检索结果-内蒙古大学图书馆

Construction and Data Analysis of a New Media Content Popularity Prediction Model Based on naive bayes algorithm

Procedia Computer Science 2025年 261卷 294-302页

作者： Yihui Ma College of Digital Media Sichuan University of Culture and Arts Mianyang 621000 Sichuan China

In view of the difficulty of predicting the popularity of content on new media platforms, the naive bayes algorithm is introduced to accurately predict the popularity and dissemination trend of content by analyzing user behavior and content characteristics. This paper constructs a popularity prediction model based on naive bayes and verifies it through actual data, demonstrating the effectiveness of the model in new media content analysis. First, the historical content data on a new media platform is collected and cleaned, including feature information such as article titles, tags, publishing time, user interactions (likes, comments, shares, etc.). Secondly, the naive bayes classification algorithm is used for modeling, and the text features of the content and user interaction data are used as input variables to predict the popularity of the content. The SMOTE (Synthetic Minority Over-sampling Technique) method is used to oversample the training data. By training the model, this paper obtains the probabilistic relationship between various features and popularity. Finally, the cross-validation method is used to evaluate the accuracy of the model, and the prediction results are adjusted and optimized according to the actual data. The short-term, medium-term and long-term features under text + user behavior features + time series features (long-term) respectively improve the model performance to 81.3%, 82%, 82.5% accuracy, F1 value to 0.73, 0.74, 0.75, and recall rate to 79%, 79.5%, 80%. By constructing a popularity prediction model of the naive bayes algorithm, this paper proposes a new data-driven analysis method, which can provide theoretical support and practical guidance for content recommendation and marketing strategies of new media platforms.

关键词： naive bayes algorithm New Media Content Popularity Prediction Model Data Analysis

来源：评论

学校读者我要写书评

暂无评论

Heart Disease Prediction System Using Decision Tree and naive bayes algorithm

引用

CURRENT MEDICAL IMAGING 2019年第8期15卷 712-717页

作者： Maheswari, Subburaj Pitchai, Ramu Natl Engn Coll Dept Comp Sci & Engn Kovilpatti 628503 Tamil Nadu India BV Raju Inst Technol Dept Comp Sci & Engn Narsapur 502313 Telangana India

The huge information of healthcare data is collected from the healthcare industry which is not "mined" unfortunately to make effective decision making for the identification of hidden information. The end user support system is used as the prediction application for the heart disease and this paper proposes windows through the intelligent prediction system the instance guidance for the heart disease is given to the user. Various symptoms of the heart diseases are fed into the application. The user precedes the processes by checking the specific detail and symptoms of the heart disease. The decision tree (ID3) and navie Hayes techniques in data mining are used to retrieve the details associated with each patient. Based on the accurate result prediction, the performance of the system is analyzed.

关键词： Intelligent prediction system decision tree algorithm knowledge representation data mining naive bayes algorithm heart disease prediction

来源：评论

学校读者我要写书评

暂无评论

A naive bayes algorithm for tissue origin diagnosis (TOD-bayes) of synchronous multifocal tumors in the hepatobiliary and pancreatic system

引用

INTERNATIONAL JOURNAL OF CANCER 2018年第2期142卷 357-368页

作者： Jiang, Weiqin Shen, Yifei Ding, Yongfeng Ye, Chuyu Zheng, Yi Zhao, Peng Liu, Lulu Tong, Zhou Zhou, Linfu Sun, Shuo Zhang, Xingchen Teng, Lisong Timko, Michael P. Fan, Longjiang Fang, Weijia Zhejiang Univ Sch Med Affiliated Hosp 1 Canc Biotherapy Ctr Hangzhou Zhejiang Peoples R China Zhejiang Univ Inst Bioinformat Hangzhou Zhejiang Peoples R China Zhejiang Univ IBM Biocomputat Lab Hangzhou Zhejiang Peoples R China Zhejiang Univ Res Ctr Air Pollut & Hlth Hangzhou Zhejiang Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 1 Dept Surg Oncol Hangzhou Zhejiang Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 1 Key Lab Precis Diag & Treatment Hepatobiliary & P Hangzhou Zhejiang Peoples R China Zhejiang Univ Sch Med Med Biotechnol Lab Hangzhou Zhejiang Peoples R China Univ Virginia Dept Biol Charlottesville VA USA Univ Virginia Dept Publ Hlth Sci Charlottesville VA USA

Synchronous multifocal tumors are common in the hepatobiliary and pancreatic system but because of similarities in their histological features, oncologists have difficulty in identifying their precise tissue clonal origin through routine histopathological methods. To address this problem and assist in more precise diagnosis, we developed a computational approach for tissue origin diagnosis based on naive bayes algorithm (TOD-bayes) using ubiquitous RNA-Seq data. Massive tissue-specific RNA-Seq data sets were first obtained from The Cancer Genome Atlas (TCGA) and approximate to 1,000 feature genes were used to train and validate the TOD-bayes algorithm. The accuracy of the model was >95% based on tenfold cross validation by the data from TCGA. A total of 18 clinical cancer samples (including six negative controls) with definitive tissue origin were subsequently used for external validation and 17 of the 18 samples were classified correctly in our study (94.4%). Furthermore, we included as cases studies seven tumor samples, taken from two individuals who suffered from synchronous multifocal tumors across tissues, where the efforts to make a definitive primary cancer diagnosis by traditional diagnostic methods had failed. Using our TOD-bayes analysis, the two clinical test cases were successfully diagnosed as pancreatic cancer (PC) and cholangiocarcinoma (CC), respectively, in agreement with their clinical outcomes. Based on our findings, we believe that the TOD-bayes algorithm is a powerful novel methodology to accurately identify the tissue origin of synchronous multifocal tumors of unknown primary cancers using RNA-Seq data and an important step toward more precision-based medicine in cancer diagnosis and treatment.

关键词： synchronous multifocal tumors tissue origin RNA-Seq naive bayes algorithm hepatobiliary and pancreatic system

来源：评论

学校读者我要写书评

暂无评论

Prediction of Cardiac Disease using naive bayes algorithm 2

Prediction of Cardiac Disease using Naive Bayes Algorithm

引用

2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Ramesh, S. M. Sengottaiyan, N. Vanathi, D. Manoja, R. Tamizharasu, K. Kalyanasundara, P. KPR Inst Engn & Technol Dept ECE Coimbatore Tamil Nadu India Alliance Univ Alliance Coll Engn & Design Fac Sci & Technol Comp Sci Anekal 562106 Karnataka India Nandha Engn Coll Dept CSE Perundurai 638052 Erode India KSR Coll Engn Dept CSE CS Tiruchengode 637215 Tamil Nadu India KSR Coll Engn Dept Comp Sci Tiruchengode 637215 Tamil Nadu India KSR Coll Engn Dept IT Tiruchengode 637215 Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

The goal of this work is to use machine learning methods, such as the naive bayes algorithm, to accurately forecast cardiac disease. The proposed work implements K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and naive bayes to perform regression and classification analysis on the data to attain improved accuracy. This revised dataset is used to train the models, and standard metrics are used to thoroughly assess the model's predicted performance. With an accuracy of 88%, the naive bayes algorithm performed better than the KNN algorithm. In the context of forecasting cardiac disorders, this conversation offers a thorough review of the naive bayes method. The implementation generates accurate predictions based on crucial health factors through data preprocessing, feature selection and model training.

关键词： Machine Learning Accuracy Datasets Prediction naive bayes algorithm Cardiac disease

来源：评论

学校读者我要写书评

暂无评论

PredICT: A Mobile Application for Predicting the Students' Career using naive bayes algorithm 2019

PredICT: A Mobile Application for Predicting the Students' C...

引用

2nd International Conference on Software Engineering and Information Management (ICSIM) / 2nd International Conference on Big Data and Smart Computing (ICBDSC)

作者： Acerado, Risty M. Marco, Roselia C. Santos, John Richard Carpio, Janina Jasmin Isanan, Hannah Aubrey Technol Inst Philippines Quezon City Metro Manila Philippines

ISBN: (纸本)9781450366427

In today's competitive economy, a lot of graduating students from different programs find it hard to look for the appropriate job after graduation that is related to their undergraduate programs. The proposed PredICT mobile application using the naive bayes algorithm is designed to help the students achieve their career goals by predicting the jobs depending on their skills. With the predicted job, the students can have more understanding of the required skills of the career they would most likely pursue. The result of the tests and evaluation shows that most users strongly agreed on the application's software quality.

关键词： Career Prediction naive bayes algorithm Mobile Application

来源：评论

学校读者我要写书评

暂无评论

Research on E-commerce Customer Churn Based on RFM Model and naive bayes algorithm 8th

Research on E-commerce Customer Churn Based on RFM Model and...

引用

8th International Conference on Artificial Intelligence and Security (ICAIS)

作者： Tang, Yuan Li, Yupeng Sun, Guang Hunan Univ Finance & Econ Changsha 410000 Peoples R China

ISBN: (纸本)9783031067945;9783031067938

In recent years, with the rapid development of e-commerce, more and more people are engaged in the e-commerce industry. In order to stand out from numerous e-commerce enterprises and retain customers, it is necessary to achieve accurate marketing, so as to segment the market, locate customer groups, and attract and retain customers through formulating marketing strategies. In this paper, RFM model and naive bayes algorithm are used to analyze customer churn. The three indicators of RFM model are relatively independent and have good representativeness for customer classification, and have been widely used in customer classification in various fields. naive bayes algorithm can calculate the probability of loss more easily, so the combination of the two can be used to identify which kind of customers are more likely to lose. Thus help enterprises to implement different marketing strategies for different customer groups, in order to save the cost of enterprises, improve the efficiency of enterprises.

关键词： RFM model naive bayes algorithm Customer churn forecast

来源：评论

学校读者我要写书评

暂无评论

A Chinese text classification system based on naive bayes algorithm

A Chinese text classification system based on Naive Bayes al...

引用

International Conference on Electronic, Information and Computer Engineering (ICEICE)

作者： Cui, Wei Luzhou Vocat & Tech Coll Dept Informat Technol Luzhou 646005 Sichuan Peoples R China

In this paper, aiming at the characteristics of Chinese text classification, using the ICTCLAS(Chinese lexical analysis system of Chinese academy of sciences) for document segmentation, and for data cleaning and filtering the Stop words, using the information gain and document frequency feature selection algorithm to document feature selection. Based on this, based on the naive bayesian algorithm implemented text classifier, and use Chinese corpus of Fudan University has carried on the experiment and analysis on the system.

关键词： Chinese word segmentation Text categorization Information gain naive bayes algorithm

来源：评论

学校读者我要写书评

暂无评论

Predicton of MRR & Surface Roughness in Wire EDM Machining using Decision Tree and naive bayes algorithm

Predicton of MRR & Surface Roughness in Wire EDM Machining u...

引用

3rd IEEE International Conference on Emerging Smart Computing and Informatics (IEEE-ESCI)

作者： Yogesh, L. Arunadevi, M. Prakash, C. P. S. Dayananda Sagar Coll Engn Dept Mech Engn Bangalore Karnataka India

ISBN: (纸本)9781728185194

Manufacturing industries facing problem in optimal selection of process parameters in machining process. Finding optimum process parameters for achieving maximum Material Removal Rate and minimum Surface Roughness is a challenging task and it requires lot of time and energy for experimentation trails or experience. It wastes lot of resources and money, sometimes ends up with negative results. To overcome the above issue, this paper presents an algorithm for prediction of Surface Roughness and Material Removal rate using Decision Tree algorithm and naive bayes algorithm without experimentation. Lot of resources and time can be saved using these machine learning algorithms. In this paper, Material removal rate and Surface roughness of EDM machining of Aluminum composites is predicted using Decision tree algorithm and naive bayes algorithm. Then the model can be used to predict the Material Removal Rate and Surface finish of any combination process parameters before machining process.

关键词： Decision Tree algorithm EDM Optimization naive bayes algorithm Surface Roughness Machine Learning

来源：评论

学校读者我要写书评

暂无评论

Using naive bayes algorithm to Students' bachelor Academic Performances Analysis 4

Using Naive Bayes Algorithm to Students' bachelor Academic P...

引用

4th IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS)

作者： Razaque, Fahad Soomro, Nareena Shaikh, Shoaib Ahmed Soomro, Safeeullah Samo, Javed Ahmed Kumar, Natesh Dharejo, Huma Indus Univ Dept Comp Karachi Sindh Pakistan Hamdard Univ Karachi Sindh Pakistan Aligarh Inst Technol Karachi Sindh Pakistan AMA Int Univ Coll Comp Studies Salmabad Bahrain

ISBN: (纸本)9781538621073;9781538621066

Academic Data Mining was one of emerging field which comprise procedure of examined students' details by different elements such as earlier semester marks, attendance, assignment, discussion, lab work were of used to improved bachelor academic performance of students, and overcome difficulties of low ranks of bachelor students. It was extracted useful knowledge from bachelor academic students data collected from department of Computing. Subsequently preprocessing data, which was applied data mining techniques to discover classification and clustering. In this study, classification method was described which was based on naive byes algorithm and used for Academic data mining. It was supportive to students along with to lecturers for evaluation of academic performance. It was cautionary method for student's to progress their performance of study.

关键词： Clustering Classification naive bayes algorithm Students Performance

来源：评论

学校读者我要写书评

暂无评论

The Realization of naive bayes algorithm in the Logic Programming Framework PROLOG

The Realization of Naive Bayes Algorithm in the Logic Progra...

引用

IEEE North-West-Russia-Section Young Researchers in Electrical and Electronic Engineering Conference (ElConRusNW)

作者： Malov, Alexei Rodionov, Sergey Kholod, Ivan Motorola Solut St Petersburg Russia St Petersburg Electrotech Univ LETI Fac Comp Sci & Technol St Petersburg Russia

The main problems and features of data mining are observed. The conveniences providing by the usage of functional language PROLOG for the solving of typical data mining problems are observed through the example of the... 详细信息

ISBN: (纸本)9781509004454

关键词： Data mining naive bayes algorithm logic programming PROLOG

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：