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Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

2017

Machine learning

Turkish Journal of Electrical Engineering and Computer Sciences

Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

A Comparison Of Feature Extraction Techniques For Malware Analysis, Mohammad Imran, Muhammad Tanvir Afzal, Muhammad Abdul Qadir Jan 2017

A Comparison Of Feature Extraction Techniques For Malware Analysis, Mohammad Imran, Muhammad Tanvir Afzal, Muhammad Abdul Qadir

Turkish Journal of Electrical Engineering and Computer Sciences

The manifold growth of malware in recent years has resulted in extensive research being conducted in the domain of malware analysis and detection, and theories from a wide variety of scientific knowledge domains have been applied to solve this problem. The algorithms from the machine learning paradigm have been particularly explored, and many feature extraction methods have been proposed in the literature for representing malware as feature vectors to be used in machine learning algorithms. In this paper we present a comparison of several feature extraction techniques by first applying them on system call logs of real malware, and then …


Late Fusion Of Facial Dynamics For Automatic Expression Recognition, Alessandra Bandrabur, Laura Florea, Cornel Florea, Matei Mancas Jan 2017

Late Fusion Of Facial Dynamics For Automatic Expression Recognition, Alessandra Bandrabur, Laura Florea, Cornel Florea, Matei Mancas

Turkish Journal of Electrical Engineering and Computer Sciences

Installment of a facial expression is associated with contractions and extensions of specific facial muscles. Noting that expression is about changes, we present a model for expression classification based on facial landmarks dynamics. Our model isolates the trajectory of facial fiducial points by wrapping them up in relevant features and discriminating among various alternatives with a machine learning classification system. The used features are geometric and temporal-based and the classification system is represented by a late fusion framework that combines several neural networks with binary responses. The proposed method is robust, being able to handle complex expression classes.