Image processing technique plays an important role in medical science for envisage various phenomenal structure of human body. Even though it helps more, sometimes it's very difficult to detect abnormal structures of human body by using simple images. Magnetic Resonance Imaging (MRI) is the one of the most significant technique to analyze human body and helpful for distinguishing and expounding the neural architecture of human brain effectively. This proposed strategy focus on detection and extraction of brain stroke from different patient's MRI images. In this work some preprocessing techniques like noise removal, filtering and segmentation is used for extract brain stroke partition accurately. The segmentation of brain stroke is implemented by using Fuzzy C-Means (FCM) clustering with two different levels of extraction. Edge detection is used for finding segmented portion of brain stroke edges accurately. Finally the stroke size is calculated for help doctors to make effective decisions about brain stroke. The experimental result proven that the proposed method is successful in detecting and extraction brain stroke efficiently with less time.

Web applications are most widely used technique for providing an access to online services. At the same time web applications are easiest way for vulnerable acts. When a security mechanism is failed then the user may download malicious code from a trusted web site. In this case, the malicious script is contracted to full access with all assets belonging to that legitimate web site. These types of attacks are called Cross-Site Scripting (XSS) attacks. Cross Site Scripting (XSS) attacks are the most common type of attack against web application, which allows hackers to inject the malicious script code for stealing the user"s confidential information. Recent studies show that malicious code detection has become the most frequent vulnerability. In web browsers, the malicious script codes are executed and used to transfer the sensitive data to the third party (or hackers) domain. Currently, most research areas are attempted to prevent XSS on both the client and server side. In this paper, we present a machine learning technique to classify the malicious web pages. This work focus some of the possible ways to detect the XSS script on client side based on the features extracted from the web document content and the URL to scan the web pages for check the malicious scripts.

Analysis of skin texture is very useful for creation and development of cosmetic products, skin texture modeling, and face recognition in security applications and also computerassisted diagnosis in dermatology. Several types of skin diseases are increasing human begins daily life; to deal with an effective and also very important manner the disease must be diagnosed properly. Skin texture analysis is one of the major problems in the field of medical diagnosis for finding skin diseases. Hence, the texture of skin is analyzed based on various features and characteristics so that the inconsistencies can be avoided during the treatment. The main goal of this study was to examine the texture of the human skin by image processing method. The skin properties like skin oiliness, dryness, pigmentation, fungus, infection, allergic symptoms and itching kind of problems association with skin texture profile is debated in the proposed work. Skin images are preprocessed using various pre-processing techniques and the Texture Filtering method is used for segment the skin textures so it can easy to identifying the skin properties accurately. Finally machine learning techniques are used to analyze and categorize the skin textures based on the texture and shape features. The experimental result shows that Decision Tree algorithm outperforms well in categorizing skin textures.

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Malicious code injection poses a serious security issue over the Internet or over the web application. In malicious code injection attacks, hackers can take advantage of defectively coded web application software to initiate malicious code into the organization's systems and network. The vulnerability persevere when a web application do not properly sanitize the data entered by the user on a web page. Attacker can steal confidential data of the user like password, pin number, and etc., these attacks resulting defeat of market value of the organization. This research work is model the malicious web page prediction as a classification task and provides a convenient solution by using a powerful machine learning technique such as Support Vector Machine (SVM), Extreme Learning Machine (ELM). The main aim of this research work is to predict the type of the malicious attack like Redirect, Script injection and XSS using the machine learning approaches; in this case, the prediction time is taken into consideration. The supervised learning algorithms such as SVM and ELM are employed for implementing the prediction model.