bayesian Neural Network model (ABNN-I) is built on the basis of the deep learning and Monte Carlo Dropout method. Meanwhile, a Feedforward Bayesian Neural Network (ABNN-II) model is introduced by Feedforward Neural Network and the Markov Chain Monte Carlo method. To better compare and verify the interval prediction capability of the ABNN models, a novel clustering method with three-dimensional features which include the number of peaks and valleys, the average power value, and the non-stationary measurement coefficient is proposed for generating sunny and non-sunny clustering sets, respectively. Results show that the ABNN-I model has an excellent performance in the field of photovoltaic short-term interval forecasting. At a 95% confidence level, the interval coverage from ABNN-I to ABNN-II can be increased by up to 3.1% and the average width of the interval can be reduced by 56%. Therefore, with the help of the high computational capacity of deep learning and the inherent ability to quantify uncertainty of the interval forecast from Bayesian methods, this research provides high-quality interval prediction results for photovoltaic power prediction and solves the problem of difficult modeling for over-fitting that exists in the training process, especially on the non-sunny clustering sets.

Previous work has proposed the ontology-based semi-automatic generation of antipattern Bayesian Network(BN) models. The generated BN model can be used to illustrate the effects of uncertainty on antipatterns using Bayesian propagation. This can guide users in detecting particular antipattern attributes of importance based on uncertain ontological information. However, the proposed approach has been implemented in the Protege ontology editor environment and requires human intervention to specify how the BN model will be generated. The fully automated generation of ontology-based antipattern BN models still remains an open issue. SPARSE is an OWL ontology based intelligent system that assists software project managers in the antipattern detection process. In this paper, we propose the use of the resulting detected antipatterns of SPARSE, their attributes (i.e. causes, symptoms, consequences) and the ontological relationships between these attributes, in order to automatically generate BN models of the detected antipatterns. We illustrate how this approach can be implemented using an example of 8 antipattern attributes of 6 inter-related antipatterns detected using SPARSE.

Minimum training sample size requirements for achieving high prediction accuracy with the BN model: A case study regarding seismic liquefaction

With the rapid development of the Internet, privacy protection in big data has become an important research hotspot in the field of information security. Among them, differential privacy protection is the most widely used big data privacy protection technology today. However, the existing differential privacy protection technology cannot fully and effectively deal with the problem of high-dimensional privacy big data. Although the private data publishing method based on a Bayesian network can effectively deal with the conversion problem of high-dimensional data sets to low-dimensional data sets, this method also has some defects and deficiencies. Therefore, this paper proposes a large data source differential privacy protection algorithm (NIE-PrivBayes algorithm) based on normalized information entropy Bayesian network and proposes three improvements to the original PrivBayes algorithm. First, the original data set is protected by differential privacy on the client side. Secondly, the Bayesian network is constructed by the joint probability distribution obtained by the EM algorithm. Finally, the existing Bayesian network construction algorithm is optimized in the aspects of normalized information entropy. Based on significantly reducing the computational overhead, the effectiveness and availability of publishing high-dimensional data sets are realized under the premise of differential privacy protection.

A method for obtaining the Bayesian network (BN) representation of a sensor's measurement process is developed so that the problems of sensor fusion and management can be approached from a unified point of view. Uncertainty, reliability, and causal information embedded in the sensor data are used to build the BN model of a sensor. The method is applied to model ground-penetrating radar, electromagnetic induction, and infrared sensors for humanitarian demining. Structural and parameter learning algorithms are employed to encode relationships among mine features, sensor measurements, and environmental conditions in the BN model. Inference is used to estimate target features in the presence of heterogeneous soil and varying environmental conditions. A multisensor fusion technique operating on BN models is developed to exploit the complementarity of the sensor measurements. Through the same approach, a BN classifier is obtained to estimate the target typology. The BN models and classifier also compute so-called confidence levels that quantify the uncertainty associated with the feature estimates and the classification decisions. The effectiveness of the approach is demonstrated by implementing these BN tools for the detection and classification of metal and plastic landmines that are characterized by different shape, size, depth, and metal content. Through BN fusion, the accuracy of the feature estimates is improved by up to 64% with respect to single-sensor measurements, and the number of objects that are both detected and classified is increased by up to 62%.

The analysis of high-dimensional microbiome multiomics datasets is crucial for understanding the complex interactions between microbial communities and host physiological states across health and disease conditions. Despite their importance, current methods, such as the microbe-metabolite vectors approach, often face challenges in predicting metabolite abundances from microbial data and identifying keystone species. This arises from the vast dimensionality of metagenomics data, which complicates the inference of significant relationships, particularly the estimation of co-occurrence probabilities between microbes and metabolites. Here we propose the variational Bayesian microbiome multiomics (VBayesMM) approach, which aims to improve the prediction of metabolite abundances from microbial metagenomics data by incorporating a spike-and-slab prior within a Bayesian neural network. This allows VBayesMM to rapidly and precisely identify crucial microbial species, leading to more accurate estimations of co-occurrence probabilities between microbes and metabolites, while also robustly managing the uncertainty inherent in high-dimensional data. Moreover, we have implemented variational inference to address computational bottlenecks, enabling scalable analysis across extensive multiomics datasets. Our large-scale comparative evaluations demonstrate that VBayesMM not only outperforms existing methods in predicting metabolite abundances but also provides a scalable solution for analyzing massive datasets. VBayesMM enhances the interpretability of the Bayesian neural network by identifying a core set of influential microbial species, thus facilitating a deeper understanding of their probabilistic relationships with the host.

Survival prediction for gallbladder carcinoma after curative resection: Comparison of nomogram and Bayesian network models

Bayesian Networks is one of simple Probabilistic Graphical Models are built from theory of bayes probability and graph theory. Probability theory Is directly related to data while graph theory directly related to the form representation to be obtained. Multinomial Bayesian Network method is one method that involves the influence of spatial linkages suggest a link between rainfall observation stations. The objective of this study was seek the result of the model probabilistic a graph Multinomial Bayesian Network and apply it in forecasting with Oldeman classification based on one or two rainfall stations are known. This research uses simulated data for 14 stations respectively each 300 sets of data. The data generated is normal distribution of data based on parameters that have been determined and classified using the classification Oldeman. Bayesian Network structure constructed using the K2 algorithm. Markov chain transition matrix is formed based on the Bayesian of the nodes are directional. Model of Multinomial Bayesian Network was established based on Markov transition matrices. The result of probability model can predict the probability of rainfall in some stations based on one or two rainfall stations are known, which is a model graph with 14 nodes and 13 arcs.

Stroke is a major chronic non-communicable disease with high incidence, high mortality, and high recurrence. To comprehensively digest its risk factors and take some relevant measures to lower its prevalence is of great significance. This study aimed to employ Bayesian Network (BN) model with Max-Min Hill-Climbing (MMHC) algorithm to explore the risk factors for stroke. From April 2019 to November 2019, Shanxi Provincial People's Hospital conducted opportunistic screening for stroke in ten rural areas in Shanxi Province. First, we employed propensity score matching (PSM) for class balancing for stroke. Afterwards, we used Chi-square testing and Logistic regression model to conduct a preliminary analysis of risk factors for stroke. Statistically significant variables were incorporated into BN model construction. BN structure learning was achieved using MMHC algorithm, and its parameter learning was achieved with Maximum Likelihood Estimation. After PSM, 748 non-stroke cases and 748 stroke cases were included in this study. BN was built with 10 nodes and 12 directed edges. The results suggested that age, fasting plasma glucose, systolic blood pressure, and family history of stroke constitute direct risk factors for stroke, whereas sex, educational levels, high density lipoprotein cholesterol, diastolic blood pressure, and urinary albumin-to-creatinine ratio represent indirect risk factors for stroke. BN model with MMHC algorithm not only allows for a complicated network relationship between risk factors and stroke, but also could achieve stroke risk prediction through Bayesian reasoning, outshining traditional Logistic regression model. This study suggests that BN model boasts great prospects in risk factor detection for stroke.

Multisource geospatial data contains a lot of information that can be used for environment assessment and management. In this paper, four environmental indicators representing typical human activities in Yellow River Delta, China are extracted from multisource geospatial data. By analysing the causal relationship between these human-related indicators and NDVI, a Bayesian network (BN) model is developed. Part of the raster data pre-processed using GIS is used for training the BN model, and the other data is used for model testing. Sensitivity analysis and performance assessment showed that the BN model was good enough to reveal the impacts of human activities on land vegetation. With the trained BN model, the vegetation change under three different scenarios was also predicted. The results showed that multisource geospatial data could be successfully collated using the GIS-BN framework for vegetation detection.

The classification of high dimensional data is a challenging problem due to the presence of redundant and irrelevant features in a higher amount. These unwanted features degrade accuracy and increase the computational complexity of machine learning algorithms. In this paper, we propose a hybrid method that integrates the complementary strengths of feature selection and feature weighting approaches for improving the classification of high dimensional data on the Nearest Neighbor classifier. Specifically, we suggest four strategies that combine filter and wrapper methods of feature selection and feature weighting. Experiments are performed on 12 high dimensional datasets and outcomes are supported by Friedman as well as Holm statistical tests for validation. Extended Adjusted Ratio of Ratios is used to recognize the best method considering accuracy, feature selection, and runtime. The results show that two proposed strategies outperform other well-known methods in accuracy and features reduction. The hybrid feature selection-feature weighting wrapper method is the best among all in accuracy while the hybrid feature selection filter-feature weighting wrapper method is the most suitable for reducing features and runtime. Thus, the promising outcomes validate the importance of hybridizing feature selection and feature weighting while dealing with high dimensional data.

Error handling in multimodal voice-enabled interfaces of tour-guide robots using graphical models

A Hybrid Modelling Framework for Ecosystem-Based Climate Change Adaptation Using System Dynamics and Bayesian Networks

Analysis for warning factors of type 2 diabetes mellitus complications with Markov blanket based on a Bayesian network model

Bayesian Networks (BNs) are increasingly recognised as a useful tool for ecological modelling due to their ability to incorporate a broad range of data types and explicit representation of uncertainty through the use of probabilities. They allow considerable flexibility with respect to the detail and focus of the models allowing conceptually simple habitat condition models or more complex mechanistic representation of ecological response. However, BN outputs are sensitive to the model structure, particularly the selection and linking of variables and how the states are defined for each variable. In this paper, we use recursive partitioning to inform the configuration of the structure of BNs, used for ecological response modelling.