Data mining algorithms play an important role in the prediction of early-stage breast cancer. In this paper, we propose an approach that improves the accuracy and enhances the performance of three different classifiers: Decision Tree (J48), Naïve Bayes (NB), and Sequential Minimal Optimization (SMO). We also validate and compare the classifiers on two benchmark datasets: Wisconsin Breast Cancer (WBC) and Breast Cancer dataset. Data with imbalanced classes are a big problem in the classification phase since the probability of instances belonging to the majority class is significantly high, the algorithms are much more likely to classify new observations to the majority class. We address such problem in this work. We use the data level approach which consists of resampling the data in order to mitigate the effect caused by class imbalance. For evaluation, 10 fold cross-validation is performed. The efficiency of each classifier is assessed in terms of true positive, false positive, Roc curve, standard deviation (Std), and accuracy (AC). Experiments show that using a resample filter enhances the classifier’s performance where SMO outperforms others in the WBC dataset and J48 is superior to others in the Breast Cancer dataset.
Authentication with textual password has several limitations: passwords have low entropy in practice, are often difficult to remember, are vulnerable "shoulder surfing". Biometric system does not meet requirement as well. It relies upon unchanging features that have a lifetime as long as the individual. To avoid this limitation, we start to authenticate with thinking pass thought. User performs one mental task such as thinking of a word or phrase. In this study, Electroencephalography (EEG) was used as method for monitoring and recording the electrical activity of the brain. These signals can be captured and processed to get the useful information that can be used in pas-thoughts authentication system. Suitable analysis is essential for EEG to differentiate between best and worst tasks used for authentication. This study focuses on usefulness of EEG signal to identify best tasks suitable for the pass-thoughts authentication system. Artificial neural network (ANN) is used to train the data set. Then tests are conducted on the testing data of EEG signal to identify best and worst tasks suitable for authentication. Finally, the system performance was evaluated by computing the accuracy and therefore promising results were obtained.
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