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14th International Conference on Frontier Computing, FC 2024

作者： Wang, Luying Jiang, Yujie Academy of Film and Television Arts Hebei Institute of Communications Hebei Shijiazhuang051430 China

ISBN: (纸本)9789819627974

With the rapid development of new media and the rise of self media, the copyright of content belongs to users, and ordinary users can also create corresponding content. However, the creation of popular science works is a very scientific and rigorous work that cannot be completed by ordinary users. The output of high-quality popular science content still relies on professional editors from popular science media organizations or relevant research institutions, as well as the scientific and technological departments of authors of popular science publications. Therefore, how to create timely and professional popular science content has become a new topic. This article explored the creation and recommendation of new media content based on artificial intelligence. The naive Bayes algorithm was used to classify new media content, and mixed similarity was used to calculate the similarity between users and content, achieving the creation and recommendation of new media content. The experimental results showed that the recommendation accuracy of the hybrid recommendation algorithm was higher than that of the collaborative filtering algorithm, and the difference in accuracy between the two was at least 2%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative Filtering algorithm Creation and Recommendation naive bayesian algorithm New Media Content

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Application of naive bayesian algorithms in E-mail Classification

Application of Naive Bayesian Algorithms in E-mail Classific...

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Chinese Automation Congress (CAC)

作者： Chen, Rui Zhang, Cai-xia Guo, Jing Wang, Xiang-dong Foshan Univ Coll Automat Guangdong Prov Smart City Infrastruct Hlth Monito Foshan Peoples R China Foshan Univ Coll Math & Big Data Foshan Peoples R China

ISBN: (纸本)9781728140940

In the context of the rapid development of today's society, high-tech information technology such as machine learning has played a very important role in promoting social, economic and technological development in all countries of the world. bayesian classification algorithm is an important algorithm in the field of machine learning and data mining. naive bayesian algorithm is a basic and simple classification algorithm in bayesian classification algorithm. The naive bayesian algorithm has the advantages of high stability, simplicity, high efficiency and strong theoretical foundation. The classification quality of the naive bayesian algorithm much depends on the choice of construction methods and the nature and quantity of the classification data. In fact, many problems in our life can be attributed to classification problems, such as risk prediction, medical diagnosis, e-mail classification, fraud detection, etc. [1]. Here, I will mainly explain the principle of naive bayesian algorithm, classification process, the evaluation of classification results, and apply this algorithm to e-mail classification tasks. At the same time, I list some related examples. Finally, I will summarize the naive bayesian algorithm.

关键词： Machine learning data mining naive bayesian algorithm text classification email classification

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Research on E-learning interactive English vocabulary recommendation education system based on naive Bayes algorithm

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ENTERTAINMENT COMPUTING 2024年 51卷

作者： Xi, Wang Changzhou Vocat Inst Mechatron Technol Changzhou 213164 Jiangsu Peoples R China

E-learning is an interactive online learning mode that can enhance students' interest in learning in an entertainment environment. This article aims to develop an English vocabulary learning recommendation system based on decision tree algorithm and naive bayesian algorithm to provide personalized learning suggestions and help learners learn and remember words more effectively. Firstly, a large amount of English vocabulary data was collected, preprocessed, and feature extracted. Decision tree algorithms were selected as the foundation, and through analysis of existing data, a decision tree was generated to classify new data. Using learners' characteristics (such as age, learning objectives, etc.) and learning history (such as learned words, learning time, etc.) as inputs, a personalized recommendation model was constructed, This model can recommend suitable learning content for learners based on their personalized needs and learning situation. In order to better understand learners' learning progress, a naive bayesian model was trained using learners' learning history and progress information to analyze their current learning progress and predict their future learning situation. Through testing and evaluation of actual learners, the recommendation system performs well in providing personalized learning suggestions, and has a significant improvement effect on learners' vocabulary learning.

关键词： Decision tree algorithm naive bayesian algorithm E-learning Recommendation system

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Design of text sentiment analysis tool using feature extraction based on fusing machine learning algorithms

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第4期40卷 6375-6383页

作者： Ajitha, P. Sivasangari, A. Rajkumar, R. Immanuel Poonguzhali, S. Sathyabama Inst Sci & Technol Sch Comp Chennai Tamil Nadu India Sathyabama Inst Sci & Technol Chennai Tamil Nadu India

Text Sentiment Analysis is a system where text feeling polarity is positive or negative or neutral from a series of texts or documents or public opinions on a particular product or general subject. Using machine learning and natural language processing techniques, the current work aims to gain insight into sentiment mining on tweets. Text classification is accomplished using Machine Learning algorithm-based fusion technique. This research suggested a system for grading feelings based on a lexicon. Bag-of-words (BOW) or lexicon-based methodology is currently the main standard way of modeling text for machine learning in sentiment analysis approaches. Marketers can use sentiment analysis to analyze their business and services, public opinion, or to evaluate customer satisfaction. Organizations can even use this analysis to gather significant feedback on issues related to newly released products. The main objective of this is to resolve the data overload problem.

关键词： Sentiment analysis natural language processing lexicon method naive bayesian algorithm

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Gaussian naive bayesian Data Classification Model Based on Clustering algorithm

Gaussian Naive Bayesian Data Classification Model Based on C...

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2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)

作者： Zeng-jun BI Yao-quan HAN Cai-quan HUANG Min WANG Air Force Early Warning Academy

ISBN: (纸本)9781510891357

A gaussian naive bayesian data classification model based on clustering algorithm was proposed for fast recognition and classification of unknown continuous data containing a large number of non-priori ***, the unknown data were extracted from the representative samples according to the information entropy measure for clustering to generate class ***, the mapping relationship between data and class labels was established by using the gaussian naive bayes algorithm, and the classification model was obtained through *** results show that this unsupervised analysis process has a good classification effect on new data.

关键词： Clustering algorithm naive bayesian algorithm Classification model

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Design and Implementation of an Intelligent Classification and Reliability Prediction Tool based on Cloud Platform

Design and Implementation of an Intelligent Classification a...

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作者： Jieyu Chen Qiuxiang Wang Yadong Ling Shizhang Liang Meixian Li Key Laboratory of Industrial Equipment Quality Big Data MIIT The 5th Electronics Research Institute MIIT

Reliability prediction is of great importance for electronic products design,and an indispensable technique to ensure that electronic components,equipment and the overall system meet their required reliability targets as *** methods,handbooks,standards and software tools of reliability prediction were studied and *** industrial and commercial electronic equipment applications,the development and demand of reliability prediction tools were analyzed in this *** new-generation information technologies such as cloud computing and machine learning,a lightweight component reliability prediction tool was designed based on the Spring Cloud microservice *** addition,the naive bayesian algorithm was adopted to achieve intelligent components classification,which enhances prediction efficiency *** example was offered,which verified the effectiveness of the cloud-based reliability prediction *** characteristics of low-cost,rapidly-deployed and easy-to-use were shown,which differ it from traditional prediction tools,and make it suitable for commercial companies and electronic products.

关键词： reliability prediction microservice architecture naive bayesian algorithm commercial electronics

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The product quality risk assessment of e-commerce by machine learning algorithm on spark in big data environment

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第4期37卷 4705-4715页

作者： Liu, Yi Lu, Jiahuan Mao, Feng Tong, Kaidi Hangzhou Dianzi Univ Management Sch Hangzhou Zhejiang Peoples R China Zhejing Inst Informatizat Dev Hangzhou Zhejiang Peoples R China

In order to pre-warning the product quality risk of the e-commerce platform, this paper studies the machine learning algorithm for the products quality risk assessment, which propose the Fuzzy C-Means clustering algorithm for the feature extraction and the Cost Sensitive Leaning (CSL)-naive bayesian algorithm to construct the assessment model for E-commerce product quality risk form the massive and unbalanced data. The experimental results show that the Machine Learning algorithm based on Spark has better scalability and superiority in the large-scale data environment, which can accurately identify e-commerce product quality risk.

关键词： E-commerce product quality risk assessment fuzzy c-means clustering algorithm cost sensitive leaning naive bayesian algorithm

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Automated Classification of Web Sites using naive bayesian algorithm

Automated Classification of Web Sites using Naive Bayesian A...

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International MultiConference of Engineers and Computer Scientists (IMECS 2012)

作者： Patil, Ajay S. Pawar, B. V. North Maharashtra Univ Jalgaon MS India

ISBN: (纸本)9789881925114

Subject based web directories like Open Directory Project's (ODP) Directory Mozilla (DMOZ), Yahoo etc., consists of web pages classified into various categories. The proper classification has made these directories popular among the web users. The exponential growth of the web has made it difficult to manage human edited subject based web directories. The World Wide Web (WWW) lacks a comprehensive web site directory. Web site classification using machine learning techniques is therefore an emerging possibility to automatically maintain directory services for the web. Home page of a web site is a distinguished page and it acts as an entry point by providing links to the rest of the web site. The information contained in the title, meta keyword, description and in the labels of the anchor (A HREF) tags along with the other content is a very rich source of features required for classification. Compared to the other pages of the website, webmasters take more care to design the homepage and its content to give it an aesthetic look and at the same time attempt to precisely summarize the organization to which the site belongs. This expression power of the home page of a website can be exploited to identify the nature of the organization. In this paper we attempt to classify web sites based on the content of their home pages using the naive bayesian machine learning algorithm.

关键词： classification machine learning naive bayesian algorithm Web mining

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Analysis and Prediction of New Media Information Dissemination of Police Microblog

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Journal of New Media 2020年第2期2卷 91-98页

作者： Leyao Chen Lei Hong Jiayin Liu Jiangsu Police Institute Nanjing210000China

This paper aims to analyze the microblog data published by the official account in a certain province of China,and finds out the rule of Weibo that is easier to be forwarded in the new police media *** this paper,a new topic-based model is ***,the LDA topic clustering algorithm is used to extract the topic categories with forwarding heat from the microblogs with high forwarding numbers,then the naive bayesian algorithm is used to topic *** sample data is processed to predict the type of microblog *** order to evaluate this method,a large number of microblog online data is used to *** experimental results show that the proposed method can accurately predict the forwarding of Weibo.

关键词： Weibo prediction LDA algorithm naive bayesian algorithm Data mining

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Building Energy Consumption Data Detecting and Recovering Using bayesian Method

Building Energy Consumption Data Detecting and Recovering Us...

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作者： Jun-qi YU Ying TIAN An-jun ZHAO Yun-fei XIE Xin-le HUANG Lei-lei HUI School of Building Services Science and Engineering Xi'an University of Architecture and Technology Smart City Research Center Research Institute of New Urbanization and Human Settlement in Shaanxi Province

Building energy consumption data plays an important role in building energy analysis and energy saving optimization. However, due to difficulties in collecting, high cost, equipment failure and other reasons, the collected data are prone to be missing, which hinders the mining and analysis of building energy consumption data. In this paper, the bayesian network is used to check and recover the building energy consumption data. In the case that the amount of time series data missing is less than 50%, the method G-test is selected to identify abnormal data, and the naive bayesian optimizing Expected Maximum algorithm is used to check the data. When a large number of building energy consumption data missing, the Sparse bayesian learning algorithm is used to fill in the missing data based on the compressed sensing theory. The results show that the model can effectively deal with the problem of missing data of building energy consumption and can be widely used in practical projects.

关键词： Building Energy Consumption Data Detection and Recovery Maximum Expected Value algorithm naive bayesian algorithm Sparse bayesian algorithm

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