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Knowledge extraction using probabilistic reasoning: An artificial neural network approach

Chelsea Dobbins, Paul Fergus
2015 2015 International Joint Conference on Neural Networks (IJCNN)  
The results illustrate that the Voted Perceptron classifier (VPC), perceptron linear classifier (PERLC) and random neural network classifier (RNNC) performed particularly well, with accuracies of 100%,  ...  Using a number of artificial neural network algorithms, a successfully developed prototype has been developed that demonstrates the applicability of the approach.  ...  Matrix for RDF information on a Jaguar Fig. 4 . 4 Scree Plot Forward Neural Network Classifier by Back Propagation (BPXNC), Feed Forward Neural Network by Levenberg-Marquardt Rule Classifier (LMNC)  ... 
doi:10.1109/ijcnn.2015.7280526 dblp:conf/ijcnn/DobbinsF15 fatcat:gudlsvrnfnazzjn6aacllvihfq

Comparison of Machine Learning Algorithms to Classify Web Pages

Ansam A.
2017 International Journal of Advanced Computer Science and Applications  
We use machine learning algorithms 'Artificial Neural Networks (ANN)', 'Random Forest (RF)', 'AdaBoost' to perform a behavior comparison on the web pages classifications problem.  ...  Many researchers focus on the issue of web pages classification technology that provides high accuracy.  ...  The feed forwardback propagation neural network is adapted as the classifiers.  ... 
doi:10.14569/ijacsa.2017.081127 fatcat:alum75ufprckbahfhdhpxprjgi

A Model to Detect Phishing Websites using Support Vector Classifier and a Deep Neural Network Algorithm

P.S. Ezekiel, O. E Taylor, F. B. Deedam- Okuchaba
2020 IJARCCE  
This paper presents a model in detecting phishing websites using support vector classifier and a deep neural network algorithm.  ...  This paper presents a model to detect phishing websites using support vector classifier and a deep neural network algorithm.  ...  The first layer builds a topic model using Probabilistic Latent Semantic Analysis (PLSA), The second layer builds a vigorous classifier using AdaBoost and the third layer employs a classifier from both  ... 
doi:10.17148/ijarcce.2020.9632 fatcat:nfqmpxgo2zcupj5j45pquu4hqa

Text Content Analysis For Illicit Web Pages By Using Neural Networks

Zhi Sam Lee, Mohd Aizaini Maarof, Ali Selamat, Siti Mariyam Shamsuddin
2009 Jurnal Teknologi  
In this paper, we have proposed a textual content analysis model using entropy term weighting scheme to classify pornography and sex education web pages.  ...  Those techniques have been tested with artificial neural network using small class dataset.  ...  The number of output layers (N) is one since the network only classifies the web pages to two classes.  ... 
doi:10.11113/jt.v50.168 fatcat:gaqzmph5wrbpvb2l5an6vizhpy

HYBRID PARTICLE SWARM OPTIMIZATION MULTI LAYER PERCEPTRON FOR WEB-SERVICES CLASSIFICATION

A Syed Mustafa, Y.S Kumaraswamy
2016 International Journal on Information Sciences and Computing  
The Web services are applications that perform specific tasks and are accessible via the network through a communication protocol.  ...  Multi-layer perceptron neural network (MLP) is the most popular and widely used nonlinear network for solving many practical problems in applied science, biology, and engineering.  ...  The problem with web page classification is split into multiple sub-problems like functional classification and other types. Subject classification is about a subject or topic of a web page.  ... 
doi:10.18000/ijisac.50160 fatcat:k3z7abearzg77o2jerv5j4olmq

Credibility Microscope: Relating Web Page Credibility Evaluations to Their Textual Content

Wojciech Jaworski, Emilia Rejmund, Adam Wierzbicki
2014 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)  
Therefore, evaluation of credibility of web page content, especially while making important decisions like those concerning health care, medical information, and large purchases, is crucial, but users  ...  often lack a necessary knowledge.  ...  To predict the web page credibility on the statement credibility and important basis we created a classifier feed forward neural network.  ... 
doi:10.1109/wi-iat.2014.47 dblp:conf/webi/JaworskiRW14 fatcat:rhxyfnavnnh3bgkqullyxfgvty

An Ensemble Click Model for Web Document Ranking

2020 International Journal of Engineering  
Annually, web search engine providers spend a lot of money on re-ranking documents in search engine result pages (SERP).  ...  Here, three modules are employed to predict users' clicks on SERPs simultaneously, the first module tries to predict users' click behaviors using Probabilistic Graphical Models, the second module is a  ...  Experiment 1 After running the MLP classifier as the combiner model with different neural network structures, we came up with a two-layer neural network containing 20×5 neurons.  ... 
doi:10.5829/ije.2020.33.07a.06 fatcat:nt6h2o6gsfch7aifveoex37mya

Web mining: Machine learning for web applications

Hsinchun Chen, Michael Chau
2005 Annual Review of Information Science and Technology  
Neural network programs have also been applied to text classification, usually employing the feedforwardhackpropagation neural network model (Lam & Lee, 1999; Ng, Goh, & Low, 1997; Wiener, Pedersen, &  ...  Maniezzo, 1994) , and because the neural network approach has a close resemblance to the probabilistic and fuzzy logic models, they can be easily combined (e.g., Paass, 1990 ).  ... 
doi:10.1002/aris.1440380107 fatcat:wdqwbszj7valbnyjfysbb4ap4y

A unified probabilistic framework for web page scoring systems

M. Diligenti, M. Gori, M. Maggini
2004 IEEE Transactions on Knowledge and Data Engineering  
In this paper, we propose a general probabilistic framework for Web Page Scoring Systems (WPSS), which incorporates and extends many of the relevant models proposed in the literature.  ...  Some successful approaches to page ranking in a hyperlinked environment, like the Web, are based on link analysis.  ...  Joint Conference on Neural Networks, for which he acted as the program chair (2000).  ... 
doi:10.1109/tkde.2004.1264818 fatcat:rn7oojuyajdmzplvblewhsrvyi

Web Navigation Prediction Using Multiple Evidence Combination and Domain Knowledge

Mamoun A. Awad, Latifur R. Khan
2007 IEEE transactions on systems, man and cybernetics. Part A. Systems and humans  
We also employ a reduction technique, which uses domain knowledge, to reduce the number of classifiers to improve the predictive accuracy and the prediction time of ANNs.  ...  Index Terms-Artificial neural networks (ANNs), association rule mining (ARM), Dempster's rule, Markov model, N-gram.  ...  Here, we define a class (or a label) as a unique identifier that represents a web page in a web site.  ... 
doi:10.1109/tsmca.2007.904781 fatcat:2ocm7popxzc3ljgcn4e33pfesu

On the Uniform Convergence of the Orthogonal Series-Type Kernel Regression Neural Networks in a Time-Varying Environment [chapter]

Meng Joo Er, Piotr Duda
2012 Lecture Notes in Computer Science  
Meng Joo Er. and Lena Pietruczuk Short Time Series of Website Visits Prediction by RBF Neural Networks and Support Vector Machine Regression 135 On Learning in a Time-Varying Environment by Using a Probabilistic  ...  Abgaz, Muhammad Javed, and Claus Pahl Measuring Web Page Similarity Based on Textual and Visual Properties 13 Vladimir Bartik New Specifics for a Hierarchial Estimator Meta-algorithm 22 Stanislaw  ... 
doi:10.1007/978-3-642-29347-4_5 fatcat:pqwbzeuqsbg6lkhlmmrsj6qb24

Ml-rbf: RBF Neural Networks for Multi-Label Learning

Min-Ling Zhang
2009 Neural Processing Letters  
After that, second layer weights of the Ml-rbf neural network are learned by minimizing a sumof-squares error function.  ...  In this paper, a neural network based multi-label learning algorithm named Ml-rbf is proposed, which is derived from the traditional radial basis function (RBF) methods.  ...  Figure 1 illustrates the architecture of a typical Ml-rbf neural network. As it is shown, the input to an Ml-rbf neural network corresponds to a d-dimensional feature vector.  ... 
doi:10.1007/s11063-009-9095-3 fatcat:pww2dh6w5nb5nlwchp5zqwf44u

Online Consumer Inspection on using E-shopping Service of E-commerce

2019 International journal of recent technology and engineering  
A customer-written product review with a low level of content abstractness yields the highest perceived review helpfulness  ...  The purpose of this study is to assess the impact of on-line consumer reviews on a decision of using e-commerce services.  ...  It is made up by a large number of highly interconnected processing neurons working in unison to solve specific problems. In this study, the back propagation neural network ANNs was employed.  ... 
doi:10.35940/ijrte.b1066.0982s1119 fatcat:3amrtef62reenlfnlhfqqjadia

Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks [article]

Jiwei Li, Alan Ritter, Dan Jurafsky
2017 arXiv   pre-print
community detection, or probabilistic reasoning over social networks.  ...  We propose learning individual representations of people using neural nets to integrate rich linguistic and network evidence gathered from social media.  ...  In EMNLP. pages 1136-1145. Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning.  ... 
arXiv:1510.05198v4 fatcat:fdac4m7r75eiveptl7jhqz4vly

Web Usage Mining using Statistical Classifiers and Fuzzy Artificial Neural Networks

Prakash S Raghavendra, Shreya Roy Chowdhury, Srilekha Vedula Kameswari
2011 International Journal of Multimedia and Image Processing  
The clustering and classification methods of k-means with non-Euclidean similarity measure, Bayesian classifiers and artificial neural networks, with standardised fuzzy inputs are implemented and compared  ...  In this paper, we model user behaviour as a vector of the time the user spends at each URL, and further classify a given new user access pattern.  ...  Employing artificial neural networks In supervised learning, we are given a set of example pairs and the aim is to find a function .  ... 
doi:10.20533/ijmip.2042.4647.2011.0002 fatcat:7unljih5ijb2rmxk5ebagt76lm
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