Accurate diagnosis of cancer plays an important role in order to save human life. The results of the diagnosis indicate by the medical experts are mostly differentiated based on the experience of different medical experts. This problem could risk the life of the cancer patients. A fast and effective method to detect the lung nodules and separate the cancer images from other lung diseases like tuberculosis is becoming increasingly needed due to the fact that the incidence of lung cancer has risen dramatically in recent years and an early detection can save thousands of lives each year. The focus of this paper is to compare the performance of the ANN and SVM classifiers on acquired online cancer datasets. The performance of both classifiers is evaluated using different measuring parameters namely; accuracy, sensitivity, specificity, true positive, true negative, false positive and false negative.
Character Recognition has been one of the most intensive research during the last few decades because of its potential applications. However, most existing classifiers used in recognizing online handwritten characters suffer from poor feature selection and slow convergence which affect training time and recognition accuracy. Hence, this paper focused on integrating an optimization (genetic algorithm) into modified backpropagation neural network to enhance the performance of character recognition. This paper proposed a methodology that is based on extraction of features using stroke number, invariant moments, projection and zoning. Genetic algorithm was use as feature selection to optimize the subset of the character for classification. A Modified Genetic Algorithm (MGA) was modified to reduce character recognition errors using fitness function and genetic operators. However, an integration of optimization algorithm (modified genetic algorithm) into an existing modified backpropagation (MOBP) learning algorithm was employed as classifier. For further enhancement of classifier, three classifiers (C1, C2 and C3) were formulated from MGA-MOBP model and evaluated using training time and correct recognition accuracy. C3 performed better than C1 and C2 in terms of convergence rate, correct recognition accuracy and feature selection (its ability to remove irrelevant features of character images). The results of the developed system achieved a false recognition of 0.56% and 99.44% overall recognition accuracy compared with existing models. General TermsPattern Recognition
World Wide Web has become a huge collection of documents and the amount of documents available is increasing on a daily basis. How to correctly classify the vast documents into a particular category and locate any document of interest easily has become a challenge researchers have been trying to solve for decades and different researchers have attempted different algorithms using different platform to achieve this aim. In this paper, a University web site was used as a case study and a machine learning workbench called WEKA (Waikato Environment for Knowledge Analysis) which provides a general-purpose environment for automatic classification, regression, clustering and feature selection was used as a machine learning platform. Running Naïve Bayes with 10-fold cross validation on the selected web data gives a 77% correctly classified instances in zero second with relative absolute error of 68.9937%. This shows the ability of Naïve Bayes algorithm to accurately classify vast amount of web document in a short time.
Handwritten character recognition has applications in several industries such as Banking for reading of cheques and Libraries/ National archives for digital searchable storage of historic texts. The main feature typically used for the recognition task is the character image. However, there are other possible features such as the hand (left or right) used by author, number of strokes and other geometric features that can be captured when writing on digital devices. This paper investigates the effect of using some non-image features on the recognition rate of three classifiers: Instance Based Learner (IBk), Support Vector Machines (SVM) and the Multilayer Perceptron (MLP) Neural Network for singly-written alpha-numeric character recognition. Our experiments were conducted using the WEKA machine learning tool on offline and online handwritten acquired locally. A percentage split (66%-34% train-test) evaluation methodology was adopted with the classification accuracy measured. Results indicate that non-image additional features improved the accuracy across the three classifiers for the online and offline character datasets. However, this improvement was not statistically significant. SVM gave the best accuracy for the online dataset while IBk performed better than the other two classifiers for the offline dataset. We intend to investigate the effect of non-image features at other levels of text granularity such as words and sentences. 5156 | P a g e S e p t e m b e r 2 3 , 2 0 1 4
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