Jafar Shayan

Advantage and drawback of support vector machine functionality

Support Vector Machine(SVM)is one of the most efficient machine learning algorithms, which is mostly used for pattern recognition since its introduction in 1990s. SVMs vast variety of usage, such as face and speech recognition, face detection and image recognition has turned it into a very useful algorithm. This has also been applied to many pattern classification problems such as image recognition, speech recognition, text categorization, face detection, and faulty card detection.Statistics was collected from journals and electronic sources published in the period of 2000 to 2013. Pattern recognition aims to classify data based on either a priori knowledge or statistical information extracted from raw data, which is a powerful tool in data separation in many disciplines. The Support Vector Machine (SVM) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables.

Identifying Benefits and Risks Associated with Utilizing Cloud Computing

Cloud computing is an emerging computing model where IT and computing operations are delivered as services in highly scalable and cost effective manner. Recently, embarking this new model in business has become popular. Companies in diverse sectors intend to leverage cloud computing architecture, platforms and applications in order to gain higher competitive advantages. Likewise other models, cloud computing brought advantages to attract business but meanwhile fostering cloud has led to some risks, which can cause major impacts if business does not plan for mitigation. This paper surveys the advantages of cloud computing and in contrast the risks associated using them. Finally we conclude that a well-defined risk management program that focused on cloud computing is an essential part of gaining value from benefits of cloud computing.

Code analysis of lightweight encryption algorithms using in RFID systems to improve cipher performance

This research has investigated the performance analysis of lightweight encryption algorithms, which are using in RFID applications. In the first part, the codes of the investigated ciphers are analyzed, and then, four encryption algorithms include TEA, HIGHT, KATAN, and KLEIN are implemented on AVR Microcontroller to evaluate the number of CPU clock cycle for each cipher. In the last part, the results of two previous parts are compared.

Face Recognition via Taxonomy of Illumination Normalization

Presently, the difficulty in managing illumination over the face recognition techniques and smooth filters has emerged as one of the biggest challenges. This is due to differences between face images created by illuminations which are always bigger than the inter-person that usually be used for identities’ recognition. No doubt, the use of illumination technique for face recognition is much more popular with a greater number of users in various applications in these days. It is able to make applications that come with face recognition as a non-intrusive biometric feature becoming executable and utilizable. There are tremendous efforts put in developing the illumination and face recognition by which numerous methods had already been introduced. However, further considerations are required such as the deficiencies in comprehending the sub-spaces in illuminations pictures, intractability in face modelling as well as the tedious mechanisms of face surface reflections as far as face recognition and illumination concerned. In this study, few illuminations have been analyzed in order to construct the taxonomy. This covers the background and previous studies in illumination techniques as well the image-based face recognition over illumination. Data was obtained from the year of 1996 through 2014 out of books, journals as well as electronic sources that would share more on the advantageous and disadvantageous, the current technique’s performance as well as future plan.

An overview of objectionable image detection

Rapid growth of internet made sharing and distribution of media files became easier. This phenomena and this fact that internet is boundary less network, raised the concern of exposure to unwanted images and more importantly offensive images. Pornographic images are among most offensive images especially for minors. Although there are numbers of researches conducted to address this challenge, but this field is still crude. In this paper, different approaches to pornographic image detection is reviewed and categorized by focusing to content based methods. Reviewing these papers, general limitations of these works are discussed including lack of standard dataset for this field and standard definition which is well accepted for pornographic or naked images.