Bayesian Classification Method Research Articles

Exploration of Fingerprints and Data Mining-based Prediction of Some Bioactive Compounds from Allium sativum as Histone Deacetylase 9 (HDAC9) Inhibitors.

Histone deacetylase 9 (HDAC9) is an important member of the class IIa family of histone deacetylases. It is well established that over-expression of HDAC9 causes various types of cancers including gastric cancer, breast cancer, ovarian cancer, liver cancer, lung cancer, lymphoblastic leukaemia, etc. The important role of HDAC9 is also recognized in the development of bone, cardiac muscles, and innate immunity. Thus, it will be beneficial to find out the important structural attributes of HDAC9 inhibitors for developing selective HDAC9 inhibitors with higher potency. The classification QSAR-based methods namely Bayesian classification and recursive partitioning method were applied to a dataset consisting of HADC9 inhibitors. The structural features strongly suggested that sulphur-containing compounds can be a good choice for HDAC9 inhibition. For this reason, these models were applied further to screen some natural compounds from Allium sativum. The screened compounds were further accessed for the ADME properties and docked in the homology-modelled structure of HDAC9 in order to find important amino acids for the interaction. The best-docked compound was considered for molecular dynamics (MD) simulation study. The classification models have identified good and bad fingerprints for HDAC9 inhibition. The screened compounds like ajoene, 1,2 vinyl dithiine, diallyl disulphide and diallyl trisulphide had been identified as compounds having potent HDAC9 inhibitory activity. The results from ADME and molecular docking study of these compounds show the binding interaction inside the active site of the HDAC9. The best-docked compound ajoene shows satisfactory results in terms of different validation parameters of MD simulation study. This in-silico modelling study has identified the natural potential lead (s) from Allium sativum. Specifically, the ajoene with the best in-silico features can be considered for further in-vitro and in-vivo investigation to establish as potential HDAC9 inhibitors.

Design and Development of Traditional Elements in Visual Communication Design under the Integration of Virtual Reality Technology

Abstract This paper proposes a method based on visual communication to generate two-dimensional motion graphics that can enhance the perception of depth and three-dimensional structure in stereoscopic vision. Through eye tracking and calibration, gaze points in 3D space are calculated and applied to Chinese traditional elements in visual communication design. Feature vectors and improved plain Bayesian classifier methods are used to achieve user gesture recognition in virtual design and construct interaction design scenarios based on virtual reality technology. Lastly, the performance impact of augmented reality technology on traditional element design is investigated through a questionnaire survey. Based on the eight samples of conventional elements, the evaluation factors with the highest scores were found to be 10. In the subjective assessment of visual communication design, traditional components fall between mediocre and good, according to the ratings of all 18 assessment factors, which range from 0.2 to 1. The study presented in this paper effectively integrates virtual reality technology into the graphical design of traditional elements, significantly enhancing their visual expression.

SENTIMENT ANALYSIS TECHNOLOGY FOR USER FEEDBACK SUPPORT IN E-COMMERCE SYSTEMS BASED ON MACHINE LEARNING

Context. The interaction between a company and its target audience has been studied for centuries. From the very beginning of commercial relations, the relationship between the service provider and the recipient has been valued almost above all else. Trade is built on trust and respect. The image of an entrepreneur is often more important than the product he sells. For hundreds of years, the relationship between the merchant and the buyer, the entrepreneur and the client has not lost its importance, and in the era of mass digitalization, the quality of the relationship between the company and the target audience of different sizes and professional feedback support with clients often start the success of e-business. To provide these additional tools and information technologies to help businessmen monitor e-business development opportunities in a specific location, as well as establish feedback with users through social networks and mass media. Obtaining such tools will significantly expand the vision of market opportunities for ebusiness, it will clarify which of them make sense to invest in, and which ones are not worth paying time for. Also see what idea has the future and what business model needs to be implemented/maintained/developed for the rapid development of territorial/interregional e-business. It will also help to understand which levers have the greatest effect for business changes: what not to touch, and what policies to change to ensure high speed in the implementation of the plan based on the analysis of relevant research results, for example, to receive: direct feedback from customers, the dynamics of changes in overall satisfaction or interest of the target audience and advantages/disadvantages from users using NLP analysis; support for the development of e-business in relation to the location of their enterprise and the best directions; – graphs of business development (improvement/deterioration) depending on the content of comments.
 Objective of the study is to develop information technology to support the development of e-business by analyzing business locations, processing feedback from users, analyzing and classifying customer feedback in real time from social networks: Twitter, Reddit, Facebook and others using deep learning and Natural methods. Language Processing of Ukrainian-speaking and Englishspeaking texts.
 Method. NLP-methods were used to analyze the opinions of users and customers. Among the methods of implementing the main functions of English-language news classification, the following machine learning methods are used: naive Bayesian classifier, logistic regression, and the method of support vectors. The Naive Bayes algorithm was used to classify Ukrainian-language user feedback, as it performs well on small amounts of data, is easy to train and operate, and works well with text data. Naive Bayes classifier is a very good option for our system and considering that the number of responses in the dataset is smaller compared to the averages.
 Results. A machine learning model was developed for the analysis and classification of Ukrainian- and English-language reviews from users of e-commerce systems.
 Conclusions. The created model shows excellent classification results on test data. The overall accuracy of the sentimental model for the analysis of Ukrainian-language content is quite satisfactory, 92.3%. The logistic regression method coped best with the task of analyzing the impact of English-language news on the financial market, which showed an accuracy of 75.67%. This is certainly not the desired result, but it is the largest indicator of all considered. The support vector method (SVM) coped somewhat worse with the task, which showed an accuracy of 72.78%, which is a slightly worse result than the one obtained thanks to the logistic regression method. And the naïve Bayesian classifier method did the worst with the task, which achieved an accuracy of 71.13%, which is less than the two previous methods.

Searching for dark matter subhalos in the Fermi-LAT catalog with Bayesian neural networks

About a third of the γ-ray sources detected by the Fermi Large Area Telescope (Fermi-LAT) remain unidentified, and some of these could be exotic objects such as dark matter subhalos. We present a search for these sources using Bayesian neural network classification methods applied to the latest 4FGL-DR3 Fermi-LAT catalog. We first simulate the γ-ray properties of dark matter subhalos using models from N-body simulations and semi-analytical approaches to the subhalo distribution. We then assess the detectability of this sample in the 4FGL-DR3 catalog using the Fermi-LAT analysis tools. We train our Bayesian neural network to identify candidate dark matter subhalos among the unidentified sources in the 4FGL-DR3 catalog. Our results allow us to derive conservative bounds on the dark matter annihilation cross section by excluding unidentified sources classified as astrophysical-like by our networks. We estimate the number of candidate dark matter subhalos for different dark matter masses and provide a publicly available list for further investigation. Our bounds on the dark matter annihilation cross section are comparable to previous results and become particularly competitive at high dark matter masses.

APPLICATION OF MACHINE LEARNING ALGORITMS FOR HEART DISEASE PREDICTION

This paper focuses on the application of machine learning algorithms to predict cardiovascular diseases (CVDs). Every year a large number of deaths are registered all over the world. According to the World Health Organisation, CVDs are the leading cause of high mortality in the world. One of the necessary preventive measures to reduce mortality from CVDs is the timely prediction of diseases in people at high risk of such diseases. Specially developed scales and machine learning algorithms are now being used for the timely prediction of CVDs.
 Background. To predict heart disease, algorithms are often used: naive Bayesian classifier (Gaussian Naïve Bayes Classificator, GNBC), k-nearest neighbours (K-Nearest Neghboors, KNN), decision tree (Decision Tree, DT). In domestic literature, there are known works devoted to the application of SWD prediction using Adam gradient algorithm in deep neural network training. One of the necessary conditions for increasing the predictive ability of a machine learning model (MLM) is the optimal selection of hyperparameters. The choice of the optimal hyperparameters is often made on the basis of empirical experience.
 Purpose. To explore the specific application of machine algorithms to the prediction of heart disease.
 Materials and methods. Scientific novelty of the work. In this research we analyse machine learning algorithms for predicting the risk of CVDs using the approach of automatic search for hyperparameters MMO. The following algorithms were used to construct MMOs: NBS, KNN, DT, Logistic Regression, Support Vector Machine (SVM), Random Foorest (RF), Complement Naïve Bayes Classificator (CNBC), Linear Discriminant Analysis (LDA), Radial Basic Function (RBF), Gradient Boost (XGBoost). To evaluate the accuracy of machine learning models we used the following indicators: mean absolute error (MAE), precision, completeness (recall), F-measure (F-beta), False Positive Rate (FPR), False Negative Rate (FNR). Additionally, visual analysis of ROC curve (receiver operating characteristic) and areas under the curve (areas under the curve, AUC) were used to analyse the results of MMO. Using AUC value allows to estimate prognostic ability of MLM.
 Results. The training results showed that RF and XGBoost algorithms are characterized by higher accuracy. With optimal selection of MMO parameters, the overall classification accuracy was 0.88 and 0.94 respectively.
 Conclusion. The application of machine learning algorithms allows predictive models to be built with high accuracy. This requires the construction of a machine learning model. The ensemble machine learning algorithms RF and XGBoost have higher accuracy rates than the following algorithms: decision trees, Bayesian classification methods, logistic regression, linear discriminant analysis.

Risk prediction for petroleum exploration based on Bayesian network classifier

Effective prediction of petroleum exploration risks is critical for boosting oil and gas extraction efficiency and increasing economic benefits. To improve the accuracy and efficiency of risk prediction, this paper proposes a scalable k-dependence Bayesian network classifier (SKDB) method for solving the risk prediction problem of oil and gas exploration. The effectiveness and efficiency of the SKDB method were illustrated by taking the Jurassic Sangonghe Formation hydrocarbon exploration risk prediction in the Junggar Basin as an example. The model accuracy comparison results showed that SKDB has the highest accuracy compared with the state-of-the-art methods, and the accuracy was improved by 3.55∼7.63%. The oil and gas probability map prediction results indicated that the SKDB method not only showed high hydrocarbon probability in the reservoir areas, but also had better trend inference ability compared with other methods. Based on the SKDB prediction results, the remaining hydrocarbon favorable areas of the Sangonghe Formation reservoir were visualized and three types of favorable hydrocarbon distribution zones were preferentially selected, namely Type I (highly favorable), Type II (moderately favorable) and Type III (low favorable). The application results can provide a decision basis for the next oil and gas exploration and optimization strategy.

Hybrid approach for text categorization: A case study with Bangla news article

The incredible expansion of online texts due to the Internet has intensified and revived the interest of sorting, managing and categorising the documents into their respective domains. This shows the pressing need for automatic text categorization system to assign a document into its appropriate domain. In this article, the focus is on showcasing the effectiveness of a hybrid approach that works elegantly by combining text-based and graph-based features. The hybrid approach was applied on 14,373 Bangla articles with 57,22,569 tokens collected from various online news corpora covering nine categories. This article also presents the individual application of both the features to explicate how they generally work. For classification purposes, the feature sets were passed through the Bayesian classification methods which yield satisfactory results with 98.73% accuracy for Naïve Bayes Multinomial (NBM). Also, to test the robustness and language independency of the system, the experiments were performed on two popular English datasets as well.

Improved Facial Identification Using Adaptive Neuro-Fuzzy Logic Inference System

Objectives: To suggest an efficient pose invariant face recognition system (PCA-ANFIS) based on PCA and ANFIS. Methods: Using a dual-tree complex wavelet transform technique, face image improvement is obtained. ORL and YALE B data sets are used in this process. To train and test the network to recognize the person, two sets of photos were used. The system will return recognized if the test image matches one of the image’s training sets. The system will report not recognized if the test image does not match one of the image’s training sets. Accuracy, specificity, sensitivity, precision, and recall are the criteria taken into account. The suggested approach is then evaluated and compared to other known, gold standard facial recognition algorithms, including PCA, WP&LDA, GOBER WAVELET, SVM, and Bayesian Classifier methods. Findings: The study results indicated that the image accuracy, specificity, sensitivity, precision, and recall are enhanced toa level of 90, 85, 95, 86, and 95 per cent respectively when compared with the test results of other known gold standard facial recognition algorithms viz., PCA, WP &LDA, GOBET WAVELET, SVM and Bayesian Classifier approaches by which it can be concluded that it is an efficient approach for facial identification. Novelty : Both the feature extraction strategy and the classification approach play a strong emphasis on these factors. Principal component analysis is used for feature extraction, while an Adaptive Neuro Fuzzy Inference System is used for classification. The study proposed a novel neuro-fuzzy system-based face recognition method that is proposed in this study correctly that identifies the input face photos with a higher recognition rate Compared to the existing reports. Keywords: Facial Recognition; Image Processing; Biometric Technology; Facial Image Enhancement; Accuracy; Sensitivity

Markov chain Monte Carlo for seismic facies classification

Seismic facies classification aims to predict a facies model, or a set of facies models, from measured seismic data. We focus on stochastic classification methods to estimate the probability distribution of facies conditioned on seismic data. Bayesian classification methods based on analytical solutions are generally applied to seismically inverted elastic attributes or petrophysical properties rather than measured seismic data, and they do not include spatial correlation models in the prior distribution. Instead, iterative stochastic methods, such as Monte Carlo acceptance-rejection sampling and approximate Bayesian computation, can be directly applied to seismic data and can be implemented with complex prior models; however, their convergence is generally slow because it requires the simulation of a large number of facies models from the prior distribution. We develop an efficient implementation of a Markov chain Monte Carlo (MCMC) approach that adopts complex prior models, such as multiple-point statistics simulations based on a training image, to generate geologically realistic facies realizations. The novelty of the approach is that the proposal distribution of the proposed MCMC method is based on a gradual deformation algorithm, in which the proposal distribution is expressed as a linear combination of the indicator variables of the previously accepted model and a random prior simulation, according to a uniform random weight. The synthetic examples indicate accurate facies predictions with a success rate of approximately 80% for well-log facies classification based on elastic attributes and approximately 60% for seismic facies classification based on seismic data. The inversion is compared to an MCMC method with prior models sampled from a first-order Markov chain and Bayesian facies classification.

Recognition method of football players' shooting action based on Bayesian classification

Aiming at the problem of low accuracy and poor real-time performance of existing algorithms in the process of football players' shooting action recognition, a football players' shooting action recognition method based on Bayesian classification is proposed. Firstly, Gaussian mixture model is constructed to extract the characteristics of shooting action. Secondly, the Gaussian parameters are estimated to obtain the optimal state sequence, which provides a basic reference for football players' shooting action recognition. Finally, based on the marking of football players' shooting action behaviour, the recognition of football players' shooting action based on Bayesian classification is realised. Experiments show that the designed Bayesian classification method can accurately identify the shooting action of football players, and has good real-time performance. This shows that the design method can provide basic basis and theoretical guarantee for football players' action recognition, and has certain practical application performance.

Classification of News Texts by Categories Using Machine Learning Methods

In parallel with the advances in technology, digital journalism is preferred more than printed journalism day by day. Due to the fast and up-to-date sense of journalism provided by digital journalism and its ubiquitous accessibility features, it is read more by users. In addition to these advantages provided by digital journalism, it also has some difficulties compared to printed journalism. The stage of preparation and delivery of the news to the user requires more technological knowledge and equipment compared to printed journalism. The processes of title selection, text creation, photo selection and determination of the appropriate news category in the preparation phase of the news are designed to be both faster and user-friendly compared to printed publishing. The news created to be presented to the target audience may belong to one or more of different categories such as economy, politics, sports, technology, and health. The inclusion of the news in the appropriate category provides convenience in terms of reaching the right audience and archiving the news correctly. In this study, news texts were classified according to their categories based on the machine learning methods. In the study, news of five newspapers in three different categories were used. Bayesian classifier and decision tree methods were used to classify the news in the dataset including a total of 10.500 news. In the results of the study, it was observed that the Bayesian classifier classified the news more successfully according to their categories.

Classifying the lung images for people infected with COVID-19 based on the extracted feature interval

ABSTRACT This study proposes a new method to classify image data for two groups and effectively apply it in identifying people infected with COVID-19 based on their lung image. First, we extract each image into a two-dimensional interval based on the Gray Level Co-occurrence Matrix (GLCM). Next, find the prior probability based on the fuzzy relationship between the classified element and the established groups. Finally, combining the above improvements, we propose a new principle similar to the Bayesian method for classification. An image is classified into a group if it has the most significant value for prior probability and the smallest value for the overlap distance between the representative interval for the image and the groups. Applying the set of lung X-ray images to distinguish people infected with COVID-19, the proposed algorithm has given outstanding results compared with many well-known methods. The result also shows that this research can be applied in practice, and has the potential for different fields.

A comprehensive framework for cell-aware diagnosis of customer returns

The first step when a customer return occurs in industry is to reproduce the failure mechanism with any original test and test conditions (voltage, temperature, etc.) used during initial post-fabrication test. Depending on the test scenario (test sequence, test scheme, test conditions), different types of defects and failure mechanisms are targeted, thus leading to different diagnosis outcomes. Based on our previous work, this paper presents a comprehensible framework for cell-aware diagnosis of customer returns. The first part of the paper summarizes our previous work in which a generic cell-aware diagnosis flow using a Bayesian classification method was proposed to precisely identify defect candidates in combinational cells of a defective circuit according to a given test scenario. The second part of the paper extends the previous work by dealing with defects in sequential cells of defective circuits. New experiments done on a silicon test chip and a customer return with a given test scenario have proven the efficacy of our flow in terms of diagnosis accuracy and resolution.

Research on Detection Model of Abnormal Data in Engineering Cost List

Abstract Projects of engineering construction have the characteristics of large investment and long cycle, which makes the cost management difficult and the data are often abnormal. Therefore, it is necessary to strengthen the detection of abnormal data in engineering cost list. Based on this, the establishment of a detection model of engineering cost list is studied in this paper. By introducing K-means clustering method into the model, the list is clustered according to the comprehensive unit cost, and the list data are classified by Bayesian list classification method where the value of k is selected as 5. The detection of abnormal data method in engineering cost list is compared with that of the traditional detection method based on distance, which is known that the detection model has good effect, high accuracy and recall rate.

Research on Recommendation of College Mental Health Teaching Materials Based on Improved Deep Learning Algorithm

In order to meet the differentiated needs of students and improve the satisfaction of college mental health textbook recommendation, a college mental health textbook recommendation scheme based on improved deep learning algorithm is proposed. Based on the analysis of the principle of deep learning data recommendation system, the data interest is calculated according to the browsing records of students on college mental health textbooks. Combine the deep learning algorithm and collaborative filtering algorithm to collect the demand data of college mental health textbooks, then use the Naive Bayesian classification method to divide the college mental health textbooks into interested and uninterested parts, and recommend the interested college mental health textbooks to the students in need. Experiments show that the longest recommendation time of the college mental health textbook recommendation scheme based on the improved deep learning algorithm proposed in this paper is 4.5 min, the highest recommended recall rate is 95.18%, the average accuracy is 97.2%, the highest content richness is 0.8, the system stability coefficient is 1.06, and the overall average praise rate is 97.79%. It has a good recommendation effect.

EFFICIENCY OF THE COURSE PRACTICUM ASSISTANT SELECTION PROCESS USING BAYESIAN ALGORITHMS

The selection of the Practicum Assistant Course is the main problem in the process of selecting the role of the practicum assistant in big data. In the process of big data, the process of selecting assistant laboratorium subjects pay attention to the processes that occur before in the prospective assistant lab practicum (previous distribution). In this study we use the Bayesian classification method for the selection of practicum assistant courses. The Bayesian algorithm is applied in the process of selecting a practicum asiststen which is received with prior knowledge and posterior . In this consideration, the initial (prior) data is used as a consideration to determine whether a student in a certain semester can become a practicum assistant in the appropriate course of interest. The benefits obtained are efficiency in choosing a practicum assistant with consideration of previous events, P (θ | x). Based on the results obtained, the results of this study show the efficiency of the selection based on the previous few who became the background of the candidates for the practicum assistant course

Data-driven Bayesian model-based prediction of fatigue crack nucleation in Ni-based superalloys

This paper develops a Bayesian inference-based probabilistic crack nucleation model for the Ni-based superalloy René 88DT under fatigue loading. A data-driven, machine learning approach is developed, identifying underlying mechanisms driving crack nucleation. An experimental set of fatigue-loaded microstructures is characterized near crack nucleation sites using scanning electron microscopy and electron backscatter diffraction images for correlating the grain morphology and crystallography to the location of crack nucleation sites. A concurrent multiscale model, embedding experimental polycrystalline microstructural representative volume elements (RVEs) in a homogenized material, is developed for fatigue simulations. The RVE domain is modeled by a crystal plasticity finite element model. An anisotropic continuum plasticity model, obtained by homogenization of the crystal plasticity model, is used for the exterior domain. A Bayesian classification method is introduced to optimally select informative state variable predictors of crack nucleation. From this principal set of state variables, a simple scalar crack nucleation indicator is formulated.

Hierarchical Bayesian classification methods to identify topics by journal quartile with an application in biological sciences

This manuscript builds on a novel, automatic, freely-available Bayesian approach to extract information in abstracts and titles to classify research topics by quartile. This approach is demonstrated for all N= 149,129 ISI-indexed publications in biological sciences journals during 2017. A Bayesian multinomial inverse regression approach is used to extract rankings of topics without the need of a pre-defined dictionary. Bigrams are used for extraction of research topics across manuscripts, and rankings of research topics are constructed by quartile. Worldwide and local results (e.g., comparison between two peer/aspirational research institutions in Colombia) are provided, and differences are explored both at the global and local levels. Some topics persist across quartiles, while the relevance of others is quartile-specific. Challenges in sustainable development appear as more prevalent in top quartile journals across institutions, while the two Colombian institutions favour plant and microorganism research. This approach can reduce information inequities, by allowing young/incipient researchers in biological sciences, especially within lower income countries or universities with limited resources, to freely assess the state of the literature and the relative likelihood of publication in higher impact journals by research topic. This can also serve institutions of higher education to identify missing research topics and areas of competitive advantage.

A Naïve Bayes Students’ Performance Prediction Model for Decision Support System

Adequate assistance in the learning process is important using accurate estimation of students' academic performance based on new emerging techniques, and discover new knowledge, finding meaningful variables answers educational problems. Data contains hidden information for analyzing, extracting information and knowledge to find patterns, and using this for decision-making. WEKA (Waikato Environment for Knowledge Analysis) is used and Naïve Bayesian Classification Method is implemented, as the pre-processing mechanism for 10-fold cross-validation where classification models were generated, cross-validation method, and percentage split were used to evaluate the efficiency of this algorithm. Linear Regression is used to identify the significant predictors that affect the students' academic performance. The simulation results show that with Naïve Bayes Classification Method algorithm for classification with a correctly classified instance of 93.48% and incorrectly classified instance of 6.52%, this result indicates that with the classification method the accuracy level of prediction increases. To validate the generated model, the experiments were conducted using real data. The result is a good model intended to be used in the school decision support system in predicting learners' academic performance.

Determination of the Children Classification with Special Needs in Extraordinary High School in Banjarmasin Using the Naive Bayes

Children with special needs with different physical characteristics are very easy to distinguish. However, in children with special needs with different psychological characteristics, there are difficulties in determining the classification of the child. Children with mental disorders are children who have deviations in the ability to think critically, logically in responding to the world around them. This study determined the classification of children with special needs, the decision support system can analyze using the naive bayes classification (NBC) method. The result showed that Naïve Bayes Classifier algorithm can be used as the basis for the decision-making process by the Special High School Special Education Foundation (SMALB YPLB) Banjarmasin. In this study, it is hoped that using an application based on the Naive Bayesian Classification method can be used as an alternative calculation that is more effective than calculations using the manual method, with the achievement of conformity of the results reaching 80%.