There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorithms are faster, easier, or more accurate than others are. This study is based on genetic programming and machine learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this study was to optimize the learning algorithm. In this context, we applied the genetic programming technique to select the best features and perfect parameter values of the machine learning classifiers. The performance of the proposed method was based on sensitivity, specificity, precision, accuracy, and the roc curves. The present study proves that genetic programming can automatically find the best model by combining feature preprocessing methods and classifier algorithms.

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The fast spread of coronavirus disease (COVID-19) caused by SARS-CoV-2 has become a pandemic and a serious threat to the world. As of May 30, 2020, this disease had infected more than 6 million people globally, with hundreds of thousands of deaths. Therefore, there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems. This study uses gradient boosting regression (GBR) to build a trained model to predict the daily total confirmed cases of COVID-19. The GBR method can minimize the loss function of the training process and create a single strong learner from weak learners. Experiments are conducted on a dataset of daily confirmed COVID-19 cases from January 22, 2020, to May 30, 2020. The results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR method. The results reveal that the GBR model achieves 0.00686 root mean square error, the lowest among several comparative models.

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Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative breast lesion diagnosis based on machine-learning algorithms. We used Tabu search to select the most significant features. The evaluation of the feature is based on the dependency degree of each attribute in the rough set. The categorization of reduced features was built using five machine-learning algorithms. The proposed models were applied to the BIDMC-MGH and Wisconsin Diagnostic Breast Cancer datasets. The performance measures of the used models were evaluated owing to five criteria. The top performing models were AdaBoost and logistic regression. Comparisons with others works prove the efficiency of the proposed method for superior diagnosis of breast cancer against the reviewed classification techniques.

An Input validation can be a critical issue. Typically, a little attention is paid to it in a web development project, because overenthusiastic validation can tend to cause failures in the software, and can also break the security upon web applications such as an unauthorized access to data. Now, it is estimated the web application vulnerabilities (such as XSS or SQL injection) for more than two thirds of the reported web security vulnerabilities. In this paper, we start with a case study of the bypassing data validation and security vulnerabilities such as SQL injection and then go on to discuss the merits of a number of common data validation techniques. We also review the different solutions to date to provide data validation techniques in ecommerce applications. From this analysis, a new data validation service which is based upon semantic web Technologies, has been designed and implemented to prevent the web security vulnerabilities at the application level and to secure the web system even if the input validation modules are bypassed. Our semantic architecture consists of the following components: RDFa annotation for elements of web pages, interceptor, RDF extractor, RDF parser, and data validator. The experimental results of the pilot study indicate that the proposed data validation service might provide a detection, and prevention of some web application attacks.

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