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model</title> <meta name="description" content="Search results for: Naive Bayes model"> <meta name="keywords" content="Naive Bayes model"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Naive Bayes model"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 7477</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Naive Bayes model</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7477</span> Improving Classification in Bayesian Networks using Structural Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hong%20Choon%20Ong">Hong Choon Ong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Na茂ve Bayes classifiers are simple probabilistic classifiers. Classification extracts patterns by using data file with a set of labeled training examples and is currently one of the most significant areas in data mining. However, Na茂ve Bayes assumes the independence among the features. Structural learning among the features thus helps in the classification problem. In this study, the use of structural learning in Bayesian Network is proposed to be applied where there are relationships between the features when using the Na茂ve Bayes. The improvement in the classification using structural learning is shown if there exist relationship between the features or when they are not independent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Bayesian%20Network" title="Bayesian Network">Bayesian Network</a>, <a href="https://publications.waset.org/search?q=Classification" title=" Classification"> Classification</a>, <a href="https://publications.waset.org/search?q=Na%C3%AFve%20Bayes" title=" Na茂ve Bayes"> Na茂ve Bayes</a>, <a href="https://publications.waset.org/search?q=Structural%20Learning." title="Structural Learning.">Structural Learning.</a> </p> <a href="https://publications.waset.org/15047/improving-classification-in-bayesian-networks-using-structural-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15047/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15047/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15047/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15047/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15047/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15047/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15047/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15047/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15047/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15047/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15047.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2599</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7476</span> Utilizing Innovative Techniques to Improve Email Security</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Amany%20M.%20Alshawi">Amany M. Alshawi</a>, <a href="https://publications.waset.org/search?q=Khaled%20Alduhaiman"> Khaled Alduhaiman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a technique to protect against email bombing. The technique employs a statistical approach, Na茂ve Bayes (NB), and Neural Networks to show that it is possible to differentiate between good and bad traffic to protect against email bombing attacks. Neural networks and Na茂ve Bayes can be trained by utilizing many email messages that include both input and output data for legitimate and non-legitimate emails. The input to the model includes the contents of the body of the messages, the subject, and the headers. This information will be used to determine if the email is normal or an attack email. Preliminary tests suggest that Na茂ve Bayes can be trained to produce an accurate response to confirm which email represents an attack. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Email%20bombing" title="Email bombing">Email bombing</a>, <a href="https://publications.waset.org/search?q=Legitimate%20email" title=" Legitimate email"> Legitimate email</a>, <a href="https://publications.waset.org/search?q=Na%C3%AFve%20Bayes" title=" Na茂ve Bayes"> Na茂ve Bayes</a>, <a href="https://publications.waset.org/search?q=Neural%20networks" title=" Neural networks"> Neural networks</a>, <a href="https://publications.waset.org/search?q=Non-legitimate%20email." title=" Non-legitimate email."> Non-legitimate email.</a> </p> <a href="https://publications.waset.org/4554/utilizing-innovative-techniques-to-improve-email-security" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4554/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4554/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4554/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4554/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4554/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4554/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4554/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4554/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4554/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4554/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4554.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1420</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7475</span> Modified Na茂ve Bayes Based Prediction Modeling for Crop Yield Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Kefaya%20Qaddoum">Kefaya Qaddoum</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to model a simple but often satisfactory supervised classification method. The original naive Bayes have a serious weakness, which is producing redundant predictors. In this paper, utilized regularization technique was used to obtain a computationally efficient classifier based on naive Bayes. The suggested construction, utilized L1-penalty, is capable of clearing redundant predictors, where a modification of the LARS algorithm is devised to solve this problem, making this method applicable to a wide range of data. In the experimental section, a study conducted to examine the effect of redundant and irrelevant predictors, and test the method on WSG data set for tomato yields, where there are many more predictors than data, and the urge need to predict weekly yield is the goal of this approach. Finally, the modified approach is compared with several naive Bayes variants and other classification algorithms (SVM and kNN), and is shown to be fairly good.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Tomato%20yields%20prediction" title="Tomato yields prediction">Tomato yields prediction</a>, <a href="https://publications.waset.org/search?q=naive%20Bayes" title=" naive Bayes"> naive Bayes</a>, <a href="https://publications.waset.org/search?q=redundancy" title=" redundancy"> redundancy</a> </p> <a href="https://publications.waset.org/9997276/modified-naive-bayes-based-prediction-modeling-for-crop-yield-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9997276/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9997276/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9997276/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9997276/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9997276/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9997276/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9997276/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9997276/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9997276/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9997276/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9997276.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">5109</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7474</span> Sentiment Analysis of Fake Health News Using Naive Bayes Classification Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Danielle%20Shackley">Danielle Shackley</a>, <a href="https://publications.waset.org/search?q=Yetunde%20Folajimi"> Yetunde Folajimi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>As more people turn to the internet seeking health related information, there is more risk of finding false, inaccurate, or dangerous information. Sentiment analysis is a natural language processing technique that assigns polarity scores of text, ranging from positive, neutral and negative. In this research, we evaluate the weight of a sentiment analysis feature added to fake health news classification models. The dataset consists of existing reliably labeled health article headlines that were supplemented with health information collected about COVID-19 from social media sources. We started with data preprocessing, tested out various vectorization methods such as Count and TFIDF vectorization. We implemented 3 Naive Bayes classifier models, including Bernoulli, Multinomial and Complement. To test the weight of the sentiment analysis feature on the dataset, we created benchmark Naive Bayes classification models without sentiment analysis, and those same models were reproduced and the feature was added. We evaluated using the precision and accuracy scores. The Bernoulli initial model performed with 90% precision and 75.2% accuracy, while the model supplemented with sentiment labels performed with 90.4% precision and stayed constant at 75.2% accuracy. Our results show that the addition of sentiment analysis did not improve model precision by a wide margin; while there was no evidence of improvement in accuracy, we had a 1.9% improvement margin of the precision score with the Complement model. Future expansion of this work could include replicating the experiment process, and substituting the Naive Bayes for a deep learning neural network model. </p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Sentiment%20analysis" title="Sentiment analysis">Sentiment analysis</a>, <a href="https://publications.waset.org/search?q=Naive%20Bayes%20model" title=" Naive Bayes model"> Naive Bayes model</a>, <a href="https://publications.waset.org/search?q=natural%0D%0Alanguage%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/search?q=topic%20analysis" title=" topic analysis"> topic analysis</a>, <a href="https://publications.waset.org/search?q=fake%20health%20news%20classification%0D%0Amodel." title=" fake health news classification model."> fake health news classification model.</a> </p> <a href="https://publications.waset.org/10012995/sentiment-analysis-of-fake-health-news-using-naive-bayes-classification-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012995/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012995/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012995/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012995/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012995/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012995/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012995/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012995/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012995/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012995/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012995.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">488</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7473</span> Comparing SVM and Na茂ve Bayes Classifier for Automatic Microaneurysm Detections </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=A.%20Sopharak">A. Sopharak</a>, <a href="https://publications.waset.org/search?q=B.%20Uyyanonvara"> B. Uyyanonvara</a>, <a href="https://publications.waset.org/search?q=S.%20Barman"> S. Barman </a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Diabetic%20retinopathy" title="Diabetic retinopathy">Diabetic retinopathy</a>, <a href="https://publications.waset.org/search?q=microaneurysm" title=" microaneurysm"> microaneurysm</a>, <a href="https://publications.waset.org/search?q=Na%C3%AFve%20Bayes%20classifier" title=" Na茂ve Bayes classifier"> Na茂ve Bayes classifier</a>, <a href="https://publications.waset.org/search?q=SVM%20classifier." title=" SVM classifier."> SVM classifier.</a> </p> <a href="https://publications.waset.org/9998289/comparing-svm-and-naive-bayes-classifier-for-automatic-microaneurysm-detections" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9998289/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9998289/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9998289/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9998289/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9998289/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9998289/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9998289/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9998289/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9998289/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9998289/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9998289.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">6106</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7472</span> Performance Comparison of ADTree and Naive Bayes Algorithms for Spam Filtering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Thanh%20Nguyen">Thanh Nguyen</a>, <a href="https://publications.waset.org/search?q=Andrei%20Doncescu"> Andrei Doncescu</a>, <a href="https://publications.waset.org/search?q=Pierre%20Siegel"> Pierre Siegel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Classification is an important data mining technique and could be used as data filtering in artificial intelligence. The broad application of classification for all kind of data leads to be used in nearly every field of our modern life. Classification helps us to put together different items according to the feature items decided as interesting and useful. In this paper, we compare two classification methods Naïve Bayes and ADTree use to detect spam e-mail. This choice is motivated by the fact that Naive Bayes algorithm is based on probability calculus while ADTree algorithm is based on decision tree. The parameter settings of the above classifiers use the maximization of true positive rate and minimization of false positive rate. The experiment results present classification accuracy and cost analysis in view of optimal classifier choice for Spam Detection. It is point out the number of attributes to obtain a tradeoff between number of them and the classification accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Classification" title="Classification">Classification</a>, <a href="https://publications.waset.org/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/search?q=spam%20filtering" title=" spam filtering"> spam filtering</a>, <a href="https://publications.waset.org/search?q=naive%0D%0ABayes" title=" naive Bayes"> naive Bayes</a>, <a href="https://publications.waset.org/search?q=decision%20tree." title=" decision tree."> decision tree.</a> </p> <a href="https://publications.waset.org/10004544/performance-comparison-of-adtree-and-naive-bayes-algorithms-for-spam-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10004544/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10004544/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10004544/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10004544/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10004544/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10004544/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10004544/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10004544/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10004544/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10004544/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10004544.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1500</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7471</span> Random Access in IoT Using Na茂ve Bayes Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Alhusein%20Almahjoub">Alhusein Almahjoub</a>, <a href="https://publications.waset.org/search?q=Dongyu%20Qiu"> Dongyu Qiu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper deals with the random access procedure in next-generation networks and presents the solution to reduce total service time (TST) which is one of the most important performance metrics in current and future internet of things (IoT) based networks. The proposed solution focuses on the calculation of optimal transmission probability which maximizes the success probability and reduces TST. It uses the information of several idle preambles in every time slot, and based on it, it estimates the number of backlogged IoT devices using Naïve Bayes estimation which is a type of supervised learning in the machine learning domain. The estimation of backlogged devices is necessary since optimal transmission probability depends on it and the eNodeB does not have information about it. The simulations are carried out in MATLAB which verify that the proposed solution gives excellent performance.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Random%20access" title="Random access">Random access</a>, <a href="https://publications.waset.org/search?q=LTE%2FLTE-A" title=" LTE/LTE-A"> LTE/LTE-A</a>, <a href="https://publications.waset.org/search?q=5G" title=" 5G"> 5G</a>, <a href="https://publications.waset.org/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/search?q=Na%C3%AFve%20Bayes%20estimation." title=" Na茂ve Bayes estimation."> Na茂ve Bayes estimation.</a> </p> <a href="https://publications.waset.org/10012016/random-access-in-iot-using-naive-bayes-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012016/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012016/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012016/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012016/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012016/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012016/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012016/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012016/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012016/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012016/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012016.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">448</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7470</span> DWT Based Image Steganalysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Indradip%20Banerjee">Indradip Banerjee</a>, <a href="https://publications.waset.org/search?q=Souvik%20Bhattacharyya"> Souvik Bhattacharyya</a>, <a href="https://publications.waset.org/search?q=Gautam%20Sanyal"> Gautam Sanyal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>‘Steganalysis’ is one of the challenging and attractive interests for the researchers with the development of information hiding techniques. It is the procedure to detect the hidden information from the stego created by known steganographic algorithm. In this paper, a novel feature based image steganalysis technique is proposed. Various statistical moments have been used along with some similarity metric. The proposed steganalysis technique has been designed based on transformation in four wavelet domains, which include Haar, Daubechies, Symlets and Biorthogonal. Each domain is being subjected to various classifiers, namely K-nearest-neighbor, K* Classifier, Locally weighted learning, Naive Bayes classifier, Neural networks, Decision trees and Support vector machines. The experiments are performed on a large set of pictures which are available freely in image database. The system also predicts the different message length definitions.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Steganalysis" title="Steganalysis">Steganalysis</a>, <a href="https://publications.waset.org/search?q=Moments" title=" Moments"> Moments</a>, <a href="https://publications.waset.org/search?q=Wavelet%20Domain" title=" Wavelet Domain"> Wavelet Domain</a>, <a href="https://publications.waset.org/search?q=KNN" title=" KNN"> KNN</a>, <a href="https://publications.waset.org/search?q=K%2A" title=" K*"> K*</a>, <a href="https://publications.waset.org/search?q=LWL" title=" LWL"> LWL</a>, <a href="https://publications.waset.org/search?q=Naive%20Bayes%20Classifier" title=" Naive Bayes Classifier"> Naive Bayes Classifier</a>, <a href="https://publications.waset.org/search?q=Neural%20networks" title=" Neural networks"> Neural networks</a>, <a href="https://publications.waset.org/search?q=Decision%20trees" title=" Decision trees"> Decision trees</a>, <a href="https://publications.waset.org/search?q=SVM." title=" SVM. "> SVM. </a> </p> <a href="https://publications.waset.org/9999449/dwt-based-image-steganalysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9999449/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9999449/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9999449/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9999449/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9999449/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9999449/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9999449/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9999449/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9999449/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9999449/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9999449.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2572</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7469</span> Predicting Application Layer DDoS Attacks Using Machine Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=S.%20Umarani">S. Umarani</a>, <a href="https://publications.waset.org/search?q=D.%20Sharmila"> D. Sharmila</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>A Distributed Denial of Service (DDoS) attack is a major threat to cyber security. It originates from the network layer or the application layer of compromised/attacker systems which are connected to the network. The impact of this attack ranges from the simple inconvenience to use a particular service to causing major failures at the targeted server. When there is heavy traffic flow to a target server, it is necessary to classify the legitimate access and attacks. In this paper, a novel method is proposed to detect DDoS attacks from the traces of traffic flow. An access matrix is created from the traces. As the access matrix is multi dimensional, Principle Component Analysis (PCA) is used to reduce the attributes used for detection. Two classifiers Naive Bayes and K-Nearest neighborhood are used to classify the traffic as normal or abnormal. The performance of the classifier with PCA selected attributes and actual attributes of access matrix is compared by the detection rate and False Positive Rate (FPR).</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Distributed%20Denial%20of%20Service%20%28DDoS%29%20attack" title="Distributed Denial of Service (DDoS) attack">Distributed Denial of Service (DDoS) attack</a>, <a href="https://publications.waset.org/search?q=Application%20layer%20DDoS" title=" Application layer DDoS"> Application layer DDoS</a>, <a href="https://publications.waset.org/search?q=DDoS%20Detection" title=" DDoS Detection"> DDoS Detection</a>, <a href="https://publications.waset.org/search?q=K-%20Nearest%20neighborhood%0D%0Aclassifier" title=" K- Nearest neighborhood classifier"> K- Nearest neighborhood classifier</a>, <a href="https://publications.waset.org/search?q=Naive%20Bayes%20Classifier" title=" Naive Bayes Classifier"> Naive Bayes Classifier</a>, <a href="https://publications.waset.org/search?q=Principle%20Component%20Analysis." title=" Principle Component Analysis."> Principle Component Analysis.</a> </p> <a href="https://publications.waset.org/10000388/predicting-application-layer-ddos-attacks-using-machine-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10000388/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10000388/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10000388/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10000388/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10000388/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10000388/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10000388/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10000388/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10000388/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10000388/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10000388.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">5279</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7468</span> A Content Vector Model for Text Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Eric%20Jiang">Eric Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a popular rank-reduced vector space approach, Latent Semantic Indexing (LSI) has been used in information retrieval and other applications. In this paper, an LSI-based content vector model for text classification is presented, which constructs multiple augmented category LSI spaces and classifies text by their content. The model integrates the class discriminative information from the training data and is equipped with several pertinent feature selection and text classification algorithms. The proposed classifier has been applied to email classification and its experiments on a benchmark spam testing corpus (PU1) have shown that the approach represents a competitive alternative to other email classifiers based on the well-known SVM and na茂ve Bayes algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Feature%20Selection" title="Feature Selection">Feature Selection</a>, <a href="https://publications.waset.org/search?q=Latent%20Semantic%20Indexing" title=" Latent Semantic Indexing"> Latent Semantic Indexing</a>, <a href="https://publications.waset.org/search?q=Text%20Classification" title="Text Classification">Text Classification</a>, <a href="https://publications.waset.org/search?q=Vector%20Space%20Model." title=" Vector Space Model."> Vector Space Model.</a> </p> <a href="https://publications.waset.org/11975/a-content-vector-model-for-text-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11975/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11975/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11975/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11975/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11975/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11975/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11975/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11975/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11975/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11975/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11975.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1885</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7467</span> On Best Estimation for Parameter Weibull Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hadeel%20Salim%20Alkutubi">Hadeel Salim Alkutubi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The objective of this study is to introduce estimators to the parameters and survival function for Weibull distribution using three different methods, Maximum Likelihood estimation, Standard Bayes estimation and Modified Bayes estimation. We will then compared the three methods using simulation study to find the best one base on MPE and MSE.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Maximum%20Likelihood%20estimation" title="Maximum Likelihood estimation ">Maximum Likelihood estimation </a>, <a href="https://publications.waset.org/search?q=Bayes%20estimation" title=" Bayes estimation"> Bayes estimation</a>, <a href="https://publications.waset.org/search?q=Jeffery%20prior%20information" title=" Jeffery prior information"> Jeffery prior information</a>, <a href="https://publications.waset.org/search?q=Simulation%20study" title=" Simulation study"> Simulation study</a> </p> <a href="https://publications.waset.org/6372/on-best-estimation-for-parameter-weibull-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/6372/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/6372/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/6372/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/6372/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/6372/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/6372/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/6372/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/6372/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/6372/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/6372/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/6372.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1266</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7466</span> Inference of Stress-Strength Model for a Lomax Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=H.%20Panahi">H. Panahi</a>, <a href="https://publications.waset.org/search?q=S.%20Asadi"> S. Asadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the estimation of the stress-strength parameter R = P(Y < X), when X and Y are independent and both are Lomax distributions with the common scale parameters but different shape parameters is studied. The maximum likelihood estimator of R is derived. Assuming that the common scale parameter is known, the bayes estimator and exact confidence interval of R are discussed. Simulation study to investigate performance of the different proposed methods has been carried out. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Stress-Strength%20model%3B%20maximum%20likelihoodestimator%3B%20Bayes%20estimator%3B%20Lomax%20distribution" title="Stress-Strength model; maximum likelihoodestimator; Bayes estimator; Lomax distribution">Stress-Strength model; maximum likelihoodestimator; Bayes estimator; Lomax distribution</a> </p> <a href="https://publications.waset.org/13042/inference-of-stress-strength-model-for-a-lomax-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13042/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13042/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13042/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13042/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13042/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13042/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13042/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13042/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13042/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13042/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13042.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1793</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7465</span> Inferences on Compound Rayleigh Parameters with Progressively Type-II Censored Samples</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Abdullah%20Y.%20Al-Hossain">Abdullah Y. Al-Hossain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper considers inference under progressive type II censoring with a compound Rayleigh failure time distribution. The maximum likelihood (ML), and Bayes methods are used for estimating the unknown parameters as well as some lifetime parameters, namely reliability and hazard functions. We obtained Bayes estimators using the conjugate priors for two shape and scale parameters. When the two parameters are unknown, the closed-form expressions of the Bayes estimators cannot be obtained. We use Lindley.s approximation to compute the Bayes estimates. Another Bayes estimator has been obtained based on continuous-discrete joint prior for the unknown parameters. An example with the real data is discussed to illustrate the proposed method. Finally, we made comparisons between these estimators and the maximum likelihood estimators using a Monte Carlo simulation study.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Progressive%20type%20II%20censoring" title="Progressive type II censoring">Progressive type II censoring</a>, <a href="https://publications.waset.org/search?q=compound%20Rayleigh%20failure%20time%20distribution" title=" compound Rayleigh failure time distribution"> compound Rayleigh failure time distribution</a>, <a href="https://publications.waset.org/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/search?q=Bayes%20estimation" title=" Bayes estimation"> Bayes estimation</a>, <a href="https://publications.waset.org/search?q=Lindley%27s%20approximation%20method" title=" Lindley's approximation method"> Lindley's approximation method</a>, <a href="https://publications.waset.org/search?q=Monte%20Carlo%20simulation." title=" Monte Carlo simulation."> Monte Carlo simulation.</a> </p> <a href="https://publications.waset.org/12659/inferences-on-compound-rayleigh-parameters-with-progressively-type-ii-censored-samples" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/12659/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/12659/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/12659/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/12659/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/12659/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/12659/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/12659/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/12659/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/12659/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/12659/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/12659.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2390</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7464</span> Evaluation of Classifiers Based On I2C Distance for Action Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Lei%20Zhang">Lei Zhang</a>, <a href="https://publications.waset.org/search?q=Tao%20Wang"> Tao Wang</a>, <a href="https://publications.waset.org/search?q=Xiantong%20Zhen"> Xiantong Zhen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Naive Bayes Nearest Neighbor (NBNN) and its variants, i,e., local NBNN and the NBNN kernels, are local feature-based classifiers that have achieved impressive performance in image classification. By exploiting instance-to-class (I2C) distances (instance means image/video in image/video classification), they avoid quantization errors of local image descriptors in the bag of words (BoW) model. However, the performances of NBNN, local NBNN and the NBNN kernels have not been validated on video analysis. In this paper, we introduce these three classifiers into human action recognition and conduct comprehensive experiments on the benchmark KTH and the realistic HMDB datasets. The results shows that those I2C based classifiers consistently outperform the SVM classifier with the BoW model.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Instance-to-class%20distance" title="Instance-to-class distance">Instance-to-class distance</a>, <a href="https://publications.waset.org/search?q=NBNN" title=" NBNN"> NBNN</a>, <a href="https://publications.waset.org/search?q=Local%20NBNN" title=" Local NBNN"> Local NBNN</a>, <a href="https://publications.waset.org/search?q=NBNN%20kernel." title=" NBNN kernel."> NBNN kernel.</a> </p> <a href="https://publications.waset.org/12474/evaluation-of-classifiers-based-on-i2c-distance-for-action-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/12474/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/12474/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/12474/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/12474/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/12474/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/12474/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/12474/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/12474/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/12474/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/12474/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/12474.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1659</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7463</span> Scaling up Detection Rates and Reducing False Positives in Intrusion Detection using NBTree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Dewan%20Md.%20Farid">Dewan Md. Farid</a>, <a href="https://publications.waset.org/search?q=Nguyen%20Huu%20Hoa"> Nguyen Huu Hoa</a>, <a href="https://publications.waset.org/search?q=Jerome%20Darmont"> Jerome Darmont</a>, <a href="https://publications.waset.org/search?q=Nouria%20Harbi"> Nouria Harbi</a>, <a href="https://publications.waset.org/search?q=Mohammad%20Zahidur%20Rahman"> Mohammad Zahidur Rahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a new learning algorithm for anomaly based network intrusion detection using improved self adaptive na茂ve Bayesian tree (NBTree), which induces a hybrid of decision tree and na茂ve Bayesian classifier. The proposed approach scales up the balance detections for different attack types and keeps the false positives at acceptable level in intrusion detection. In complex and dynamic large intrusion detection dataset, the detection accuracy of na茂ve Bayesian classifier does not scale up as well as decision tree. It has been successfully tested in other problem domains that na茂ve Bayesian tree improves the classification rates in large dataset. In na茂ve Bayesian tree nodes contain and split as regular decision-trees, but the leaves contain na茂ve Bayesian classifiers. The experimental results on KDD99 benchmark network intrusion detection dataset demonstrate that this new approach scales up the detection rates for different attack types and reduces false positives in network intrusion detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Detection%20rates" title="Detection rates">Detection rates</a>, <a href="https://publications.waset.org/search?q=false%20positives" title=" false positives"> false positives</a>, <a href="https://publications.waset.org/search?q=network%20intrusiondetection" title=" network intrusiondetection"> network intrusiondetection</a>, <a href="https://publications.waset.org/search?q=na%C3%AFve%20Bayesian%20tree." title=" na茂ve Bayesian tree."> na茂ve Bayesian tree.</a> </p> <a href="https://publications.waset.org/1750/scaling-up-detection-rates-and-reducing-false-positives-in-intrusion-detection-using-nbtree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1750/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1750/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1750/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1750/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1750/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1750/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1750/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1750/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1750/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1750/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1750.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2281</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7462</span> An Estimating Parameter of the Mean in Normal Distribution by Maximum Likelihood, Bayes, and Markov Chain Monte Carlo Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Autcha%20Araveeporn">Autcha Araveeporn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper is to compare the parameter estimation of the mean in normal distribution by Maximum Likelihood (ML), Bayes, and Markov Chain Monte Carlo (MCMC) methods. The ML estimator is estimated by the average of data, the Bayes method is considered from the prior distribution to estimate Bayes estimator, and MCMC estimator is approximated by Gibbs sampling from posterior distribution. These methods are also to estimate a parameter then the hypothesis testing is used to check a robustness of the estimators. Data are simulated from normal distribution with the true parameter of mean 2, and variance 4, 9, and 16 when the sample sizes is set as 10, 20, 30, and 50. From the results, it can be seen that the estimation of MLE, and MCMC are perceivably different from the true parameter when the sample size is 10 and 20 with variance 16. Furthermore, the Bayes estimator is estimated from the prior distribution when mean is 1, and variance is 12 which showed the significant difference in mean with variance 9 at the sample size 10 and 20.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Bayes%20method" title="Bayes method">Bayes method</a>, <a href="https://publications.waset.org/search?q=Markov%20Chain%20Monte%20Carlo%20method" title=" Markov Chain Monte Carlo method"> Markov Chain Monte Carlo method</a>, <a href="https://publications.waset.org/search?q=Maximum%20Likelihood%20method" title=" Maximum Likelihood method"> Maximum Likelihood method</a>, <a href="https://publications.waset.org/search?q=normal%20distribution." title=" normal distribution."> normal distribution.</a> </p> <a href="https://publications.waset.org/10005322/an-estimating-parameter-of-the-mean-in-normal-distribution-by-maximum-likelihood-bayes-and-markov-chain-monte-carlo-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10005322/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10005322/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10005322/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10005322/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10005322/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10005322/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10005322/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10005322/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10005322/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10005322/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10005322.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1435</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7461</span> Segmentation of Piecewise Polynomial Regression Model by Using Reversible Jump MCMC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Suparman">Suparman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Piecewise polynomial regression model is very flexible model for modeling the data. If the piecewise polynomial regression model is matched against the data, its parameters are not generally known. This paper studies the parameter estimation problem of piecewise polynomial regression model. The method which is used to estimate the parameters of the piecewise polynomial regression model is Bayesian method. Unfortunately, the Bayes estimator cannot be found analytically. Reversible jump MCMC algorithm is proposed to solve this problem. Reversible jump MCMC algorithm generates the Markov chain that converges to the limit distribution of the posterior distribution of piecewise polynomial regression model parameter. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of piecewise polynomial regression model.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Piecewise" title="Piecewise">Piecewise</a>, <a href="https://publications.waset.org/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/search?q=reversible%20jump%20MCMC" title=" reversible jump MCMC"> reversible jump MCMC</a>, <a href="https://publications.waset.org/search?q=segmentation." title=" segmentation."> segmentation.</a> </p> <a href="https://publications.waset.org/10004307/segmentation-of-piecewise-polynomial-regression-model-by-using-reversible-jump-mcmc-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10004307/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10004307/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10004307/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10004307/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10004307/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10004307/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10004307/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10004307/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10004307/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10004307/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10004307.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1668</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7460</span> Elimination Noise by Adaptive Wavelet Threshold</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Iman%20Elyasi">Iman Elyasi</a>, <a href="https://publications.waset.org/search?q=Sadegh%20Zarmehi"> Sadegh Zarmehi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Due to some reasons, observed images are degraded which are mainly caused by noise. Recently image denoising using the wavelet transform has been attracting much attention. Waveletbased approach provides a particularly useful method for image denoising when the preservation of edges in the scene is of importance because the local adaptivity is based explicitly on the values of the wavelet detail coefficients. In this paper, we propose several methods of noise removal from degraded images with Gaussian noise by using adaptive wavelet threshold (Bayes Shrink, Modified Bayes Shrink and Normal Shrink). The proposed thresholds are simple and adaptive to each subband because the parameters required for estimating the threshold depend on subband data. Experimental results show that the proposed thresholds remove noise significantly and preserve the edges in the scene.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Image%20denoising" title="Image denoising">Image denoising</a>, <a href="https://publications.waset.org/search?q=Bayes%20Shrink" title=" Bayes Shrink"> Bayes Shrink</a>, <a href="https://publications.waset.org/search?q=Modified%20Bayes%20Shrink" title=" Modified Bayes Shrink"> Modified Bayes Shrink</a>, <a href="https://publications.waset.org/search?q=Normal%20Shrink." title=" Normal Shrink."> Normal Shrink.</a> </p> <a href="https://publications.waset.org/8882/elimination-noise-by-adaptive-wavelet-threshold" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8882/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8882/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8882/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8882/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8882/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8882/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8882/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8882/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8882/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8882/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8882.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2473</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7459</span> Estimation of R= P [Y < X] for Two-parameter Burr Type XII Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=H.Panahi">H.Panahi</a>, <a href="https://publications.waset.org/search?q=S.Asadi"> S.Asadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this article, we consider the estimation of P[Y < X], when strength, X and stress, Y are two independent variables of Burr Type XII distribution. The MLE of the R based on one simple iterative procedure is obtained. Assuming that the common parameter is known, the maximum likelihood estimator, uniformly minimum variance unbiased estimator and Bayes estimator of P[Y < X] are discussed. The exact confidence interval of the R is also obtained. Monte Carlo simulations are performed to compare the different proposed methods.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Stress-Strength%20model" title="Stress-Strength model">Stress-Strength model</a>, <a href="https://publications.waset.org/search?q=Maximum%20likelihood%20estimator" title=" Maximum likelihood estimator"> Maximum likelihood estimator</a>, <a href="https://publications.waset.org/search?q=Bayes%20estimator" title=" Bayes estimator"> Bayes estimator</a>, <a href="https://publications.waset.org/search?q=Burr%20type%20XII%20distribution." title=" Burr type XII distribution."> Burr type XII distribution.</a> </p> <a href="https://publications.waset.org/15474/estimation-of-r-p-y-x-for-two-parameter-burr-type-xii-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15474/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15474/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15474/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15474/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15474/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15474/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15474/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15474/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15474/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15474/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15474.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2296</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7458</span> Detecting Email Forgery using Random Forests and Na茂ve Bayes Classifiers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Emad%20E%20Abdallah">Emad E Abdallah</a>, <a href="https://publications.waset.org/search?q=A.F.%20Otoom"> A.F. Otoom</a>, <a href="https://publications.waset.org/search?q=ArwaSaqer"> ArwaSaqer</a>, <a href="https://publications.waset.org/search?q=Ola%20Abu-Aisheh"> Ola Abu-Aisheh</a>, <a href="https://publications.waset.org/search?q=Diana%20Omari"> Diana Omari</a>, <a href="https://publications.waset.org/search?q=Ghadeer%20Salem"> Ghadeer Salem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As emails communications have no consistent authentication procedure to ensure the authenticity, we present an investigation analysis approach for detecting forged emails based on Random Forests and Na茂ve Bays classifiers. Instead of investigating the email headers, we use the body content to extract a unique writing style for all the possible suspects. Our approach consists of four main steps: (1) The cybercrime investigator extract different effective features including structural, lexical, linguistic, and syntactic evidence from previous emails for all the possible suspects, (2) The extracted features vectors are normalized to increase the accuracy rate. (3) The normalized features are then used to train the learning engine, (4) upon receiving the anonymous email (M); we apply the feature extraction process to produce a feature vector. Finally, using the machine learning classifiers the email is assigned to one of the suspects- whose writing style closely matches M. Experimental results on real data sets show the improved performance of the proposed method and the ability of identifying the authors with a very limited number of features. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Digital%20investigation" title="Digital investigation">Digital investigation</a>, <a href="https://publications.waset.org/search?q=cybercrimes" title=" cybercrimes"> cybercrimes</a>, <a href="https://publications.waset.org/search?q=emails%20forensics" title=" emails forensics"> emails forensics</a>, <a href="https://publications.waset.org/search?q=anonymous%20emails" title=" anonymous emails"> anonymous emails</a>, <a href="https://publications.waset.org/search?q=writing%20style" title=" writing style"> writing style</a>, <a href="https://publications.waset.org/search?q=and%20authorship%20analysis" title=" and authorship analysis"> and authorship analysis</a> </p> <a href="https://publications.waset.org/3339/detecting-email-forgery-using-random-forests-and-naive-bayes-classifiers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3339/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3339/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3339/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3339/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3339/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3339/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3339/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3339/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3339/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3339/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3339.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">5254</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7457</span> Statistical Measures and Optimization Algorithms for Gene Selection in Lung and Ovarian Tumor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=C.%20Gunavathi">C. Gunavathi</a>, <a href="https://publications.waset.org/search?q=K.%20Premalatha"> K. Premalatha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Microarray technology is universally used in the study of disease diagnosis using gene expression levels. The main shortcoming of gene expression data is that it includes thousands of genes and a small number of samples. Abundant methods and techniques have been proposed for tumor classification using microarray gene expression data. Feature or gene selection methods can be used to mine the genes that directly involve in the classification and to eliminate irrelevant genes. In this paper statistical measures like T-Statistics, Signal-to-Noise Ratio (SNR) and F-Statistics are used to rank the genes. The ranked genes are used for further classification. Particle Swarm Optimization (PSO) algorithm and Shuffled Frog Leaping (SFL) algorithm are used to find the significant genes from the top-m ranked genes. The Naïve Bayes Classifier (NBC) is used to classify the samples based on the significant genes. The proposed work is applied on Lung and Ovarian datasets. The experimental results show that the proposed method achieves 100% accuracy in all the three datasets and the results are compared with previous works.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Microarray" title="Microarray">Microarray</a>, <a href="https://publications.waset.org/search?q=T-Statistics" title=" T-Statistics"> T-Statistics</a>, <a href="https://publications.waset.org/search?q=Signal-to-Noise%20Ratio" title=" Signal-to-Noise Ratio"> Signal-to-Noise Ratio</a>, <a href="https://publications.waset.org/search?q=FStatistics" title=" FStatistics"> FStatistics</a>, <a href="https://publications.waset.org/search?q=Particle%20Swarm%20Optimization" title=" Particle Swarm Optimization"> Particle Swarm Optimization</a>, <a href="https://publications.waset.org/search?q=Shuffled%20Frog%20Leaping" title=" Shuffled Frog Leaping"> Shuffled Frog Leaping</a>, <a href="https://publications.waset.org/search?q=Na%C3%AFve%20Bayes%20Classifier." title=" Na茂ve Bayes Classifier."> Na茂ve Bayes Classifier.</a> </p> <a href="https://publications.waset.org/9999853/statistical-measures-and-optimization-algorithms-for-gene-selection-in-lung-and-ovarian-tumor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9999853/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9999853/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9999853/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9999853/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9999853/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9999853/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9999853/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9999853/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9999853/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9999853/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9999853.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1945</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7456</span> The Performance of Predictive Classification Using Empirical Bayes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=N.%20Deetae">N. Deetae</a>, <a href="https://publications.waset.org/search?q=S.%20Sukparungsee"> S. Sukparungsee</a>, <a href="https://publications.waset.org/search?q=Y.%20Areepong"> Y. Areepong</a>, <a href="https://publications.waset.org/search?q=K.%20Jampachaisri"> K. Jampachaisri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This research is aimed to compare the percentages of correct classification of Empirical Bayes method (EB) to Classical method when data are constructed as near normal, short-tailed and long-tailed symmetric, short-tailed and long-tailed asymmetric. The study is performed using conjugate prior, normal distribution with known mean and unknown variance. The estimated hyper-parameters obtained from EB method are replaced in the posterior predictive probability and used to predict new observations. Data are generated, consisting of training set and test set with the sample sizes 100, 200 and 500 for the binary classification. The results showed that EB method exhibited an improved performance over Classical method in all situations under study.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Classification" title="Classification">Classification</a>, <a href="https://publications.waset.org/search?q=Empirical%20Bayes" title=" Empirical Bayes"> Empirical Bayes</a>, <a href="https://publications.waset.org/search?q=Posterior%20predictive%20probability." title=" Posterior predictive probability."> Posterior predictive probability.</a> </p> <a href="https://publications.waset.org/2822/the-performance-of-predictive-classification-using-empirical-bayes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/2822/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/2822/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/2822/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/2822/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/2822/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/2822/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/2822/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/2822/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/2822/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/2822/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/2822.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1597</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7455</span> Improving Classification Accuracy with Discretization on Datasets Including Continuous Valued Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mehmet%20Hacibeyoglu">Mehmet Hacibeyoglu</a>, <a href="https://publications.waset.org/search?q=Ahmet%20Arslan"> Ahmet Arslan</a>, <a href="https://publications.waset.org/search?q=Sirzat%20Kahramanli"> Sirzat Kahramanli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study analyzes the effect of discretization on classification of datasets including continuous valued features. Six datasets from UCI which containing continuous valued features are discretized with entropy-based discretization method. The performance improvement between the dataset with original features and the dataset with discretized features is compared with k-nearest neighbors, Naive Bayes, C4.5 and CN2 data mining classification algorithms. As the result the classification accuracies of the six datasets are improved averagely by 1.71% to 12.31%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Data%20mining%20classification%20algorithms" title="Data mining classification algorithms">Data mining classification algorithms</a>, <a href="https://publications.waset.org/search?q=entropy-baseddiscretization%20method" title=" entropy-baseddiscretization method"> entropy-baseddiscretization method</a> </p> <a href="https://publications.waset.org/1314/improving-classification-accuracy-with-discretization-on-datasets-including-continuous-valued-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1314/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1314/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1314/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1314/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1314/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1314/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1314/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1314/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1314/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1314/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1314.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2461</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7454</span> Contextual Sentiment Analysis with Untrained Annotators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Lucas%20A.%20Silva">Lucas A. Silva</a>, <a href="https://publications.waset.org/search?q=Carla%20R.%20Aguiar"> Carla R. Aguiar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This work presents a proposal to perform contextual sentiment analysis using a supervised learning algorithm and disregarding the extensive training of annotators. To achieve this goal, a web platform was developed to perform the entire procedure outlined in this paper. The main contribution of the pipeline described in this article is to simplify and automate the annotation process through a system of analysis of congruence between the notes. This ensured satisfactory results even without using specialized annotators in the context of the research, avoiding the generation of biased training data for the classifiers. For this, a case study was conducted in a blog of entrepreneurship. The experimental results were consistent with the literature related annotation using formalized process with experts.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Contextualized%20classifier" title="Contextualized classifier">Contextualized classifier</a>, <a href="https://publications.waset.org/search?q=na%C3%AFve%20Bayes" title=" na茂ve Bayes"> na茂ve Bayes</a>, <a href="https://publications.waset.org/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/search?q=untrained%20annotators." title=" untrained annotators. "> untrained annotators. </a> </p> <a href="https://publications.waset.org/9997689/contextual-sentiment-analysis-with-untrained-annotators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9997689/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9997689/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9997689/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9997689/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9997689/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9997689/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9997689/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9997689/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9997689/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9997689/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9997689.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">4703</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7453</span> Cross Project Software Fault Prediction at Design Phase</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Pradeep%20Singh">Pradeep Singh</a>, <a href="https://publications.waset.org/search?q=Shrish%20Verma"> Shrish Verma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. Earlier we predicted the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Na茂ve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven datasets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Software%20Metrics" title="Software Metrics">Software Metrics</a>, <a href="https://publications.waset.org/search?q=Fault%20prediction" title=" Fault prediction"> Fault prediction</a>, <a href="https://publications.waset.org/search?q=Cross%20project" title=" Cross project"> Cross project</a>, <a href="https://publications.waset.org/search?q=Within%20project." title=" Within project."> Within project.</a> </p> <a href="https://publications.waset.org/10001596/cross-project-software-fault-prediction-at-design-phase" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10001596/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10001596/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10001596/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10001596/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10001596/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10001596/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10001596/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10001596/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10001596/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10001596/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10001596.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2546</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7452</span> Predicting Protein-Protein Interactions from Protein Sequences Using Phylogenetic Profiles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Omer%20Nebil%20Yaveroglu">Omer Nebil Yaveroglu</a>, <a href="https://publications.waset.org/search?q=Tolga%20Can"> Tolga Can</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a high accuracy protein-protein interaction prediction method is developed. The importance of the proposed method is that it only uses sequence information of proteins while predicting interaction. The method extracts phylogenetic profiles of proteins by using their sequence information. Combining the phylogenetic profiles of two proteins by checking existence of homologs in different species and fitting this combined profile into a statistical model, it is possible to make predictions about the interaction status of two proteins. For this purpose, we apply a collection of pattern recognition techniques on the dataset of combined phylogenetic profiles of protein pairs. Support Vector Machines, Feature Extraction using ReliefF, Naive Bayes Classification, K-Nearest Neighborhood Classification, Decision Trees, and Random Forest Classification are the methods we applied for finding the classification method that best predicts the interaction status of protein pairs. Random Forest Classification outperformed all other methods with a prediction accuracy of 76.93% <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Protein%20Interaction%20Prediction" title="Protein Interaction Prediction">Protein Interaction Prediction</a>, <a href="https://publications.waset.org/search?q=Phylogenetic%20Profile" title=" Phylogenetic Profile"> Phylogenetic Profile</a>, <a href="https://publications.waset.org/search?q=SVM" title=" SVM "> SVM </a>, <a href="https://publications.waset.org/search?q=ReliefF" title=" ReliefF"> ReliefF</a>, <a href="https://publications.waset.org/search?q=Decision%20Trees" title=" Decision Trees"> Decision Trees</a>, <a href="https://publications.waset.org/search?q=Random%20Forest%20Classification" title=" Random Forest Classification"> Random Forest Classification</a> </p> <a href="https://publications.waset.org/11139/predicting-protein-protein-interactions-from-protein-sequences-using-phylogenetic-profiles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11139/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11139/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11139/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11139/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11139/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11139/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11139/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11139/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11139/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11139/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11139.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1613</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7451</span> Prediction of MicroRNA-Target Gene by Machine Learning Algorithms in Lung Cancer Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Nilubon%20Kurubanjerdjit">Nilubon Kurubanjerdjit</a>, <a href="https://publications.waset.org/search?q=Nattakarn%20Iam-On"> Nattakarn Iam-On</a>, <a href="https://publications.waset.org/search?q=Ka-Lok%20Ng"> Ka-Lok Ng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> MicroRNAs are small non-coding RNA found in many different species. They play crucial roles in cancer such as biological processes of apoptosis and proliferation. The identification of microRNA-target genes can be an essential first step towards to reveal the role of microRNA in various cancer types. In this paper, we predict miRNA-target genes for lung cancer by integrating prediction scores from miRanda and PITA algorithms used as a feature vector of miRNA-target interaction. Then, machine-learning algorithms were implemented for making a final prediction. The approach developed in this study should be of value for future studies into understanding the role of miRNAs in molecular mechanisms enabling lung cancer formation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=MicroRNA" title="MicroRNA">MicroRNA</a>, <a href="https://publications.waset.org/search?q=miRNAs" title=" miRNAs"> miRNAs</a>, <a href="https://publications.waset.org/search?q=lung%20cancer" title=" lung cancer"> lung cancer</a>, <a href="https://publications.waset.org/search?q=machine%0D%0Alearning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/search?q=Na%C3%AFve%20Bayes" title=" Na茂ve Bayes"> Na茂ve Bayes</a>, <a href="https://publications.waset.org/search?q=SVM." title=" SVM."> SVM.</a> </p> <a href="https://publications.waset.org/10003520/prediction-of-microrna-target-gene-by-machine-learning-algorithms-in-lung-cancer-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10003520/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10003520/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10003520/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10003520/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10003520/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10003520/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10003520/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10003520/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10003520/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10003520/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10003520.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2387</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7450</span> Combining ILP with Semi-supervised Learning for Web Page Categorization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Nuanwan%20Soonthornphisaj">Nuanwan Soonthornphisaj</a>, <a href="https://publications.waset.org/search?q=Boonserm%20Kijsirikul"> Boonserm Kijsirikul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper presents a semi-supervised learning algorithm called Iterative-Cross Training (ICT) to solve the Web pages classification problems. We apply Inductive logic programming (ILP) as a strong learner in ICT. The objective of this research is to evaluate the potential of the strong learner in order to boost the performance of the weak learner of ICT. We compare the result with the supervised Naive Bayes, which is the well-known algorithm for the text classification problem. The performance of our learning algorithm is also compare with other semi-supervised learning algorithms which are Co-Training and EM. The experimental results show that ICT algorithm outperforms those algorithms and the performance of the weak learner can be enhanced by ILP system.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Inductive%20Logic%20Programming" title="Inductive Logic Programming">Inductive Logic Programming</a>, <a href="https://publications.waset.org/search?q=Semi-supervisedLearning" title=" Semi-supervisedLearning"> Semi-supervisedLearning</a>, <a href="https://publications.waset.org/search?q=Web%20Page%20Categorization" title=" Web Page Categorization"> Web Page Categorization</a> </p> <a href="https://publications.waset.org/2918/combining-ilp-with-semi-supervised-learning-for-web-page-categorization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/2918/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/2918/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/2918/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/2918/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/2918/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/2918/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/2918/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/2918/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/2918/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/2918/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/2918.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1644</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7449</span> Bayesian Inference for Phase Unwrapping Using Conjugate Gradient Method in One and Two Dimensions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yohei%20Saika">Yohei Saika</a>, <a href="https://publications.waset.org/search?q=Hiroki%20Sakaematsu"> Hiroki Sakaematsu</a>, <a href="https://publications.waset.org/search?q=Shota%20Akiyama"> Shota Akiyama</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>We investigated statistical performance of Bayesian inference using maximum entropy and MAP estimation for several models which approximated wave-fronts in remote sensing using SAR interferometry. Using Monte Carlo simulation for a set of wave-fronts generated by assumed true prior, we found that the method of maximum entropy realized the optimal performance around the Bayes-optimal conditions by using model of the true prior and the likelihood representing optical measurement due to the interferometer. Also, we found that the MAP estimation regarded as a deterministic limit of maximum entropy almost achieved the same performance as the Bayes-optimal solution for the set of wave-fronts. Then, we clarified that the MAP estimation perfectly carried out phase unwrapping without using prior information, and also that the MAP estimation realized accurate phase unwrapping using conjugate gradient (CG) method, if we assumed the model of the true prior appropriately.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Bayesian%20inference%20using%20maximum%20entropy" title="Bayesian inference using maximum entropy">Bayesian inference using maximum entropy</a>, <a href="https://publications.waset.org/search?q=MAP%0D%0Aestimation%20using%20conjugate%20gradient%20method" title=" MAP estimation using conjugate gradient method"> MAP estimation using conjugate gradient method</a>, <a href="https://publications.waset.org/search?q=SAR%20interferometry." title=" SAR interferometry."> SAR interferometry.</a> </p> <a href="https://publications.waset.org/9598/bayesian-inference-for-phase-unwrapping-using-conjugate-gradient-method-in-one-and-two-dimensions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9598/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9598/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9598/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9598/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9598/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9598/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9598/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9598/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9598/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9598/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9598.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1751</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7448</span> Automatic Microaneurysm Quantification for Diabetic Retinopathy Screening</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=A.%20Sopharak">A. Sopharak</a>, <a href="https://publications.waset.org/search?q=B.%20Uyyanonvara"> B. Uyyanonvara</a>, <a href="https://publications.waset.org/search?q=S.%20Barman"> S. Barman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Microaneurysm is a key indicator of diabetic retinopathy that can potentially cause damage to retina. Early detection and automatic quantification are the keys to prevent further damage. In this paper, which focuses on automatic microaneurysm detection in images acquired through non-dilated pupils, we present a series of experiments on feature selection and automatic microaneurysm pixel classification. We found that the best feature set is a combination of 10 features: the pixel-s intensity of shade corrected image, the pixel hue, the standard deviation of shade corrected image, DoG4, the area of the candidate MA, the perimeter of the candidate MA, the eccentricity of the candidate MA, the circularity of the candidate MA, the mean intensity of the candidate MA on shade corrected image and the ratio of the major axis length and minor length of the candidate MA. The overall sensitivity, specificity, precision, and accuracy are 84.82%, 99.99%, 89.01%, and 99.99%, respectively.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Diabetic%20retinopathy" title="Diabetic retinopathy">Diabetic retinopathy</a>, <a href="https://publications.waset.org/search?q=microaneurysm" title=" microaneurysm"> microaneurysm</a>, <a href="https://publications.waset.org/search?q=naive%20Bayes%20classifier" title=" naive Bayes classifier"> naive Bayes classifier</a> </p> <a href="https://publications.waset.org/15211/automatic-microaneurysm-quantification-for-diabetic-retinopathy-screening" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15211/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15211/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a 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