Naive Bayesian Classification Algorithm Research Articles

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.

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.

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.

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.

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.

Study of Chinese spam filtering Based on Improved Naive Bayesian Classification Algorithm

Spam is a growing threat to mobile communications. This paper puts forward some mitigation technologies, including white list and blacklist, challenge response and content-based filtering. However, none are perfect and it makes sense to use an algorithm with higher accuracy for classification. Bayesian classification method shows high accuracy in spam processing, so it has attracted extensive attention. In this paper, a Bayesian classification method based on annealing evolution algorithm is introduced into Chinese spam filtering to improve the accuracy of classification. Our simulation results show that the algorithm has better performance in spam filtering.

Improved naive Bayes classification algorithm for traffic risk management

Naive Bayesian classification algorithm is widely used in big data analysis and other fields because of its simple and fast algorithm structure. Aiming at the shortcomings of the naive Bayes classification algorithm, this paper uses feature weighting and Laplace calibration to improve it, and obtains the improved naive Bayes classification algorithm. Through numerical simulation, it is found that when the sample size is large, the accuracy of the improved naive Bayes classification algorithm is more than 99%, and it is very stable; when the sample attribute is less than 400 and the number of categories is less than 24, the accuracy of the improved naive Bayes classification algorithm is more than 95%. Through empirical research, it is found that the improved naive Bayes classification algorithm can greatly improve the correct rate of discrimination analysis from 49.5 to 92%. Through robustness analysis, the improved naive Bayes classification algorithm has higher accuracy.

Realization Methods of Computer-aided Diagnosis System of Medical Images

In this paper, medical images are used to realize the Computer-Aided Diagnosis (CAD) system, which develops targeted solutions to existing problems. Relying on the MiCOM platform, this system has collected and collated cases of all kinds, based on which a unified data model is constructed according to the gold standard derived by deducting each instance. Afterwards, the object segmentation algorithm is employed to segment the diseased tissues. Edge modification and feature extraction are performed for the tissue block segmented. The features extracted are classified by applying support vector machines or the Naive Bayesian classification algorithm. From the simulation results, the CAD system developed in this paper allows the realization of diagnosis and treatment and sharing of data resources.

Research on spam filtering algorithm based on mutual information and weighted naive Bayesian classification

In this paper, N-gram algorithm is used to construct the characteristic database of e-mail viruses. Based on the characteristic database, the transmission and immune automatic behaviour of e-mail viruses are constructed based on the disease transmission model. Based on a large number of samples of five different virus types, a mail virus feature library is generated, and the automatic transmission and immune process of mail virus are analysed according to the mail virus feature library. Then, aiming at the low precision and recall rate of the traditional spam filtering algorithm, an improved mutual information feature and weighted naive Bayesian classification algorithm is proposed to complete the spam filtering. Experiments on trec06c open source data set show that the feature library generated by this method has a good performance of e-mail virus detection, and the analysis of e-mail virus behaviour can better meet the actual work of e-mail virus prevention.

Application of Classification Algorithm Based on Naive Bayes in Data Analysis of Fitness Test

An important aspect of physique data analysis is to provide a basis for developing national sports and improving physical education. This article aims to explore the application of Naive Bayesian classification algorithm in physical test data analysis, in order to conduct differentiated teaching and training from person to person, so as to effectively improve the physical health of students. This article uses the real physical test data of our school in 2018 and 2019 to form a complete data set after coding to construct a student physical classifier. In this way, the correct judgment of the student’s physical condition can be realized with a certain probability, and the problems of the student’s physical health can be detected and warned in time. Using the slicing method to cut out 20% as the test data, the classifier is tested. From the test results, it can be seen that the comprehensive correct rate of the physique classifier based on the naive Bayes algorithm reaches 81.02%. The research in this article provides help to better promote the physical fitness testing of colleges and universities, and provides a new way of thinking and practical methods for improving the quality of physical education teaching.

Potential Trend for Online Shopping Data Based on the Linear Regression and Sentiment Analysis

How to reduce the cost of competition in the industry, identify effective customers, and understand the emotional needs and consumer preferences of customers, so as to carry out fast and accurate commercial marketing, is an important research topic. In this paper, we discussed the method for the analysis of three product data which represent the customer-supplied ratings and reviews for microwave ovens, baby pacifiers, and hair dryers sold in the Amazon marketplace over the time period. The sentiment analysis, linear regression analysis, and descriptive statistics were implemented to analyze the three datasets. Based on the sentiment analysis given by the naive Bayesian classification algorithm, we found that the star rating is positively correlated with the reviews, while the helpfulness ratings have no specific relationship with the star rating and reviews. We use multiple regression analysis and clustering algorithm analysis to get the relationship between the 4 indexes such as time, star rating, reviews, and helpfulness rating. We find that there is a positive correlation between the 4 indexes, and the reputation of the product online market is improving as time grows. Based on the analysis of the positive reviews and star ratings, we suggested indicating a potentially successful or failing product by the positive reviews. We also discussed the relations between the star ratings and number of reviews. Finally, we selected the words from the Amazon sentiment dictionary as candidate words. By counting the candidate words’ appearance in the review, the keywords that can reflect the star rating were found.

Анализ применения методов машинного обучения компьютерных систем для повышения защищенности от мошеннических текстов

IT Security is an essential condition for functioning of each company whose work is related to the information storage. Various models for detecting fraudulent texts including a support vector machine, neural networks, logistic regression, and a naive Bayes classifier, have been analyzed. It is proposed to increase the efficiency of detection of fraudulent messages by combining classifiers in ensembles. The metaclassifier allows to consider the accuracy values of all analyzers, involving in the work the construction of the weight matrix and the characteristic that determines the minimum accuracy boundary. Based on the developed method, a software module for the classification of fraudulent text messages written in Java using M1 class of the OPENCV open library was created and tested. The general algorithm of the ensemble method is given. An experiment based on logistic regression, a naive Bayesian classifier, a multilayer perceptron, and an ensemble of these classifiers has revealed the maximum efficiency of the naive Bayesian classification algorithm and the prospect of combining classifiers into ensembles. The combined methods (ensembles) improve the results and increase the efficiency of the analysis, in contrast 
 to the work of individual analyzers.

Optimization of architectural art teaching model based on Naive Bayesian classification algorithm and fuzzy model

At present, the teaching of architectural art in China is still relatively traditional, and there are still some problems in the actual teaching. Based on this, this study combines the Naive Bayesian classification algorithm with the fuzzy model to construct a new architectural art teaching model. In teaching, the Naive Bayesian classification algorithm generates only a small number of features for each item in the training set, and it only uses the probability calculated in the mathematical operation to train and classify the item. Moreover, by combining the fuzzy model, the materials needed for architectural art teaching can be quickly generated, and the teaching principles and implementation strategies of architectural art are summarized. In addition, this paper proposes an attribute weighted classification algorithm combining differential evolution algorithm with Naive Bayes. The algorithm assigns weights to each attribute based on the Naive Bayesian classification algorithm and uses differential evolution algorithm to optimize the weights. The research shows that the method proposed in this paper has certain effect on the optimization of architectural art teaching mode.

Enhanced Performance of the Automatic Learning Style Detection Model using a Combination of Modified K-Means Algorithm and Naive Bayesian

Learning Management System (LMS) is well de-signed and operated by an exceptional teaching team, but LMS does not consider the needs and characteristics of each student’s learning style. The LMS has not yet provided a feature to detect student diversity, but LMS has a track record of student learning activities known as log files. This study proposes a detection model of student’s learning styles by utilizing information on log file data consisting of four processes. The first process is pre-processing to get 29 features that are used as the input in the clustering process. The second process is clustering using a modified K-Means algorithm to get a label from each test data set before the classification process is carried out. The third process is detecting learning styles from each data set using the Naive Bayesian classification algorithm, and finally, the analysis of the performance of the proposed model. The test results using the validity value of the Davies-Bouldin Index (DBI) matrix indicate that the modified K-Means algorithm achieved 2.54 DBI, higher than that of original K-Means with 2.39 DBI. Besides having high validity, it also makes the algorithm more stable than the original K-Means algorithm because the labels of each dataset do not change. The improved performance of the clustering algorithm also increases the values of precision, recall, and accuracy of the automatic learning style detection model proposed in this study. The average precision value rises from 65.42% to 71.09%, the value of recall increases from 72.09% to 80.23%, and the value of accuracy increases from 67.06% to 71.60%.

A Bayesian Classification Intrusion Detection Method Based on the Fusion of PCA and LDA

The rapid development of network technology is facing severe security threats while bringing convenience to people. How to build a secure network environment has become an important guarantee for social development. Intrusion detection plays an important role in the field of network security. With the complexity and diversification of networks, intrusion detection systems also need to be constantly improved and developed to match external environmental changes. The innovative work of this paper is as follows: principal component analysis and linear discriminant analysis are used to reduce the dimensionality of the data set, which avoids unnecessary detection content and improves detection efficiency and accuracy. The principal component analysis method, linear discriminant analysis algorithm, and Bayesian classification are combined to construct the PCA-LDA-BC classification algorithm, and the intrusion detection model is established based on this algorithm. The simulation experiment was carried out on the algorithm CICIDS2017 data set proposed in this paper. From the experimental results, it can be analysed that in the intrusion detection of missing data, the improved algorithm is compared with the traditional naive Bayesian classification algorithm, the detection rate is improved, and the false detection rate and the missed alarm rate are reduced. In terms of intrusion detection for various types of attacks, the detection rate, false detection rate, and missed alarm rate have been improved accordingly. It is proved that the algorithm has certain validity and feasibility.

Analisis Sentimen Pemilihan Presiden Indonesia Tahun 2019 Di Twitter Berdasarkan Geolocation Menggunakan Metode Naïve Bayesian Classification

Pemilihan Presiden Indonesia 2019 ramai diperbincangkan di dunia nyata maupun dunia maya, khususnya di media sosial Twitter. Semua orang bebas berpendapat tentang pasangan calon Presiden Indonesia 2019 tersebut. Sehingga memunculkan banyak opini, tidak hanya opini yang positif atau netral, ada pula opini negatif. Media sosial khususnya Twitter sekarang ini menjadi salah satu tempat promosi atau kampanye yang efektif dan efisien untuk menggait para pendukung. Dalam hal ini peneliti akan melakukan riset terhadap tokoh publik yang mencalonkan diri menjadi Presiden Indonesia. Metode penelitian yang digunakan dalam riset kali ini adalah algoritma klasifikasi Naïve Bayesian Classifer. Data yang digunakan adalah tweet berbahasa Indonesia dengan kata kunci Jokowi (#Jokowi2Periode) dan Prabowo (#PrabowoSandi) sebanyak 1009 data tweet selama 5 bulan dimulai dari 1 September 2019 sampai 31 Januar1 2019. Yang di mana data tweet tersebut diambil dari empat daerah terbesar di Indonesia, yaitu Jakarta, Bandung, Medan, dan Surabaya. Setiap data akan diambil secara manual menggunakan Geolocation API yang telah di sediakan oleh Twitter melalui Twitter search. Hasil dari klasifikasi menggunakan algoritma Naïve Bayesian Classifier didapat 839 tweet positif, 32 tweet negatif, dan 67 tweet netral dari 938 tweet keseluruhan, atau dalam bentuk persentase ada 90% merupakan sentimen positif, 3% sentimen negatif, dan 7% sentimen netral terhadap bapak Joko Widodo. Dan 56 tweet positif, 6 tweet negatif, dan 8 tweet netral dari 70 tweet keseluruhan, atau dalam bentuk persentase ada 80% merupakan sentimen positif, 9% sentimen negatif, dan 11% sentimen netral terhadap bapak Prabowo. Tingkat akurasi yang dihasilkan dari algoritma Naïve Bayesian Classifier sendiri terhadap penelitian ini sebesar 77,62%.

Differential Classification Method in Different Teaching Models of Accounting Courses Based on Naive Bayesian Classification Algorithm

Accounting is an important management discipline with strong theoretical foundation and practical operation. Due to the differences between individuals in the process of learning, the mastery of the subject is different. This requires teachers to implement differential teaching from the differences in student personality in the process of teaching. However, when teachers use the concept of difference teaching to teach, the classification of students' differences is mostly calculated by manual quantification such as records, tests, surveys, etc. This kind of measurement and qualitative method not only wastes manpower, but also has personal subjectivity, blindly relies on individual subjective judgment to judge students' advantages and interests, and has accuracy and scientificity. This requires research on students' differential classification methods. Therefore, this paper proposes a student classification method based on naive Bayesian algorithm. It constructs a classifier based on historical data, and then uses a well-structured and stable classifier to classify the actual pre-classification objects, and actually applies it to the teaching of accounting courses, realizing the difference in the teaching process. Provide data support for future differential teaching research. The results show that the naive Bayesian classification algorithm can be used to analyze the difference in personality and learning of students. Presupposition and generative teaching objectivesand students improve their self-awareness to better promote self-development.

Analisis Sentimen Pemilihan Gubernur Jawa Barat Tahun 2018 Dengan Aplikasi Twitter Menggunakan Metode Naïve Bayesian Classification

Pemilihan Gubernur Jawa Barat 2018 ramai diperbincangkan di dunia nyata maupun dunia maya, khususnya di media sosial Twitter. Semua orang bebas berpendapat atau beropini tentang calon Gubernur Jawa Barat 2018 sehingga memunculkan banyak opini, tidak hanya opini yang positif atau netral, adapula opini negatif. Media sosial khususnya Twitter sekarang ini menjadi salah satu tempat promosi atau kampanye yang efektif dan efisien untuk menggait para pendukung. Dalam hal ini peneliti akan melakukan riset terhadap salah satu tokoh publik yang mencalonkan diri gubernur Jawa Barat. Metode penelitian yang digunakan dalam riset kali ini adalah algoritma klasifikasi Naïve Bayesian Classifer. Data yang digunakan adalah tweet berbahasa Indonesia dengan kata kunci Ridwan Kamil (#RidwanKamil) sebanyak 1031 data tweet selamat setiap hari dimulai dari 15 Januari 2018 sampai 15 April 2018. Hasil dari klasifikasi menggunakan algoritma Naïve Bayesian Classifier didapat 690 jumlah tweet atau 67% dari jumlah keseluruhan data tweet yang mendukung bapak Ridwan Kamil atau bersifat positif khususnya terhadap program kerja yang akan dilakukan dan ini memberikan statistik probabilitas sebesar 73,13% tingkat akurasi Correctly Classified Instances.

Feature Selection for Multi-label Document Based on Wrapper Approach through Class Association Rules

Each document in a multi-label classification is connected to a subset of labels. These documents usually include a big number of features, which can hamper the performance of learning algorithms. Therefore, feature selection is helpful in isolating the redundant and irrelevant elements that can hold the performance back. The current study proposes a Naive Bayesian (NB) multi-label classification algorithm by incorporating a wrapper approach for the strategy of feature selection aiming at determining the best minimum confidence threshold. This paper also suggests transforming the multi-label documents prior to utilizing the standard algorithm of feature selection. In such a process, the document was copied into labels that belonged to by adopting all the assigned characteristics for each label. Then, this study conducted an evaluation of seven minimum confidence thresholds. Additionally, Class Association Rules (CARs) represents the wrapper approach for this evaluation. The experiments carried out with benchmark datasets revealed that the Naive Bayes Multi-label (NBML) classifier with business dataset scored an average precision of 87.9% upon using a 0.1 % of minimum confidence threshold.