Morphological aspects in the diagnosis of skin lesions

En col·laboració amb la Universitat de Barcelona (UB), la Universitat Autònoma de Barcelona (UAB) i l’Institut de Ciències Fotòniques (ICFO)The ABCDE (Asymmetry, Border, Color, Rambla de Sant Nebridi, 10, Diameter and Elevation) rule represents a commonly used clinical guide for the early identification of melanoma. Here we develop a methodology based on an Artificial Neural Network which is trained to stablish a clear differentiation between benign and m lesions. This machine learning approach improves prognosis and diagnosis accuracy rates. align In order to obtain the 6 morphological feature data set for each of the 69 lesions considered, a 3D handheld system is used for acquiring the skin images and an image processing algorithm is applied

Application of Fractal and Wavelets in Microcalcification Detection

Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection

Feature and Variable Selection in Classification

The amount of information in the form of features and variables avail- able to machine learning algorithms is ever increasing. This can lead to classifiers that are prone to overfitting in high dimensions, high di- mensional models do not lend themselves to interpretable results, and the CPU and memory resources necessary to run on high-dimensional datasets severly limit the applications of the approaches. Variable and feature selection aim to remedy this by finding a subset of features that in some way captures the information provided best. In this paper we present the general methodology and highlight some specific approaches.Comment: Part of master seminar in document analysis held by Marcus Eichenberger-Liwick

Anti-Hijack: Runtime Detection of Malware Initiated Hijacking in Android

AbstractAccording to studies, Android is having the highest market share in smartphone operating systems. The number of Android apps (i.e. applications) are increasing day by day. Consequent threats and attacks on Android are also rising. There are a large number of apps which bypass users by hiding their functionalities and send users sensitive information and data across the network. Due to flexibility and openness of Android operating system, attack surfaces are being introduced every other day.In this paper, we are addressing detection of two fatal malware attacks; intent based hijacking and authenticated session hijacking. We have used the concept of honey-pot in detection of these two authentication hijacking problems. In order to achieve this, we have tested various apps and their interaction with the honey-pot maintained by real device or an emulator. We have designed benign app as a honey framed app. We argue that hijacking malware can be detected with higher accuracy using our method at run-time as compared to the traditional machine learning methods. Our approach, Anti-Hijack, which has provided the detection accuracy as high as 96%. This has been highly accurate to detect the unwanted interaction between hijacking malware and designed benign app. We have tested our approach on a strong data-set of Android apps for experiment and identifying vulnerable points. Our detection method Anti-Hijack is a novel contribution in this area which provides light weight, device operated run-time detection at hijacking malware