AIM: To develop a mathematical model for the early detection of hepatocellular carcinoma (HCC) with a panel of serum proteins in combination with α-fetoprotein (AFP). METHODS: Serum levels of interleukin (IL)-8, soluble intercellular adhesion molecule-1 (sICAM-1), soluble tumor necrosis factor receptor II (sTNF-R II), proteasome, and β-catenin were measured in 479 subjects categorized into four groups: (1) HCC concurrent with hepatitis C virus (HCV) infection (n = 192); (2) HCV related liver cirrhosis (LC) (n = 96); (3) Chronic hepatitis C (CHC) (n = 96); and (4) Healthy controls (n = 95). The

Dynamic Bayesian Networks (DBNs) are efficient graphical tools that could be used to detect causal relationships in multivariate systems. Here, we utilize DBNs to infer causality among electroencephalography (EEG) electrodes during a motor imagery task. We inferred the causal relationships between EEG electrodes during each of right and left hands imagery movements from 9 different subjects. We demonstrate how using the inferred connectivity as a feature enhances the discrimination among right and left hands imagery movements compared to using traditional band power features. Our analysis

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With the rapid increase in the volume of Arabic opinionated posts on different microblogging mediums comes an increasing demand for Arabic sentiment analysis tools. Yet, research in the area of Arabic sentiment analysis is progressing at a very slow pace compared to that being carried out in English and other languages. This paper highlights the major problems and open research issues that face sentiment analysis of Arabic social media. The paper also presents a case study the goal of which is to investigate the possibility of determining the semantic orientation of Arabic Egyptian tweets and

Research has shown that Arabic broken plurals constitute approximately 10% of the content of Arabic texts. Detecting Arabic broken plurals and mapping them to their singular forms is a task that can greatly affect the performance of information retrieval, annotation or tagging tasks, and many other text mining applications. It has been reported that the most effective way of detecting broken plurals is through the use of dictionaries. However, if the target domain is a specialized one, or one for which there are no such dictionaries, building those manually becomes a tiresome, not to mention

In this work, we present a new method for analyzing cardiac tagged Magnetic Resonance Imaging (tMRI). The method combines two major tracking techniques: Harmonic Phase (HARP) and Optical Flow (OF). The results of the two techniques are fused together to accurately estimate the displacement of each myocardium point. The developed methods were tested using numerical MRI phantom at different SNR levels and deformation rates. The results show that the proposed method is more accurate and reliable than the HARP and the OF methods. © 2012 IEEE.

Purpose: This paper aims to present an integrated framework for hotel revenue room maximization. The revenue management (RM) model presented in this work treats the shortcomings in existing systems. In particular, it extends existing optimization techniques for hotel revenue management to address group reservations and uses "forecasted demand" arrivals generated from the real data. Design/methodology/approach: The proposed forecasting module attempts to model the hotel reservation process from first principles. In particular, it models hotel arrivals as an interrelated process of stochastic

Strain measurement is a quantity used for assessing the regional function of the left ventricular (LV) of the heart. They are computed by tracking the motion of the non-invasive, virtual tags in the cardiac muscle with time. Tracking these tags gives information for each region of the cardiac muscle by quantifying its deformation during contraction (systolic period) and relaxation (diastolic period). However, these strain measurements suffer from inaccuracies caused by the degradation of the tags and the image quality. In this work, numerical simulations are used to investigate the factors

Object localization has become a necessary module in many radiofrequency identification (RFID) systems that require tracking features besides the conventional identification feature. A number of techniques exists in literature that uses the RFID signal information to locate the tagged objects, i.e. objects wearing RFID tags. Nevertheless, in many applications, it is required to track objects that do not carry a tag (whether intentionally or unintentionally). In this work, we propose a technique for tag-less object localization. The technique is based on reconstructing the attenuation map of

Brain-Computer Interfaces (BCI) is a one kind of communication system that enables control of devices or communication with others only through brain signal activities without using motor activities. P300 Speller is a BCI paradigm that helps disabled subjects to spell words by means of their brain signal activities. This paper tries to demonstrate the performance of different machine learning algorithms based on classification accuracy. Performance has been evaluated on the data sets acquired using BC12000's P300 Speller Paradigm provided by BCI competitions II (2003) & III (2004) organizers