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10th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS)

作者： Ma, Yong Liang, Shuang Chen, Xuan Jia, Chunfu Civil Aviat Univ Informat Secur Evaluat Ctr Tianjin Peoples R China Nankai Univ Coll Comp & Control Engn Tianjin Peoples R China

ISBN: (纸本)9781509009848

Recently the act of fraud aimed at the air tickets is more and more rampant. Lawbreakers get passenger information from illegal channels, not only make a lot of passengers suffering financial loss, but also influence the reputation and business of airline. There are many ways that passenger information easy to be leaked, in this paper, we just focus on the detection of abnormal access behaviors that hacker intrudes the frequent passenger system to get the passenger information. In this paper, we analyze the passenger's behavior of visiting the frequent passenger system deeply. Then we base on the naive bayes algorithm to detect the behavior of passengers, so that we can decide whether the account is abnormal. Through the experiment and evaluation, we demonstrate that our approach is effective to identify the illegal log in of hackers and makes a great help in improving the security of passenger information.

关键词： abnormal Access Behavior detection naive bayes algorithm information security

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Identification of hidden disaster causing factors in coal mine based on naive bayes algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第2期41卷 2823-2831页

作者： Zhao, Yifan Tian, Shuicheng Xian Univ Sci & Technol Coll Safety Sci & Engn Xian Peoples R China Xian Univ Sci & Technol Inst Safety & Emergency Management Xian Peoples R China

In order to overcome the problems of low recognition rate and long recognition time existing in traditional methods, a method for identifying hidden disaster factors in coal mines based on naive bayes algorithm was proposed. The posterior probability of bayesian network is calculated to obtain the maximum value of the posterior probability, so as to judge the categories of hidden disaster factors in coal mines. The method of combining soft and hard threshold functions is used to denoise naive bayes network. Combined with the structural equation of coal mine concealed disaster-causing factors, the index weight of coal mine disaster-causing factors is calculated, and a fast identification model of disaster-causing factors is built to complete the identification. Experimental results show that the quality factors of the proposed method are all higher than 8, the recognition rate is as high as 98%, and the recognition time is basically controlled within 0.8 s.

关键词： naive bayes algorithm coal mine hidden disaster factors identification

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Spam filter based on naive bayes algorithm

Spam filter based on naive bayes algorithm

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作者： Mengyuan Han The department of International Software Engineering Dalian University of Technology

The widespread use of Electronic Mail(E-mail) has led to a significant increase in spam,which has severely impeded the growth and well-being of the *** mitigate this issue,the implementation of email filtering techniques has become necessary,requiring the use of specific technological ***,the K-Nearest Neighbor(KNN),Support Vector Machine(SVM),and naive bayes(NB) algorithms are commonly used in probability statistical classification methods for email *** these,the NB algorithm is the most classical,with its rich mathematical theory as the basis,high classification efficiency,and straightforward algorithmic ***,the algorithm relies on the conditional independence assumption,making the accuracy susceptible to the correlation between *** study focuses on email filtering techniques based on the NB algorithm,conducting experiments to evaluate the classification accuracy and proposing feasible improvements to weaken the independence *** experimental results demonstrated the effectiveness of the employed method.

关键词： spam filter naive bayes algorithm machine learning

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Dynamic human contact prediction based on naive bayes algorithm in mobile social networks

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SOFTWARE-PRACTICE & EXPERIENCE 2020年第11期50卷 2031-2045页

作者： Zeng, Feng Yao, Lan Wu, Baoling Li, Wenjia Meng, Lin Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Hunan Univ Coll Math & Econometr Changsha 410082 Peoples R China New York Inst Technol Dept Comp Sci New York NY USA Ritsumeikan Univ Dept Elect & Comp Engn Kusatsu Japan

Human contact prediction is a challenging task in mobile social networks. The existing prediction methods are based on the static network structure, and directly applying these static prediction methods to dynamic network prediction is bound to reduce the prediction accuracy. In this paper, we extract some important features to predict human contacts and propose a novel human contact prediction method based on naive bayes algorithm, which is suitable for dynamic networks. The proposed method takes the ever-changing structure of mobile social networks into account. First, the past time is partitioned into many periods with equal intervals, and each period has a feature matrix of all node pairs. Then, with the feature matrixes used for classifiers training based on naive bayes algorithm, we can get a classifier for each time period. At last, the different weights are assigned to the classifiers according to their importance to contact prediction, and all classifiers are weighted combination into the final prediction classifier. The extensive experiments are conducted to verify the effectiveness and superiority of the proposed method, and the results show that the proposed method can improve the prediction accuracy and TP Rate to a large extent. Besides, we find that the size of time interval has a certain impact on the clustering coefficient of mobile social networks, which further affects the prediction accuracy.

关键词： clustering coefficient features extraction human contact prediction mobile social networks naive bayes algorithm time interval

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

A naive Bayes algorithm for tissue origin diagnosis(TOD-Baye...

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浙江省生物信息学学会第五届学术年会暨大数据时代精准医学国际学术研讨会

作者： WeiqinJiang Yifei Shen Yongfeng Ding Longjiang Fan Weijia Fang Cancer Biotherapy Center First Affiliated HospitalZhejiang University Institute of Bioinformatics &IBM Bio-computational Laboratory Zhejiang University Research Center for Air Pollution and Health Zhejiang University Department of Surgical Oncology First Affiliated HospitalZhejiang University Key Laboratory of Precision Diagnosis & Treatment for Hepatobiliary & Pancreatic Tumor of Zhejiang Province First Affiliated HospitalZhejiang University

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 *** 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 *** tissue-specific RNA-Seq data sets were first obtained from The Cancer Genome Atlas（TCGA） and ,000 feature genes were used to train and validate the TOD-bayes *** accuracy of the model was > 95%based on 10-fold cross validation by the data from TCGA.A total of 18 clinical cancer samples（including 6 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 *** 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 *** 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 towards 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

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Internet Traffic Detection using naive bayes and K-Nearest Neighbors (KNN) algorithm

Internet Traffic Detection using Naive Bayes and K-Nearest N...

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International Conference on Intelligent Computing and Control Systems (ICCS)

作者： Dixit, Mrudul Sharma, Ritu Shaikh, Saniya Muley, Krutika MKSSSs Cummins Coll Engn Women Pune Dept Elect & Telecommun Pune Maharashtra India

ISBN: (纸本)9781538681138

Growth of internet has led to rise in number of users and its usage. Despite its advantages, exponential rise in internet usage has resulted in excess data flow over the system flooding the internet. To maintain quality of service and speed of internet along with ensuring data security as well as preventing data misuse, analysis of the internet data becomes essential. Analysis of the dataflow involves characterizing it into different types. This can be done by inspecting the packets either on basis of port numbers, payload information or statistical features. This paper aims to discuss the analysis of internet traffic using statistical features such as interpacket arrival time, time to live and number of packets helping us prevent invasion of packet information. This helps us protect user's privacy. To automate the process of categorizing internet traffic, machine learning based supervised classification techniques namely naive bayes and K Nearest Neighbors are implemented. Experiments to obtain highest accuracy in classifying internet traffic on basis of transaction protocol were performed. The dataset used is UNSW-NB. The results show that classification using K-Nearest Neighbors algorithm gives accuracy of 85% whereas maximum accuracy achieved using naive bayes algorithm is 54%.

关键词： Internet traffic detection machine learning K-Nearest Neighbors naive bayes algorithm

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Crop Price Prediction Using Machine Learning naive bayes algorithms 1

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2nd International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)

作者： Vikram, R. Divij, R. Hishore, N. Naveen, G. Rudhramoorthy, D. MKumarasamy Coll Engn Dept Comp Sci & Engn Karur 639113 Tamil Nadu India

ISBN: (数字)9789811925412

ISBN: (纸本)9789811925412;9789811925405

In our nation, development is the crucial mainstay of the economy improvement. A large portion of families are dependent on cultivation. Most of the land is used for cultivation to resolve the issues of the quantity of occupants in the area. Modernize agrarian practices to meet the mentioning requirements. Our assessment hopes to handle the issue of yield cost conjecture even more feasibly to ensure farmers' profit. To consider better plans, it uses Machine Learning different techniques on various information. In our day today life we are using the agricultural products, Agricultural product price will we varied from one month to another month. Sometimes weather conditions crop price will be varied from one season to another season. If any major price fluctuation is happened in the Agricultural field means it will affect the Gross Domestic Product (GDP). In this paper we are going to predicting the crop price fluctuations from one season to another season Which was very useful for farmers. These predications will help to farmer in the way of proper analysing the crop price to the next season.

关键词： Data analysis naive bayes algorithm Predictions Prophet and Booster algorithm

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Analysing Effectiveness of Sentiments in Social Media Data Using Machine Learning Techniques

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Journal of Computer and Communications 2025年第1期13卷 136-151页

作者： Thambusamy Velmurugan Mohandas Archana Ajith Singh Nongmaithem PG & Research Department of Computer Science D.G.Vaishnav College Chennai India PG Department of IT & BCA D.G.Vaishnav College Chennai India Department of Computer Science Souht East Manipur College Manipur India

Every second, a large volume of useful data is created in social media about the various kind of online purchases and in another forms of reviews. Particularly, purchased products review data is enormously growing in different database repositories every day. Most of the review data are useful to new customers for theier further purchases as well as existing companies to view customers feedback about various products. Data Mining and Machine Leaning techniques are familiar to analyse such kind of data to visualise and know the potential use of the purchased items through online. The customers are making quality of products through their sentiments about the purchased items from different online companies. In this research work, it is analysed sentiments of Headphone review data, which is collected from online repositories. For the analysis of Headphone review data, some of the Machine Learning techniques like Support Vector Machines, naive bayes, Decision Trees and Random Forest algorithms and a Hybrid method are applied to find the quality via the customers’ sentiments. The accuracy and performance of the taken algorithms are also analysed based on the three types of sentiments such as positive, negative and neutral.

关键词： Support Vector Machine Random Forest algorithm naive bayes algorithm Machine Learning Techniques Decision Tree algorithm

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Rock-Burst Occurrence Prediction Based on Optimized naive bayes Models

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IEEE ACCESS 2021年 9卷 91347-91360页

作者： Ke, Bo Khandelwal, Manoj Asteris, Panagiotis G. Skentou, Athanasia D. Mamou, Anna Armaghani, Danial Jahed Wuhan Univ Technol Sch Resources & Environm Engn Wuhan 430070 Peoples R China Wuchang Univ Technol Sch Intelligent Construct Wuhan 430223 Peoples R China Federat Univ Australia Sch Engn Informat Technol & Phys Sci Ballarat Vic 3350 Australia Sch Pedag & Technol Educ Computat Mech Lab Athens 14121 Greece South Ural State Univ Inst Architecture & Construct Dept Urban Planning Engn Networks & Syst Chelyabinsk 454080 Russia

Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naive bayes models, in predicting rock-burst failures in underground projects. The naive bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naive bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naive bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naive bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naive bayes algorithms in predicting rock-burst occurrence.

关键词： Optimization Rocks Predictive models Radio frequency Stress Genetic algorithms Excavation Evolutionary random forest naive bayes algorithm particle swarm optimization rock-burst occurrence weight optimization

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An Ensemble-Based Hotel Reviews System Using naive bayes Classifier

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 131-154页

作者： Joseph Bamidele Awotunde Sanjay Misra Vikash Katta Oluwafemi Charles Adebayo Department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Computer Science and Communication Otfold University CollegeHaldenNorway

The task of classifying opinions conveyed in any form of text online is referred to as sentiment *** emergence of social media usage and its spread has given room for sentiment analysis in our daily *** media applications and websites have become the foremost spring of data recycled for reviews for sentimentality in various *** subject matter can be encountered on social media platforms,such as movie product reviews,consumer opinions,and testimonies,among others,which can be used for sentiment *** rapid uncovering of these web contents contains divergence of many benefits like profit-making,which is one of the most vital of them *** to a recent study,81%of consumers conduct online research prior to making a *** the reviews available online are too huge and numerous for human brains to process and ***,machine learning classifiers are one of the prominent tools used to classify sentiment in order to get valuable information for use in companies like hotels,game companies,and so *** the sentiments of people towards different commodities helps to improve the services for contextual promotions,referral systems,and market ***,this study proposes a sentiment-based framework detection to enable the rapid uncovering of opinionated contents of hotel reviews.A naive bayes classifier was used to process and analyze the dataset for the detection of the polarity of the *** dataset from Datafiniti’s Business Database obtained from Kaggle was used for the experiments in this *** performance evaluation of the model shows a test accuracy of 96.08%,an F1-score of 96.00%,a precision of 96.00%,and a recall of 96.00%.The results were compared with state-of-the-art classifiers and showed a promising performance andmuch better in terms of performancemetrics.

关键词： Sentiment analysis hotel reviews naive bayes algorithm consumer opinions web 2.0 machine learning

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