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

2021 ◽  
Vol 2083 (4) ◽  
pp. 042079
Author(s):  
Kaiying Zuo
Keyword(s):  
Mobile Communications ◽  
High Accuracy ◽  
Bayesian Classification ◽  
Classification Algorithm ◽  
Classification Method ◽  
Spam Filtering ◽  
White List ◽  
Evolution Algorithm ◽  
Simulation Results ◽  
Content Based Filtering

Abstract 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.

Study of Network Public Opinion Classification Method Based on Naive Bayesian Algorithm in Hadoop Environment

2014 ◽  
Vol 519-520 ◽  
pp. 58-61 ◽  
Author(s):  
Jian Xu ◽  
Bin Ma
Keyword(s):  
Public Opinion ◽  
Distributed Storage ◽  
Bayesian Classification ◽  
Classification Algorithm ◽  
Classification Method ◽  
Single Server ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Bayes Algorithm ◽  
Network Public Opinion

In the light of the excellent distributed storage and parallel processing feature of hadoop cluster, a new kind of network public opinion classification method based on Naive Bayes algorithm in hadoop environment is studied. The collected public opinion documents are stored locally according to the HDFS architecture, and whose character words are extracted paralleled in Mapreduce process. Thus the naive Bayesian classification algorithm is parallel encapsulated on cloud computing platform. The MapReduce packaged Naive Bayesian classification algorithm performance is verified and the results show that the algorithm execution speed are significantly improved compared to a single server. Its public opinion classification accuracy rate is more than 85%, which can effectively improve the classification performance of network public opinion and classification efficiency.

Bayesian Spam Detection Framework on Mobile Device

2019 ◽  
Vol 12 ◽  
Author(s):  
Yitao Yang ◽  
Guozi Sun ◽  
Chengyan Qiu
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Bayesian Classification ◽  
Classification Algorithm ◽  
Spam Detection ◽  
Spam Filtering ◽  
Learning Mechanism ◽  
The Real ◽  
Filtering Method ◽  
Feedback Learning

In recent years, the spam message problem becomes more serious. Similar to spam mail, the spam message in phone brings a big trouble to users. Bayesian classification algorithm, which is simple to design and has the higher accuracy, becomes the most effective filtration methods. Bayesian classification algorithm, which is simple to design and has the higher accuracy, becomes the most effective filtering method. A Bayesian spam detection framework is designed in the paper and is deployed on Android device to test. Besides it can filtering coming messages and classify them into normal or spam in real time, it introduces feedback learning mechanism to make its result more accurate. The experiments are conducted under the real environment. The results show that the framework can meet the requirement of spam filtering.

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

2019 ◽  
Vol 14 (08) ◽  
pp. 38
Author(s):  
Xiuying Ou
Keyword(s):  
Bayesian Classification ◽  
Classification Algorithm ◽  
Classification Method ◽  
Self Awareness ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Accounting Courses ◽  
Generative Teaching ◽  
The Difference ◽  
Student Classification

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.

scholarly journals Obscure Image Classification and Restoration using Support Vector Machines

2019 ◽  
Vol 8 (4) ◽  
pp. 8231-8236
Keyword(s):  
Kernel Estimation ◽  
Classification Algorithm ◽  
Support Vector ◽  
Svm Classification ◽  
Vector Machines ◽  
Blurred Image ◽  
Simulation Results ◽  
Blurred Images ◽  
Identification And Characterization ◽  
Restoration Model

A restoration and classification computation for blurred image which depends on obscure identification and characterization is proposed in this paper. Initially, new obscure location calculation is proposed to recognize the Gaussian, Motion and Defocus based blurred locales in the image. The degradation-restoration model referred with pre-processing followed by binarization and features extraction/classification algorithm applied on obscure images. At this point, support vector machine (SVM) classification algorithm is proposed to cluster the blurred images. Once the obscure class of the locales is affirmed, the structure of the obscure kernels of the blurred images are affirmed. At that point, the obscure kernel estimation techniques are embraced to appraise the obscure kernels. At last, the blurred locales are re-established utilizing nonblind image deblurring calculation and supplant the blurred images with the restored images. The simulation results demonstrate that the proposed calculation performs well

scholarly journals Nonparametric estimation of the measure of functional dependence

2021 ◽  
Vol 6 (12) ◽  
pp. 13488-13502
Author(s):  
Qingsong Shan ◽  
◽  
Qianning Liu
Keyword(s):  
Asymptotic Distribution ◽  
Nonparametric Estimation ◽  
Functional Dependence ◽  
Kernel Estimator ◽  
Real Data ◽  
High Accuracy ◽  
Simulation Results

<abstract><p>In this paper, we propose a beta kernel estimator to measure functional dependence (MFD). The MFD not only can measure the strength of linear or monotonic relationships, but it is also suitable for more complicated functional dependence. We derive the asymptotic distribution of the proposed estimator and then use several simulated examples to compare our estimator with the traditional measures. Our simulation results demonstrate that beta kernel provides high accuracy in estimation. A real data example is also given to illustrate one possible application of the new estimator.</p></abstract>

A Method for Real-Time Radar Signal Main Sorting

2013 ◽  
Vol 760-762 ◽  
pp. 567-571
Author(s):  
Hong Chao Wu ◽  
Wei Hua Xiao ◽  
Jian Feng Pu
Keyword(s):  
Real Time ◽  
Pulse Sequence ◽  
High Accuracy ◽  
Radar Signal ◽  
Sorting Algorithm ◽  
The Real ◽  
Sorting Algorithms ◽  
Key Technologies ◽  
Simulation Results ◽  
Sequence Simulation

The real-time radar signal sorting is one of the key technologies for electronic reconnaissance, first analyzes the defects of traditional main sorting algorithms, and then proposes a comprehensive main sorting algorithm. The method first uses the SDIF algorithm to sort PRI fixed and PRI stagger radar signal, then uses dynamic expansion association method to search PRI jitter radar signal. When using the SDIF algorithm, in order to improve the efficiency of extraction of PRI, first taking amplitude pretreatment, then only accumulate the signals whose amplitude meet certain requirements. After extracting PRI, extract to the original full pulse sequence. Simulation results show that the method of sorting has high accuracy and good real-time.

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