Bayesian Discriminant Method Research Articles

Impact of Mild SARS-CoV-2 Infection on Hematological Parameters and Suitability of Apheresis Platelets Donation in Blood Donors

To investigate the effects of mild SARS-CoV-2 infection on hematological parameters of adult blood donors and the suitability of apheresis platelet donation, the changes of the hematological parameters in blood donors with mild infection of the SARS-CoV-2 Omicron variant strain were evaluated. Seventy-two blood donors with mild COVID-19 symptoms who donated consecutive apheresis platelets for 3 times from December 2022 to January 2023, 42 cases among which were included in the infection-positive group, and 30 cases in the suspected infection group. Forty-two donors un-vaccinated against SARS-CoV-2, un-infected, and donated three consecutive apheresis platelets from October to November 2022 were included in the control group. The changes of blood routine testing in the positive group and the suspected infection group were retrospectively compared before (Time1) and after (Time2 and Time3) the onset of symptoms, three consecutive times (Time1, Time2, Time3) in the control group by repeated measures analysis of variance. The Bayesian discriminant method was used to establish a discriminant equation to determine whether the recent infection of SARS-CoV-2 occurred or not. Simple effect of the number times of tests in the positive and suspected infection groups was significant（ Finfection-positive group=6.98, P < 0.001, partial η2=0.79, Fsuspected infection group=4.31, P < 0.001, partial η2=0.70）. The positive group and the suspected infection group had lower RBC, HCT, and HGB, and higher PLT and PCT at Time2 compared to Time1 and Time3(P < 0.05). The positive group and the suspected infection group showes RDW-CV and RDW-SD at Time3 higher than Time1 and Time2 (P < 0.001). The simple effect of the number times of tests in the control group was not significant ( F=0.96, P =0.55, partial η2=0.34). The difference of the whole blood count parameters in the control group for three times was not statistically significant (P >0.05). We established a discriminant equation to determine whether the recent infection of SARS-CoV-2 occurred or not. The equation had an eigenvalue of 0.22, a canonical correlation of 0.43 (χ2=27.81, P < 0.001), and an analysis accuracy of 72.9%. The hematological indicators of RBC, HCT, HGB, PLT, PCT, RDW-CV and RDW-SD in blood donors who had infected with mild COVID-19 showed dynamic changes. The discriminant equation for whether they are infected recently with COVID-19 has a high accuracy rate.

Quantitative Method of Classification and Discrimination of a Porous Carbonate Reservoir Integrating K‐means Clustering and Bayesian Theory

AbstractReservoir classification is a key link in reservoir evaluation. However, traditional manual means are inefficient, subjective, and classification standards are not uniform. Therefore, taking the Mishrif Formation of the Western Iraq as an example, a new reservoir classification and discrimination method is established by using the K‐means clustering method and the Bayesian discrimination method. These methods are applied to non‐cored wells to calculate the discrimination accuracy of the reservoir type, and thus the main reasons for low accuracy of reservoir discrimination are clarified. The results show that the discrimination accuracy of reservoir type based on K‐means clustering and Bayesian stepwise discrimination is strongly related to the accuracy of the core data. The discrimination accuracy rate of Type I, Type II, and Type V reservoirs is found to be significantly higher than that of Type III and Type IV reservoirs using the method of combining K‐means clustering and Bayesian theory based on logging data. Although the recognition accuracy of the new methodology for the Type IV reservoir is low, with average accuracy the new method has reached more than 82% in the entire study area, which lays a good foundation for rapid and accurate discrimination of reservoir types and the fine evaluation of a reservoir.

Classification of English Translation Teaching Models based on Multiple Intelligence Theory

In order to improve the quality of English translation teaching, this paper combines the theory of multiple intelligence to classify the English translation teaching process. Moreover, this paper adopts Fisher’s discriminant method and Bayesian discriminant method to classify the English translation teaching samples. In order to improve the discrimination accuracy of the extreme learning machine algorithm, this paper applies the particle swarm optimization extreme learning machine algorithm to the research on the classification of English translation teaching samples and proposes an intelligent English classification teaching model based on the actual situation of English translation teaching. In addition, this paper verifies the system model proposed in this paper by evaluating the teaching method. The research shows that the classification model of English translation teaching mode based on the theory of multiple intelligence proposed in this paper has a certain effect, which can promote the effect of English translation teaching.

An intelligent approach for reservoir quality evaluation in tight sandstone reservoir using gradient boosting decision tree algorithm

Abstract This article focuses on the study of identifying the quality of tight sandstone reservoirs based on machine learning. The machine learning method – Gradient Boosting Decision Tree (GBDT) algorithm is used to design and classify reservoir quality. First, it is based on logging data, core observation, cast thin section, and reservoir physical statistics. The permeability, porosity, resistivity, mud content, sand-to-ground ratio, and sand thickness were preferred as reservoir evaluation criteria in the area, and the gray correlation method was used to obtain reservoir quality categories and construct training datasets. The machine learning GBDT algorithm is used to train and test the obtained dataset. It is found that the recognition accuracy of the GBDT model is 95% by confusion matrix analysis. In addition, it is compared with four commonly used reservoir prediction methods (Bayesian discriminant method, random forest, support vector machine, and artificial neural network) for verifying the reliability of the GBDT model. Finally, the GBDT model is used to identify the reservoir quality of the study area, and it is well verified in the production data. The research results show that the GBDT model can become an important tool for rapid and real-time tight sandstone reservoir evaluation.

Use of Graphical Methods in The Diagnostic Of Parametric Probability Distributions for Bivariate Lifetime Data in Presence of Censored Data

The choice of an appropriate bivariate parametrical probability distribution for pairs of lifetime data in presence of censored observations usually is not a simple task in many applications. Each existing bivariate lifetime probability distribution proposed in the literature has different dependence structure. Commonly existing classical or Bayesian discrimination methods could be used to discriminate the best among different proposed distributions, but these techniques could not be appropriate to say that we have good fit of some particular model to the data set. In this paper, we explore a recent dependence measure for bivariate data introduced in the literature to propose a graphical and simple criterion to choose an appropriate bivariate lifetime distribution for data in presence of censored data.

An intelligent approach for reservoir quality evaluation in tight sandstone reservoir using gradient boosting decision tree algorithm - A case study of the Yanchang Formation, mid-eastern Ordos Basin, China

The identification of high-quality areas with relatively high porosity and high permeability zones in tight sandstone reservoirs has always been an enormous challenge beneath the background of the ultra-low petrophysical properties. Meanwhile, machine learning has made enormous breakthroughs in many fields as an important branch of artificial intelligence. Hence, the present study is focused on examining how machine learning technology can help improve the accuracy of reservoir quality assessment. To this end, a machine learning method known as the gradient boosting decision tree (GBDT) algorithm is used to design and perform reservoir quality evaluation. First, a typical data set for reservoir quality evaluation is constructed on the basis of the observed lithofacies and diagenetic facies characteristics, and is used to set six logging curves reflecting reservoir characteristics as the input variables and the corresponding reservoir quality categories of the samples as the output results. Then the intelligent GBDT algorithm is applied in order to train the constructed data set. Through confusion matrix analysis, the accuracy of the GBDT model is found to be about 89%. In addition, five commonly-used prediction methods (namely, factor analysis, Bayesian discriminant method, cluster analysis, random forest, and artificial neural network (ANN)) are executed for the lithofacies or diagenetic facies to confirm the reliability and application effectiveness of the GBDT model. Finally, the GBDT model is used to predict the reservoir quality in the study area, which is also well verified in terms of production data. The findings of this study demonstrate that, among the many available machine learning algorithms, the GBDT algorithm has the potential to become an essential tool for fast real-time reservoir quality evaluation.

A Bayesian discriminant analysis method under semiparametric density ratio models

We propose a semiparametric Bayesian discriminant analysis method. Statistical simulation shows that when the hypothesis of normal distribution is satisfied, our method is comparable to and even slightly better than the traditional method; when the hypothesis of normal distribution is not satisfied, our method is obviously better than the traditional one. We also apply our method to analyze a real data set and find that our method is better than the traditional one. Finally, we point out that implementation of our method is easy since the usual polytomous logistic regression procedures in many statistical softwares can be employed.

Mid and long-term hydrological classification forecasting model based on KDE-BDA and its application research

In view of the uncertainties in the current mid and long-term hydrological forecast results, a mid and long-term hydrological classification forecasting model based on KDE-BDA is built and its application research is performed. The kernel density estimation method (KDE) is used to improve the distribution density function estimation method in the conventional Bayesian discriminant method (BDA). Based on this, a mid and long-term hydrological classification prediction model based on KDE-BDA was built. An example of its application is demonstrated in the runoff forecast of the Danjiangkou Reservoir in the autumn of September. The results show that different forecasting factors have different forecasting ability for runoff classification (flood, normal, dry). Among them, the 500hPa height field factor has higher forecasting ability for the Danjiangkou runoff category (flood, normal, dry), and the 100hPa height field factor, SST and circulation characteristics have a good indication of the runoff’s flood state. The results of Half-Brier score show that the combined forecasting model combines the forecasting advantages of each factor, so the forecasting effect is the best. The pass rate in the simulation period was 89.8%, and the pass rate in the inspection period was 87.5%. The simulation and forecast results were relatively stable.

Method to Select Copula Functions

Copula functions have been extensively used in applied statistics, becoming a good alternative for modeling the dependence of multivariate data. Each copula function has a different dependence structure. An important issue in these applications is the choice of an appropriate copula function model for each one; thus common classical or Bayesian discrimination methods might not be appropriate for determining the best copula. Considering only the special case of bivariate data, we propose a procedure obtained from a recently introduced dependence measure for selecting an appropriate copula for the statistical data analyses.

The Identification Method for Machilus Nees Species Based on Visible-Near Infrared Spectral Analysis Technology

A novel technology for rapid identification of Machilus Nees species using the visible-near infrared spectrum (300-1100 nm) is described in this study. The reflectivities of new leaves of seedlings from 9 species of the genus Machilus Nees were collected. Stepwise discriminant analysis was applied to the spectral information of the leaves, and 18 unique bands were selected from 126 bands total. After obtaining the spectral information for the unique bands, the Bayesian discriminant method was applied to establish the discriminant analysis model for Machilus Nees species. According to the discrimination model, combinations of 6, 12, and 18 unique bands were selected, and the discrimination accuracies of 180 training samples reached 76.111%, 83.889%, and 93.889%, respectively, while the accuracies of 90 testing samples were 77.778%, 84.444%, and 95.556%, respectively. These results validated the discrimination model for Machilus Nees species constructed from the spectral information of 18 selected unique bands. The application of visible-near infrared spectrum technology combined with discriminant analysis could provide a novel approach for the rapid and accurate identification of Machilus Nees species.

Development of a generalised dilution-based stability graph for open stope design

With decreasing profits, optimising the size of stopes to minimise dilution is a step towards achieving a productive and profitable mining operation. The stability graph was developed for the determination of stope sizes in wide orebodies to control dilution in bulk mining. Unfortunately, this graph is qualitative and stopes can only be described as stable, unstable or cave. The equivalent linear overbreak slough stability graph is quantitative but only gives average depths of failure. The graph is also only applicable to narrow vein orebodies. This paper presents a generalised quantitative dilution-based stability graph independent of orebody width. Data were collected from six underground metalliferous mines across Australia and statistically analysed using logistic regression and the Bayesian likelihood discrimination methods. The graphs provide the mining engineer the flexibility to design open stope sizes based on acceptable dilution to a given operation. More data could improve the reliability of the graphs.

Reduced field-of-view DTI segmentation of cervical spine tissue

The number of diffusion tensor imaging (DTI) studies regarding the human spine has considerably increased and it is challenging because of the spine’s small size and artifacts associated with the most commonly used clinical imaging method. A novel segmentation method based on the reduced field-of-view (rFOV) DTI dataset is presented in cervical spinal canal cerebrospinal fluid, spinal cord grey matter and white matter classification in both healthy volunteers and patients with neuromyelitis optica (NMO) and multiple sclerosis (MS). Due to each channel based on high resolution rFOV DTI images providing complementary information on spinal tissue segmentation, we want to choose a different contribution map from multiple channel images. Via principal component analysis (PCA) and a hybrid diffusion filter with a continuous switch applied on fourteen channel features, eigen maps can be obtained and used for tissue segmentation based on the Bayesian discrimination method. Relative to segmentation by a pair of expert readers, all of the automated segmentation results in the experiment fall in the good segmentation area and performed well, giving an average segmentation accuracy of about 0.852 for cervical spinal cord grey matter in terms of volume overlap. Furthermore, this has important applications in defining more accurate human spinal cord tissue maps when fusing structural data with diffusion data. rFOV DTI and the proposed automatic segmentation outperform traditional manual segmentation methods in classifying MR cervical spinal images and might be potentially helpful for detecting cervical spine diseases in NMO and MS.

Discrimination of the Production Season of Chinese Green Tea by Chemical Analysis in Combination with Supervised Pattern Recognition

High-performance liquid chromatography (HPLC) has been used to quantify levels of free amino acids, catechins, and caffeine in Chinese green tea. Levels of free amino acids and catechins in green tea leaves show obvious variation from spring to summer, which is useful information to identify the production season of commercial green tea. Supervised pattern recognition methods such as the K-nearest neighbor (KNN) method and Bayesian discriminant method (a type of linear discriminant analysis (LDA)) were used to discriminate between the production seasons of Chinese green tea. The optimal accuracy of the KNN method was ≤97.61 and ≤94.80% as validated by resubstitution and cross-validation tests, respectively, and that of LDA was ≤95.22 and ≤93.54%, respectively. Compared with LDA, the KNN method did not require a Gaussian distribution and was more accurate than LDA. The KNN method in combination with chemical analysis is recommended for discrimination of the production seasons of Chinese green tea.

Modelos de volatilidade estocástica em séries financeiras: uma aplicação para o IBOVESPA

Neste artigo apresentamos uma análise Bayesiana para o modelo de volatilidade estocástica (SV) e uma forma generalizada deste, cujo objetivo é estimar a volatilidade de séries temporais financeiras. Considerando alguns casos especiais dos modelos SV usamos algoritmos de Monte Carlo em Cadeias de Markov e o software WinBugs para obter sumários a posteriori para as diferentes formas de modelos SV. Introduzimos algumas técnicas Bayesianas de discriminação para a escolha do melhor modelo a ser usado para estimar as volatilidades e fazer previsões de séries financeiras. Um exemplo empírico de aplicação da metodologia é introduzido com a série financeira do IBOVESPA.

Prediction of N ε-acetylation on internal lysines implemented in Bayesian Discriminant Method

Protein acetylation is an important and reversible post-translational modification (PTM), and it governs a variety of cellular dynamics and plasticity. Experimental identification of acetylation sites is labor-intensive and often limited by the availability of reagents such as acetyl-specific antibodies and optimization of enzymatic reactions. Computational analyses may facilitate the identification of potential acetylation sites and provide insights into further experimentation. In this manuscript, we present a novel protein acetylation prediction program named PAIL, prediction of acetylation on internal lysines, implemented in a BDM (Bayesian Discriminant Method) algorithm. The accuracies of PAIL are 85.13%, 87.97%, and 89.21% at low, medium, and high thresholds, respectively. Both Jack–Knife validation and n-fold cross-validation have been performed to show that PAIL is accurate and robust. Taken together, we propose that PAIL is a novel predictor for identification of protein acetylation sites and may serve as an important tool to study the function of protein acetylation. PAIL has been implemented in PHP and is freely available on a web server at: http://bioinformatics.lcd-ustc.org/pail.

A homogeneous mobile robot team that is fault-tolerant

This paper introduces a design methodology of a fault-tolerant autonomous multi-robot system (MRS). An important fundamental topic for this type of system is the design of an on-line autonomous behavior acquisition mechanism that is capable of developing cooperative roles as well as assigning them to a robot appropriately in a noisy embedded environment. Our approach is to apply reinforcement learning that adopts the Bayesian discrimination method for segmenting a continuous state space and a continuous action space simultaneously. In addition, a neural network is provided for predicting the average of the other robots’ postures at the next time step in order to stabilize the reinforcement-learning environment. Computer simulations are conducted to illustrate the fault-tolerance of our MRS against a system change that occurs after the MRS achieves stable behavior.

Autonomous Role Assignment in a Homogeneous Multi-Robot System

This paper describes an approach for controlling an autonomous homogeneous multi-robot system. An extremely important topic for this type of system is the design of an on-line autonomous behavior acquisition mechanism that is capable of developing cooperative roles as well as assigning them to a robot appropriately in a noisy embedded environment. Our approach applies reinforcement learning that adopts the Bayesian discrimination method for segmenting a continuous state space and a continuous action space simultaneously. In addition, a neural network is provided for predicting the average of the other robots’ postures at the next time step in order to stabilize the reinforcement learning environment. The proposed method is validated through computer simulations as well as our hand-made multi-robot system.

4106 身体性認知に基づくマルチロボットシステムの設計

This paper proposes a design method for a multi-robot system that acquires cooperative behaviors autonomously. From embodied cognitive point of view, a robot should have the capability of acquiring appropriate cooperative behaviors through its experience. One of the most important issues for this function is how to design an on-line autonomous behavior acquisition mechanism capable of developing the robot's role in an embedded environment. In our approach, reinforcement learning that uses Bayesian discrimination method for segmenting the continuous state and action spaces simultaneously is applied to a robot for behavior acquisition. In addition to this, in order to support the learning in a dynamic environment that originates from the other learning robots, neural networks are provided for predicting the other robots' moves at the next time step. The output signals are utilized as the sensory information of the reinforcement learning to increase the stability of the learning problem. A homogeneous real robot system will be built for evaluating our approach.

Two Classifier Systems for Reinforcement Learning of Motion Patterns

Two reinforcement learning technique for adaptive segmentation of the continuous state space have been formulated in this paper. The first technique uses weight vectors and continuous matching function, while the second technique makes use of Bayesian discrimination method, where the segmented state space is represented by Bayes boundaries. The proposed techniques have been tested under simulation for a navigation task, where the agent does not have a priori knowledge of the environment and its own internal model. The simulation results show that the agent can segment its continuous state space adaptively and reach the goal.