A widespread global health concern among women is the incidence of the second most leading cause of fatality which is breast cancer. Predicting the occurrence of breast cancer based on the risk factors will pave the way to an early diagnosis and an efficient treatment in a quicker time. Although there are many predictive models developed for breast cancer in the past, most of these models are generated from highly imbalanced data. The imbalanced data is usually biased towards the majority class but in cancer diagnosis, it is crucial to diagnose the patients with cancer correctly which are oftentimes the minority class. This study attempts to apply three different class balancing techniques namely oversampling (Synthetic Minority Oversampling Technique (SMOTE)), undersampling (SpreadSubsample) and a hybrid method (SMOTE and SpreadSubsample) on the Breast Cancer Surveillance Consortium (BCSC) dataset before constructing the supervised learning methods. The algorithms employed in this study include Naïve Bayes, Bayesian Network, Random Forest and Decision Tree (C4.5). The balancing method which yields the best performance across all the four classifiers were tested using the validation data to determine the final predictive model. The performances of the classifiers were evaluated using a Receiver Operating Characteristic (ROC) curve, sensitivity, and specificity.
The objective of this study is to investigate the significance impact of critical success factors on critical delays in the field of water infrastructure construction projects (WICPs) in the Abu Dhabi emirate in particular. Investigation was conducted utilizing quantitative approach by means of questionnaire survey to examine the understanding of professionals engaged in water infrastructure construction towards several critical success factors influencing critical delays. A total of 323 completed responses from owners, consultants and contractors representatives were gathered against 450 distributed questionnaires.The gathered questionnaires were analysed using an advanced multivariate statistical method of P artial Least Square Structural Equation Modelling (PLS-SEM). Data analysis was conducted in two major phases. The first phase involved a preliminary analysis of the data, to ensure that the data adequately meet the basic assumptions in using SEM. The second phase applied the two stages of SEM. The first stage included the establishment of measurement models for the latent constructs in the research. After confirming the uni-dimensionality, reliability and validity of the constructs in the first stage, the second stage developed to test the research hypotheses through developing the structural models. The results indicated
User authentication is an essential factor to protect digital service and prevent malicious users from gaining access to the system. As Single Factor Authentication (SFA) is less secure, organizations started to utilize Multi-Factor Authentication (MFA) to provide reliable protection by using two or more identification measures. Keystroke dynamics is a behavioral biometric, which analyses users typing rhythm to identify the legitimacy of the subject accessing the system. Keystroke dynamics that have a low implementation cost and does not require additional hardware in the authentication process since the collection of typing data is relatively simple as it does not require extra effort from the user. This study aims to propose deep learning model using Multilayer Perceptron (MLP) in keystroke dynamics for user authentication on CMU benchmark dataset. The user typing rhythm from 51 subjects collected based on the static password (.tie5Roanl) typed 400 times over 8 sessions and 50 repetitions per session. The MLP achieved optimum EER of 4.45% compared to original benchmark classifiers such as 9.6% (scaled Manhattan), 9.96% (Mahalanobis Nearest Neighbor), 10.22% (Outlier Count), 10.25% and 16.14% (Neural Network Auto-Assoc).
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