Computer Aided Diagnosis (CAD) is used to assist radiologist in classifying various type of breast cancers. It already proved its success not only in reducing human error in reading the mammograms but also shows better and reliable classification into benign and malignant abnormalities. This paper will report and attempt on using Radial Basis Function Neural Network (RBFNN) for mammograms classification based on Gray-level Co-occurrence Matrix (GLCM) texture based features. In this study, normal and abnormal breast image used as the standard input are taken from Mammographic Image Analysis Society (MIAS) digital mammogram database. The computational experiments show that RBFNN is better than Back-propagation Neural Network (BPNN) in performing breast cancer classification. For normal and abnormal classification, the result shows that RBFNN's accuracy is 93.98%, which is 14% higher than BPNN, while the accuracy of benign and malignant classification is 94.29% which is 2% higher than BPNN.

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The security of website application has become important in the last decades. According to the Open Web Application Security Project (OWASP), the SQL Injection is classified as one of the major vulnerabilities found in web application security. This research is focused on improving website security in dealing with SQL Injection attacks by stopping, monitoring, and dividing types of SQL Injection attacks using the features provided by the proposed Web Application Firewall (WAF). The architecture is designed to detect and prevent some types of SQL Injection attacks, including Tautologies, Logically Incorrect Queries, Union Queries, Piggy Backed Queries, Stored Procedures. For the testing scenario, this experiment uses an application that has become an industry standard in identifying and validating security holes on a website. The result of this research is that the proposed system is able to increase the website security from SQL Injection.