Bayesian Classification Algorithm Research Articles

Personalized Recommendation System of E-learning Resources Based on Bayesian Classification Algorithm

Personalized Recommendation System of E-learning Resources Based on Bayesian Classification Algorithm

Research on quantitative evaluation of rebar stress based on weak magnetic effect

In order to explore an evaluation method for nondestructive testing of the rebar stress, the weak magnetic field test of the rebar stress was carried out in this paper. The evolution characteristics of the magnetic induction component By along the length direction of the rebar under tensile load were analyzed. The value of the root mean square gradient of the axial magnetic induction intensity KRMSe was introduced. The law of linear decline of characteristic parameters with the stress in the elastic stage was revealed. The relationship between the feature parameters and the lift-off value of the sensor was further analyzed, and the lifting effect of the feature parameters was modified. Based on the correction of the lifting effect, the influence of the initial magnetization on the weak magnetic detection of stress was removed through the simulation test. The naive Bayesian classification algorithm is introduced into the stress classification evaluation. The statistical analysis of the relationship between the modified characteristic parameter KRMSecN and stress is carried out. A stress classification evaluation model is established based on the maximum of posteriori probability. The model can classify the rebar stress in specific ranges with high accuracy. This study provides a new evaluation method for nondestructive testing of the rebar stress state.

Who publishes imaging articles in non-imaging journals? A large sample data-mining study.

To assess the weight of imaging and imaging specialists (i.e., affiliated to a radiology/nuclear medicine department) in publications in non-imaging journals. All articles indexed in English on the Embase database between 1989 and 2019 were extracted. The number and affiliation of authors were determined. A naive Bayesian classifier algorithm was trained to classify abstracts as "imaging" or "non-imaging." The main outcome was the number and position of imaging specialists in the authorship of imaging articles published in non-imaging journals. Analyses per medical specialties and per journal impact factor (IF) were performed. A total of 15,787,825 articles were included with 968,259 (6%) "imaging" articles. The proportion of imaging articles increased over time (+ 370%), quicker than the overall academic output. The proportion of imaging specialist among authors grew from 0.58% in 1989-1994 to 1.54% in 2015-2019. About 20% of imaging articles had ≥ 1 imaging specialist among authors. The proportion of imaging articles decreased with the IF (7.3% for IF 0-2.5 vs. 5.1% for IF > 10, p < 0.001), but the proportion of imaging specialist authors in imaging papers with ≥ 1 imaging specialist author increased with the IF (40% for IF 0-2.5, 53% for IF > 10, p < 0.001). There was significant variability across medical specialties. The weight of imaging articles and imaging specialist among authors in non-imaging journals has increased over time but remains limited. Most of the authors of imaging publications are not imaging specialists. Imaging specialists among authors in imaging papers are associated with a greater IF. • The proportion of imaging specialist authors in non-imaging journals, though small, has increased significantly. • Marked differences are observed according to medical specialties and the reputation/impact factor of the journal. • Collaboration between imaging specialists and non-specialists is associated with publication in higher impact journals.

Implementasi Algoritma Naïve Bayes Classifier Dalam Penerimaan Karyawan

The final research report of this study aims to find out how the employee recruitment procedure at PT. Kreasibeton Nusapada, because human resources are a component that has a very important role in a company. In today's technological age, some companies have used certain methods in making choices to recruit new employees. This is because the assessment used to obtain competent new employees should be an objective assessment. In this research, an assessment method will be developed to make it easier for the HRD (Human Resource Department) to select competent candidates to fill certain positions needed by the company. The assessment will be calculated by assessing all the criteria that must be met by prospective company employees, where the assessment is made with standards predetermined by management. The research produced a suitable candidate to fill the position of HSE (Health Safety Environment) staff at PT Kreasibeton Nusapersada, namely Rizki Vadlin with a final score of 1.75 which was calculated based on the Naïve Bayesian Classifier Algorithm. The results of this study indicate that the process of assessing the competence of prospective employees to fill available positions in the company is proven to produce an objective and effective assessment in accordance with the criteria set by the company's managemen.

Early prediction of acute respiratory distress syndrome complicated by acute pancreatitis based on four machine learning models

BackgroundAcute Respiratory Distress syndrome (ARDS) is a common complication of Acute Pancreatitis (AP) and is associated with high mortality. This study used Machine Learning (ML) to predict ARDS in patients with AP at admission. MethodsThe authors retrospectively analyzed the data from patients with AP from January 2017 to August 2022. Clinical and laboratory parameters with significant differences between patients with and without ARDS were screened by univariate analysis. Then, Support Vector Machine (SVM), Ensembles of Decision Trees (EDTs), Bayesian Classifier (BC), and nomogram models were constructed and optimized after feature screening based on these parameters. Five-fold cross-validation was used to train each model. A test set was used to evaluate the predictive performance of the four models. ResultsA total of 83 (18.04%) of 460 patients with AP developed ARDS. Thirty-one features with significant differences between the groups with and without ARDS in the training set were used for modeling. The Partial Pressure of Oxygen (PaO2), C-reactive protein, procalcitonin, lactic acid, Ca2+, the neutrophil:lymphocyte ratio, white blood cell count, and amylase were identified as the optimal subset of features. The BC algorithm had the best predictive performance with the highest AUC value (0.891) than SVM (0.870), EDTs (0.813), and the nomogram (0.874) in the test set. The EDT algorithm achieved the highest accuracy (0.891), precision (0.800), and F1 score (0.615), but the lowest FDR (0.200) and the second-highest NPV (0.902). ConclusionsA predictive model of ARDS complicated by AP was successfully developed based on ML. Predictive performance was evaluated by a test set, for which BC showed superior predictive performance and EDTs could be a more promising prediction tool for larger samples.

Secret information security system in computer network based on Bayesian classification and nonlinear algorithm

Abstract To solve the problem that most computer network security assessment systems cannot comprehensively analyze data, this article proposes a research method for computer network confidentiality information security system based on the Bayesian classification algorithm. This research takes the computer network security secret-related information security as the core, cleverly uses the EBCA back-propagation model, and comprehensively uses nonlinear functions to propose a computer network secret-related information security application system considering EBCA, which effectively solves the complex cross-linking relationship. The effectiveness of the method is verified by simulation experiments. Simulation experiments show that when the network performance is in the optimal state, the slope is 1, the intercept is 0, and the fitting degree is 1. The method proposed in this article is especially suitable for the nonlinear characteristics of computer network security and can accurately, comprehensively, and systematically reflect the security operation status of network security and confidential information. Then, through the simulation test, it is found that the method in this study can effectively meet the error requirements of equipment integrity and equipment normal rate.

On the Origin of the Dichotomy of Stellar Activity Cycles

The presence of possible correlations between stellar rotation rate Ω and the frequency of the activity cycle ω cyc is still much debated. We implement a new Bayesian classification algorithm based on a simultaneous regression analysis of multiple scaling laws and we demonstrate the existence of two different scalings in the – plane for an extended Mt. Wilson sample of 67 stars. Thanks to metallicity measurements obtained from both ESA Gaia and high-resolution spectroscopy, we argue that the origin of this dichotomy is likely related to the chemical composition: stars whose magnetic cycle frequency increases with rotation rate are less metallic than stars whose magnetic cycle frequency decreases with stellar rotation rates. On the contrary, no clear difference in chromospheric magnetic activity indicators characterizes the two branches.

Automatic classification of document resources based on Naive Bayesian classification algorithm

World Wide Web has become big as the amount of documents collection is increasing rapidly. The automatic classification of document resources based on Naive Bayesian classification algorithm is detailed in this paper. Firstly, this paper introduces the relevant theories of naive Bayes classification and the automatic document classification system. Then, a massive network academic document automatic classification system is designed and implemented. The system uses modular design, including academic document automatic capture module, academic document word document matrix processing module, ontology integration module and semantic driven classification module. Finally, based on the Naive Bayesian classification algorithm, the training set of 12 categories preset is utilized in the professional classification directory of the Ministry of education.. Experiments show that the naive Bayesian classification algorithm can effectively complete the automatic capture, processing and classification of massive academic documents, which can not only improve the classification accuracy, but also reduce the running time of automatic classification. It solves the problems of the integration of two heterogeneous ontology libraries and also the problem that the traditional word vector space cannot meet people's needs for semantic classification.

Prediction of Sports Performance and Analysis of Influencing Factors Based on Machine Learning and Big Data Statistics

Performance prediction is one of the important contents of sports future development strategy research. This paper conducts research on sports performance prediction and influencing factor analysis based on machine learning and big data statistics. The purpose of forecasting research is to formulate short-term, medium-term, and long-term sports development planning and policy services for decision-makers of sports development. Advantages and disadvantages make sports workers consciously revise and realize their own sports future plans. First, the analysis of the influencing factors of sports performance is proposed, including the setting and scoring of sports examination items, research on standards, training methods for physical examination items, and physical examination learning. Second, the application of big data statistics based on machine learning algorithms is studied. Based on machine optimization algorithms, it summarizes the naive Bayesian classification algorithm and big data statistical classification algorithm. The theoretical idea of rough set algorithm and finally through the comparative analysis experiment based on big data statistics of male and female sports test scores and the comparison experiment of the accuracy of three algorithms to predict the results, it is concluded that the endurance quality of boys is better than that of girls, which indicates the quality of sustainable development. There is a certain gender gap, there are significant differences in the skills of male and female students, and the machine-optimized algorithm can predict sports performance with the highest accuracy among the three algorithms.

Data Mining Performance Assessment of Regional Development Planning Agency Employees Using Bayesian Classifier Algorithm

Performance is the success or work achievement of a person or group in an organization in completing work. Performance is the goals and targets given, performance appraisal is carried out. The results of employee performance consist of two, namely good or bad. These results are used as indicators in making decisions for giving rewards or punishments. This study aims to measure the level of employee performance with targets very good, good, quite good, less good and bad. The data used in this study is employee data. The assessment criteria are the results of the best employee assessment at the Regional Development Planning Agency in June 2022. The method used in determining employee performance is the Bayesian Classifier Algorithm. This algorithm uses the concept of classification. The data that is processed is first classified and followed by the analysis process in producing employee performance. This study uses training data as many as 43 records then the assessment is used as testing data. The results of the analysis of employee performance appraisal using the Bayesian Classifier Algorithm that the algorithm succeeded in classifying employee performance in accordance with the objectives of this study very well.

Design of Computer-Aided Translation System Based on Naive Bayesian Algorithm.

With the progress of society and the rapid development of science and technology, computer translation technology has become an important auxiliary tool in the fields of software localization and technical translation. This realistic demand has prompted translators to pay more attention to computer translation and have made some useful explorations on this basis. This paper aims to study and discuss computer-aided translation systems based on the fusion of naive Bayesian algorithms. This paper theoretically analyzes some key technologies in computer-aided translation. Computer-aided translation refers to helping translators to translate texts with a series of tools and then proposes a Bayesian classification algorithm. Translation memory technology can solve many practical problems, especially in the machinery manufacturing industry, processing some sentences in documents, which can reduce repetitive labor, unify vocabulary, and make translation styles more coordinated. The experimental results of this paper show that applying the naive Bayes method to the computer-aided translation system can better classify the documents in the translation system, thereby improving the ability of computer-aided translation. When the proportion of professional terms in the article reaches 85%, computer-aided translation has an auxiliary role for the translator. When the proportion of professional terms in the article reaches about 95%, computer-assisted translation can efficiently speed up the work speed and quality of translators. Due to the prosperity of computer translation systems, the duplication of labor for translators has been significantly reduced, and this ensures the consistency of terminology and translation style, so that the fruits of labor are fully utilized.

Bayesian networks and intelligence technology applied to climate change: An application of fuzzy logic based simulation in avalanche simulation risk assessment using GIS in a Western Himalayan region

Avalanches are snowfalls that cascade down a mountainside, although rockfalls and debris flows can also occur. The study identifies the snow meteorological data for the avalanche risk forecast through various associated factors and models a tool for forecasting avalanches according to the reference database. The terrain factors such as aspect, slope, and altitude have been studied using the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) at 90 m resolution. Snow meteorological observed data 2540 records were obtained for Snow and Avalanche Study Establishment (SASE) Nov 2004 to April 2009 for five years data. In the present study, nine parameters were used for Avalanche forecast in the Western Himalaya. The Bayesian classification algorithm may improve the feedback system of prediction than the existing one. Fuzzy logic decides to improve the system to predict the level of danger by studying the influence of parameters. Hazard mitigation strategies applying the Bayesian classification algorithm and Fuzzy logic were combined with Integrated GIS-based methods to determine the risk zones better to identify snow avalanches in three-dimensional terrain.

Naive Bayes Algorithm Mining Mobile Phone Trojan Crime Clues

After the mobile phone virus infects the mobile phone, it can transmit the real-time information of the user to the designated place set by the virus through the built-in recorder and camera on the mobile phone, thereby causing information leakage. With the rapid development of the Internet, the penetration rate of mobile terminals is also increasing day by day. As an emerging mobile terminal, smart phones have now fully occupied the market. With this trend, the importance of mobile phone information security is also increasing day by day. How to prevent mobile phone virus has gradually become an important issue. Trojan horse crime cases have different manifestations and behavioral characteristics from traditional cases. They have the characteristics of low crime cost, high income, high concealment, novel criminal methods, and great difficulty in detection, which brings greater difficulties to the public security organs in their investigation and detection. And the current research on mobile phone virus behavior is still in the preliminary stage, and some existing detection models can only target random networks. Trojan horses, viruses, and malicious software for smartphones have sprung up like mushrooms after rain, seriously infringing on the data security of mobile communication terminals, such as mobile phones and causing incalculable losses to users. This paper proposes a naive Bayesian algorithm to mine the clues of the criminal cases of mobile phone Trojans. It helps detect and discover new viruses at the beginning of an attack, allowing them to be more effectively defended and contained. And based on the feature set data extracted from the network data packets, it conducts an in-depth analysis of the current business behaviors of mobile phone Trojans, such as propagation and implantation, remote control, leakage of user privacy information, and malicious ordering, and extracts its behavior characteristics. Thus, unknown mobile Trojan horses that are taking place can be detected. The experimental results of the naive Bayesian classification algorithm proposed in this paper show that the algorithm improves the accuracy of mobile phone Trojan virus mining by 28%, which plays a significant role.

On-the-Spot Decision-Making System of Basketball Game Based on Data Mining Algorithm

Modern competitive basketball has developed into a skilled international competitive sport. The situation in basketball is changing rapidly. At the same time, tactics are one of the main factors affecting game performance in athletic basketball. Whether the athletes successfully use the techniques and tactics in the game or not will often become the decisive factor for the outcome of the game. Therefore, it is essential to grasp the situation of basketball games, to study the technical and tactical rules of basketball competitive games, and to provide scientific support for the daily technical and tactical training of competitive basketball and technical and tactical decision-making during the game, which is an important way to improve the level of basketball competition in China. As a decision support technology, data mining plays an important role in decision support systems. This decision-making method is closely related to the amount of data, the way of organization, and the structure of the organization. Based on this, this study studies the improved Bayesian algorithm classification algorithm in data mining, and it also reorganizes and restructures the large, intricate, and unorganized data from the database of the system according to the decision-making demand. After a series of data processing processes are completed, the latest data organization mode is obtained, and new data correlation information is found from it, by finding the relevance of knowledge in the decision support system of the database-based relational database management system, and presenting the implicit structural information in the data to provide users with more accurate decision-making information. Finally, in the investigation and analysis of the on-site decision-making system of basketball games, it is found that 48% of sport coaches think it is very helpful, indicating that the system has merit.

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

In order to strengthen the supervision and management of environmental pollution and timely understand and record the basic information of potential environmental pollution of enterprises and institutions, this paper proposes a general survey and inventory method of pollution sources based on big data technology under machine learning. Firstly, this paper evaluates and screens the data provided by government departments, constructs a machine learning classification model, and uses a variety of classification algorithms to compare and analyze according to the basic idea of machine learning to deal with practical problems. Then, the calibration data set constructed is used as the training set to predict and classify the national industrial and commercial data, provincial industrial and commercial data, and municipal industrial and commercial data provided by government departments. The experimental results show that the naive Bayesian classification algorithm is the best algorithm, and the F1 values of each data set are increased by 32.92%, 21.42%, and 14.91%, respectively. The classified prediction of the screened Internet data shows that the accuracy of the final Internet supplementary data is 17.26%, which is similar to the industrial and commercial data of the city. The availability of the machine learning model established in this paper is proven.

Population genomics reveal multiple introductions and admixture of Sonchus oleraceus in Australia

AbstractAimThe goal of this study was to investigate the invasion history of the weed Sonchus oleraceus in Australia by comparing the population genetic structure of individuals at different locations in Australia, and in the most likely areas of origin in the native range.LocationSamples were collected in Europe and Morocco, North Africa (27 locations), and Australia (17 locations).MethodsWe performed population genetic analyses using a large dataset comprising 2883 single nucleotide polymorphism markers from 547 plant samples and investigated the invasion history of S. oleraceus with Approximate Bayesian Computation and Random Forest classification algorithms. We compared single and multiple invasion scenarios considering admixture having occurred before and after introduction.ResultsOur results revealed high levels of inbreeding within sampling locations in the two ranges. Analyses also showed that S. oleraceus was possibly introduced to Australia at least twice: a first introduction around 1000 years ago before British settlement and a more recent introduction (~65 years ago) from Europe and North Africa. We also found evidence of post‐introduction admixture and a potential reintroduction of S. oleraceus from Australia back to its native range.Main conclusionsWe conclude that the invasion history of S. oleraceus into Australia is probably historic (i.e. prior to British settlements) and complex showing recent evidence of post‐introduction admixture. The complex invasion history of S. oleraceus in Australia poses challenges for the search of potential biological control agents.

Cultural Tourism Development in Himachal Pradesh Emphasizing Local Festivals, Fairs, and their Promotion

-In the last decade, ownership and use of mobile phone has increased dramatically in India promoting the use of mobile wallet service, especially among the adults. Mobile wallet adoption and usage is poised for major growth in the next few years, and thereby displace traditional payments such as cash and cards. The COVID-19 pandemic has accelerated the trend to use mobile payment. During this study, the essential variables of technology accepted model (TAM), viz. perceived security, social influence, and perceived innovativeness, have been identified through the survey. These variables area unit expected to own associate influence on the mobile services adoption intention. The goal of this paper is to predict the adoption of mobile wallet using 5 different classifiers namely Logistic Regression (LR), Multilayer Perceptron (MLP), Random Forest (RF), Naïve Bayesian and Logistic Model Tree (LMT) classification algorithm. We assessed the classifiers of the samples collected from 100 respondents from Puducherry India.. For experimentation, WEKA is used as a simulation tool; the results reveal that the RF achieves better performance when compared to other classifiers. LR attains the classification accuracy of 78.11%, Naive Bayes 63.88%, LMT 81.5% and MLP 82.66% for the dataset respectively. Key Words:Mobile wallet service, traditional payments, TAM, social influence, perceived security, innovativeness.

The Practice and Reform of Classroom Teaching Based on the Coordination of Online Environment and Deep Learning

By analyzing and reflecting on the characteristics of the network in teaching applications and some problems that arise in the traditional classroom teaching mode and classroom teaching in the network environment, some ideas and measures are proposed on how to strengthen and improve the organization and management of teaching in the network environment, and for the effective integration of teaching elements and resources. This paper proposes a teaching evaluation model based on a weighted plain Bayesian algorithm. According to the degree of influence of different attributes on the evaluation results, a method is proposed to determine the weights of each evaluation attribute using the correlation probabilities of class attributes and set the corresponding weights for each evaluation index. The experiment demonstrates that the accuracy of using the weighted plain Bayesian algorithm to construct the model for classification can reach 75%, and the average accuracy can be improved by about 3% compared with the traditional Bayesian classification algorithm.

A multivariate Bayesian classification algorithm for cerebral stage prediction by diffusion tensor imaging in amyotrophic lateral sclerosis

Background and ObjectiveDiffusion tensor imaging (DTI) can be used to tract-wise map correlates of the sequential disease progression and, therefore, to assess disease stages of amyotrophic lateral sclerosis (ALS) in vivo. According to a threshold-based sequential scheme, a classification of ALS patients into disease stages is possible, however, several patients cannot be staged for methodological reasons. This study aims to implement a multivariate Bayesian classification algorithm for disease stage prediction at an individual ALS patient level based on DTI metrics of involved tract systems to improve disease stage mapping. MethodsThe analysis of fiber tracts involved in each stage of ALS was performed in 325 ALS patients and 130 age- and gender-matched healthy controls. Based on Bayes’ theorem and in accordance with the sequential disease progression, a multistage classifier was implemented. Patients were categorized into in vivo DTI stages using the threshold-based method and the Bayesian algorithm. By the margin of confidence, the reliability of the Bayesian categorizations was accessible. ResultsBased on the Bayesian multistage classifier, 88% of all ALS patients could be assigned into an ALS stage compared to 77% using the threshold-based staging scheme. Additionally, the confidence of all classifications could be estimated. ConclusionsBy the application of the multi-stage Bayesian classifier, an individualized in vivo cerebral staging of ALS patients was possible based on the sequentially involved tract systems and, furthermore, the reliability of the respective classifications could be determined. The Bayesian classification algorithm is an improvement of the threshold-based staging method and could provide a framework for extending the DTI-based in vivo cerebral staging in ALS.

Design and Implementation of Data Analysis System of Social Network

To solve the problem that data collection efficiency of social network analysis technology based on single-mode network structure is low and cannot meet the problem of data information processing of large-scale social network, the social network data analysis system based on the network node centrality theory is proposed. The naive Bayesian classification algorithm is used to improve the traditional data mining mode. The traditional social network data analysis technology is compared with the social network data analysis system proposed in this research from four aspects: multi-platform network data processing response time, data acquisition and processing accuracy, social network data feature condition introduction rate, and data feature condition introduction accuracy. The accuracy of data collection and processing in the traditional social network data analysis system is 89.26%, the response time of multi-platform network data processing is 7.32s, the introduction rate of data feature conditions is 82.65%, and the accuracy rate of data feature conditions is 78.88%. The data processing accuracy of the data analysis system proposed in this research based on the network node centrality theory is 98.99%, the response time of multi-platform network data processing is 1.35s, the data feature condition introduction rate is 97.91%, and the data feature condition introduction accuracy rate is 97.39%. The social network data analysis system proposed in this research is significantly better than the traditional social network data analysis system in all aspects, and its data processing performance is very good especially in multi-platform social network. The results show that the social network data analysis system based on network centrality theory proposed in this research is feasible and can meet the daily application and research requirements of social network data collection, storage, analysis and visual display, which has the advantages of fast data processing speed, high accuracy and wide range of data storage.