Digital watermarking is hiding the information inside a digital media. Its widely used in copyright protection of digital images .This paper presents a comparative study of digital watermarking techniques in frequency domain and explores the role of Discrete cosine transform DCT, Discrete wavelet transform DWT and Contourlet transform CT in generating robust embedding technique that resist various attacks. The experimental results show the superiority of CT-based watermarking over DWT and DCT watermarking techniques, the quality of the watermarked image is excellent, it can include huge amount of hidden data, but it's not much better than other techniques in resisting attacks.
With the rapid and the fast development of artificial intelligence technology, intelligent tutoring Systems (ITSs) are becoming one of the most important area of research and development. Intelligent tutoring Systems have very good impact for making computer-based instruction more adaptive and interactive. Intelligent tutoring Systems are becoming important aspect of educational systems that makes use of adaptive technologies to bring in aspects of a human-teacher delivering personalized and customized tutoring to a student, into online computer-based learning environments. Early Intervention Program (EIP) is very important to improve and enhance the overall development of children with Tiresome 21 (Down syndrome). Up till now, there is no ITS for Early Intervention for Down syndrome children. In order to help a child and parents in the implementation of Early Intervention Program, a proposed ITS framework has been developed. This ITS can help his/her parents assess and evaluate children's' skills in order to provide effective early intervention services to handicaps children according to their mental age and to evaluate their progress and learn. This paper explore the construction requirements to build ITS for Down syndrome children, and the points that differ the ITS for Down syndrome from the traditional ITSs.
Currently, there is no serum marker that is routinely recommended for lung cancer. Therefore, the aim of the present study was to demonstrate that plasma vascular endothelial growth factor 165 (VEGF 165) may be a potential marker for advanced lung cancer. Lung cancer is the leading cause of cancer-related mortality worldwide, therefore, it is important to develop novel diagnostic techniques. The present prospective case control study included two groups of patients; a control group of healthy volunteers and a second group of patients with advanced non-small cell lung cancer (NSCLC). The plasma VEGF 165 levels were measured at baseline by ELISA prior to the first-line gemcitabine-cisplatin regimen. The high VEGF 165 expression level cut-off was >703 pg/ml, and the primary endpoint was used to compare the plasma VEGF 165 levels between the NSCLC patients and the control group subjects. The secondary endpoint was used to identify the correlations between high VEGF 165 levels and; clinical response (CR), progression-free survival (PFS) and overall survival (OS) in the advanced NSCLC patients. In total, patients with advanced NSCLC (n=35) were compared with a control group of age- and gender-matched healthy subjects (n=34). The follow-up period was between Oct 2009 and Oct 2012, with a median follow-up time of 10.5 months. The median plasma VEGF 165 level was 707 pg/ml in the NSCLC patients versus 48 pg/ml in the healthy control subjects (P<0.001). However, no significant correlation was found between the plasma VEGF 165 levels and CR (P<0.5), median PFS (P=1.00) or OS (P=0.70). Therefore, it was concluded that plasma VEGF 165 may serve as a potential diagnostic marker for advanced NSCLC.
This paper proposes a deep learning based predictive model for forecasting and classifying the price of cryptocurrency and the direction of its movement. These two tasks are challenging to address since cryptocurrencies prices fluctuate with extremely high volatile behavior. However, it has been proven that cryptocurrency trading market doesn't show a perfect market property, i.e., price is not totally a random walk phenomenon. Based upon this, this study proves that the price value forecast and price movement direction classification is both predictable. A recurrent neural networks based predictive model is built to regress and classify prices. With adaptive dynamic features selection and the use of external dependable factors with a potential degree of predictability, the proposed model achieves unprecedented performance in terms of movement classification. A naïve simulation of a trading scenario is developed and it shows a 69% profitability score a cross a six months trading period for bitcoin.
Before the emergence of Component-Based Frameworks, similar issues have been addressed by other software development paradigms including e.g. Object-Oriented Programming (OOP), Component-Based Development (CBD), and Object-Oriented Framework. In this study, these approaches especially object-oriented Frameworks are compared to Component-Based Frameworks and their relationship are discussed. Different software reuse methods impacts on architectural patterns and support for application extensions and versioning. It is concluded that many of the mechanisms provided by Component-Based Framework can be enabled by software elements at the lower level. The main contribution of Component-Based Framework is the focus on Component development. All of them can be built on each other in layered manner by adopting suitable design patterns. Still some things such as which method to develop and upgrade existing application to other approach.
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