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检索条件"主题词=naive bayes algorithm"

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Two Layers Multi-class Detection Method for Network Intrusion Detection System

Two Layers Multi-class Detection Method for Network Intrusio...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Yuan, Yali Huo, Liuwei Hogrefe, Dieter Inst Comp Sci Goldschmidtstr 7 D-37077 Gottingen Germany Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9781538616291

Intrusion Detection Systems (IDSs) are powerful systems which monitor and analyze events in order to detect signs of security problems and take action to stop intrusions. In this paper, the Two Layers Multi-class Detection (TLMD) method used together with the C5.0 method and the naive bayes algorithm is proposed for adaptive network intrusion detection, which improves the detection rate as well as the false alarm rate. The proposed TLMD algorithm also addresses some difficulties in data mining situations such as handling imbalance datasets, dealing with continuous attributes, and reducing noise in training dataset. We compared the performance of the proposed TLMD method with that of existing algorithms, using the detection rate, accuracy as well as false alarm rate on the KDDcup99 benchmark intrusion detection dataset. The experimental results prove that the proposed TLMD method has a reduced false alarm rate and a good detection rate based on the imbalanced dataset.

关键词： Intrusion Detection Systems C5.0 Method naive bayes algorithm Imbalance Data Detection Rate False Alarm Rate

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An Intelligent Software defined Network Controller for preventing Distributed Denial of Service Attack 2

An Intelligent Software defined Network Controller for preve...

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Prakash, Aditya Priyadarshini, Rojalina CV Raman Coll Engn Dept Informat Technol Bhubaneswar Odisha India

ISBN: (纸本)9781538619742

Software Defined Network (SDN) architecture is a new and novel way of network management mechanism. In SDN, switches do not process the incoming packets like conventional network computing environment. They match for the incoming packets in the forwarding tables and if there is none it will be sent to the controller for processing which is the operating system of the SDN. A Distributed Denial of Service (DDoS) attack is a biggest threat to cyber security in SDN network. The attack will occur at the network layer or the application layer of the compromised systems that are connected to the network. In this paper a machine learning based intelligent method is proposed which can detect the incoming packets as infected or not. The different machine learning algorithms adopted for accomplishing the task are naive bayes, K-Nearest neighbor (KNN) and Support vector machine (SVM) to detect the anomalous behavior of the data traffic. These three algorithms are compared according to their performances and KNN is found to be the suitable one over other two. The performance measure is taken here is the detection rate of infected packets.

关键词： Software defined network Denial of service Distributed denial of service Support vector machine K-Nearest Neighbor naive bayes algorithm

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Student Attention Evaluation System Using Machine Learning for Decision Making 19th

Student Attention Evaluation System Using Machine Learning f...

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19th EPIA Conference on Artificial Intelligence (EPIA)

作者： Duraes, Dalila Polytech Inst Porto ESTG CIICESI Felgueiras Portugal Univ Minho Dept Informat Algoritmi Res Ctr Braga Portugal

ISBN: (纸本)9783030302412;9783030302405

The student attention evaluation is a very important feature that has a high influence on student results, so it's necessary to consider. This paper focuses on the evaluation of the student attention level using a machine learning categorization model using several machine learning techniques. The Support Vector Machine, Nearest Neighbor, naive bayes, Neural Networks and Random Forest algorithms are applied to model an intelligent system which will evaluate the attention level of the students. Thirteen important features such as alltime, percApp (time in the application), age, grade, behavior biometrics of keyboard (kdt - key down time, tbk - time between keys) and behavior biometrics of mouse (cd - click duration, my - mouse velocity, ma - mouse acceleration, ddc - duration distance clicks, dplbc - distance point to line between clicks, dbc - distance between clicks, and tbc - time between clicks) are taken for training and testing. Above mentioned machine learning techniques are compared in terms of accuracy rate.

关键词： Support Vector Machine Nearest Neighbor naive bayes algorithm Neural Networks Random Forest

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A Comparative Study of Classification algorithms for Predicting Gestational Risks in Pregnant Women

A Comparative Study of Classification Algorithms for Predict...

引用

International Conference on Computers, Communications and Systems (ICCCS)

作者： Lakshmi, B. N. Indumathi, T. S. Ravi, Nandini VTU RRC VIAT PG Res Ctr Bangalore Karnataka India Dhruva Nursing Home Obstet & Gynaecol Obs & Gyn Bangalore Karnataka India

ISBN: (纸本)9781467397544

Pregnant women must be protected from complications during the period of gestation which is a stage every woman undergoes many physiological changes, sometimes inducing complications that may turn severe and lead to death of both mother and fetus. Classification methods are popularly used method whose algorithms are best suitable in medical diagnosis. Among them C4.5 Decision Tree algorithm and naive bayes algorithm are effectively used for classifying medical data. Both algorithms are most capable to classify the pregnancy data of present research study. The main aim of this paper is to highlight concept of standardization of parameters selected for data collection in study to achieve elevated results of classification by selected classification algorithms. The paper also presents how efficiently risk levels can be predicted by the selected algorithms to protect pregnant women from complications in pregnant women health. Pregnancy data of present research study is considered for classifying and predicting complications leading to different risk levels during pregnancy. The best algorithm among selected two algorithms namely C4.5 and naive bayes Classifier is identified in the paper for further improving the efficiency of selected classifier. By identifying the risks in pregnancy, present maternal and fetal mortality rate can be efficiently reduced to lower rates thus providing a safe pregnancy period, post natal period and a healthy baby to pregnant mothers.

关键词： Pregnancy classification C4.5 Decision Tree Classification algorithm Gestation naive bayes algorithm Comparison

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Teacher's Performance Evaluation Tool Using Opinion Mining with Sentiment Analysis

Teacher's Performance Evaluation Tool Using Opinion Mining w...

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IEEE Region 10 Symposium (TENSYMP)

作者： Balahadia, Francis F. Fernando, Ma. Corazon G. Juanatas, Irish C. FEU Inst Technol Coll Comp Studies Manila Philippines

ISBN: (纸本)9781509009312

The research aims to develop a teacher's performance evaluation tool using opinion mining with sentiment analysis. The study may help to identify the strengths and weaknesses of the faculty members based on the positive and negative feedback of the students either in English or in Filipino language. The proposed system provides the sentiment score from the qualitative data and numerical response rating from the quantitative data of teachers evaluation. It will also graphically represent the evaluation result including the percentage of positive and negative feedback of the students. Thus, the school administrators and educators will be more aware about the sentiments and concerns of the students.

关键词： Sentiment Analysis naive bayes algorithm Teacher Performance Rating Quantitative and Qualitative Ratings

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Classification of Leakage Current Waveforms using Wavelet Packet Transform on High Voltage Insulator 4

Classification of Leakage Current Waveforms using Wavelet Pa...

引用

International Conference on High Voltage Engineering and Application (ICHVE)

作者： Chaou, A. K. Mekhaldi, A. Moula, B. Teguar, M. Ecole Natl Polytech Alger Lab Rech Electrotech 10 Ave Hassen BadiBP 182 Algiers 16200 Algeria

ISBN: (纸本)9781479966134

In this paper, the Wavelet Packet Transform (WPT) for Leakage Current (LC) examination on high voltage insulators under pollution conditions is exposed. Based on laboratory experiments under various artificial solution natures (consisting in a mixture of distilled water with NaCl, Kaolin or Kieselguhr), LC acquisition is firstly carried out. After a careful examination, three groups of LC waveforms are constituted depending on their peak values. Then, WPT is used to decompose LC waveforms. From this decomposition, feature extraction by energy calculation is processed. Hence, a feature vector, composed of wavelet coefficients energies values, is used as input for three classification algorithms consisting in K-Nearest Neighbors, naive bayes and Support Vector Machine, to distinguish between three LC groups. Indeed, this paper introduces WPT for LC investigation and classification.

关键词： bayes methods feature extraction insulators leakage currents power engineering computing support vector machines wavelet transforms K-nearest neighbor algorithm LC acquisition LC examination naive bayes algorithm WPT energy calculation feature vector high voltage insulator leakage current waveform classification pollution condition support vector machine wavelet packet transform Classification algorithms Discrete wavelet transforms Feature extraction Insulators Pollution Wavelet packets Feature extraction Wavelet packets insulators Support Vector Network Sorting algorithm K-nearest neighbor algorithm High Voltage Eigenvector discrete wavelet transform Power engineering computing bayes methods wavelet transforms leakage currents line controller

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Exploration of Autism Spectrum Disorder using Classification algorithms 2nd

Exploration of Autism Spectrum Disorder using Classification...

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2nd International Conference on Recent Trends in Advanced Computing, Disruptive Innovation (ICRTAC-DTI)

作者： Deepa, B. Marseline, Jeen K. S. Sri Krishna Arts & Sci Coll Coimbatore 8 Tamil Nadu India

Data mining plays a vital role in identifying the classification algorithm for classification and prediction of disease in medical field. Predicting disease depends on the accuracy of the dataset and mechanism of machine learning algorithms used to classify the dataset. To detect new born with risk of autism spectrum disorder (ASD) which is predominantly diagnosing the behavioral observation of children with above 2 years. Although identifying brain disease using magnetic resonance imaging (MRI) available in the last few years it is time consuming and very expensive. Early stage of predicting autism disorder done with the help of using machine learning algorithms. This paper criticize various classification algorithms then evaluates the accuracy and then selects the best classification algorithm to analyze and classify each individual who gets affect from autism spectrum disorder (ASD) in early stage. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Data mining naive bayes algorithm Decision table Confusion Matrix Autism Support Vector Machine

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A Classification Model to Analyze the Spread and Emerging Trends of the Zika Virus in Twitter 3rd

引用

3rd International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Tripathy, B. K. Thakur, Saurabh Chowdhury, Rahul VIT Univ Sch Comp Sci & Engn Vellore Tamil Nadu India

ISBN: (纸本)9789811038747;9789811038730

The Zika disease is a 2015-16 virus epidemic and continues to be a global health issue. The recent trend in sharing critical information on social networks such as Twitter has been a motivation for us to propose a classification model that classifies tweets related to Zika and thus enables us to extract helpful insights into the community. In this paper, we try to explain the process of data collection from Twitter, the preprocessing of the data, building a model to fit the data, comparing the accuracy of support vector machines and naive bayes algorithm for text classification and state the reason for the superiority of support vector machine over naive bayes algorithm. Useful analytical tools such as word clouds are also presented in this research work to provide a more sophisticated method to retrieve community support from social networks such as Twitter.

关键词： Zika Twitter analysis Twitter classification Support vector machines naive bayes algorithm

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A Comparative Study of Classification algorithms for Risk Prediction in Pregnancy 35

A Comparative Study of Classification Algorithms for Risk Pr...

引用

IEEE Region 10 Conference

作者： Lakshmi, B. N. Indumathi, T. S. Ravi, Nandini Visvesvaraya Technol Univ PG Res Ctr Bangalore Karnataka India Dhruva Nursing Home Obstet & Gynaecol Bangalore Karnataka India

ISBN: (纸本)9781479986415

Pregnancy is one of the most delicate stages in every woman's life requiring increased medical care and attention. Pregnancy induces a variety of abnormalities which may lead to severe complications if unnoticed or neglected. Pregnancy complications are health problems that are caused due to changes in physiological parameters during the period of gestation. Pregnancy complications can lead to severe maternal illness by women during pregnancy, at delivery and after delivery. The aim of this paper is to predict the present complications in the health of a pregnant woman using two classification algorithms namely C4.5 decision tree classification algorithm and naive bayes Classification algorithm. The selected algorithms are powerful and popular tools used for the tasks of classification and prediction in Data mining. These two algorithms use pregnancy data collected from pregnant women in different stages of pregnancy to predict their present health state and the associated health complications. This study focuses on identifying the best algorithm among the two classification algorithms to predict the health status of each pregnant woman and its associated complication. Applying these classification techniques on pregnancy related data the status of risk in the health of any pregnant women can be identified and maternal and fetal morality rate can be reduced.

关键词： Pregnancy classification C4.5 Decision Tree Classification algorithm naive bayes algorithm Comparison

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Understanding the Mobile Game App Activity 19

Understanding the Mobile Game App Activity

引用

Annual ACM Southeast Conference (ACMSE)

作者： Vyas, Jhanvi Devangbhai Han, Meng Kennesaw State Univ Data Driven Intelligence Res DIR Lab Marietta GA 30060 USA

ISBN: (纸本)9781450362511

The ratings and reviews found on online stores leave a powerful impact on new users. The idea of the project focuses on identifying clues from ratings, reviews and other related factors to ensure a secure future of the application. This project collects raw data such as ratings, customer reviews and price of top game applications on Google Play store, and performing analysis based on category and applications to draw results.

关键词： Web Scrapping BeautifulSoup Requests Natural Language Processing Natural Language Toolkit Sentimental Analysis naive bayes algorithm TextBlob Analysis

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