Hepatocellular Carcinoma is the most common type of liver cancer having a strong relation with cirrhosis. Undoubtedly, cirrhosis may be caused by the virus infection of hepatitis B (HBV) and hepatitis C (HBC) or through alchoholism. However, even when cirrhosis has not been developed, patients with hepatitis viral infections are still at the risk of liver cancer. Apparently, among the numerous medical imaging techniques, Computed Tomography (CT) is the best in defining liver tumor borders. Unfortunately, these imaging techniques, including the CT procedures, usually rely on an appended application to reconstruct the generated 2-D slices to 3-D model. This may involve high performance computation, may be time-consuming or costly. Moreover, even with the outstanding performances of CT in defining the liver tumor boundaries, contrast between tumor tissues and the surrounding liver parenchyma is too low in CT slices. With such a close proxity in the tumor and the surrounding liver tissues, accurate characterization of liver tumor is a challenge. Previously, algorithms were developed to reveal abnormalities in brain's MRI datasets and CT abdominal pelvic, however, introducing a framework that could accurately characterize liver tumor and its surrounding tissues in CT datasets would go a long way in contributing to medical diagnosis and therapy planning of Hepatocellular Carcinoma. This paper proposes an Hepatocellular Carcinoma framework by extending the functionalities of SurLens Visualization System with an automatic liver tumor localization technique using Compute Unified Device Architecture (CUDA). The study was evaluated with liver CT datasets from the Imaging Science and Information Systems (ISIS) Center, the Georgetown University Medical Center. Significantly, visualization of liver CT datasets and the localization of the entangled tumor was achieved without prior datasets segmentation. Interestingly, the framework achieved remarkably good processing speed at a reasonably cheaper cost with an immediate reconstruction of the datasets and mapping of the tumor tissues within the surrounding liver parenchyma.
Cloud computing (CC) has recently been receiving tremendous attention from the IT industry and academic researchers. CC leverages its unique services to cloud customers in a pay-as-you-go, anytime, anywhere manner. Cloud services provide dynamically scalable services through the Internet on demand. Therefore, service provisioning plays a key role in CC. The cloud customer must be able to select appropriate services according to his or her needs. Several approaches have been proposed to solve the service selection problem, including multicriteria decision analysis (MCDA). MCDA enables the user to choose from among a number of available choices. In this paper, we analyze the application of MCDA to service selection in CC. We identify and synthesize several MCDA techniques and provide a comprehensive analysis of this technology for general readers. In addition, we present a taxonomy derived from a survey of the current literature. Finally, we highlight several state-of-the-art practical aspects of MCDA implementation in cloud computing service selection. The contributions of this study are four-fold: (a) focusing on the state-of-the-art MCDA techniques, (b) highlighting the comparative analysis and suitability of several MCDA methods, (c) presenting a taxonomy through extensive literature review, and (d) analyzing and summarizing the cloud computing service selections in different scenarios.
Cognitive radio (CR) enables unlicensed users (or secondary users, SUs) to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs). Reinforcement learning (RL) is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research area remains largely unexplored in the domain of routing in CR networks. This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate) through simulation. New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing. Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs' network performance without significantly jeopardizing PUs' network performance, specifically SUs' interference to PUs.
This paper presents a study of the application of autonomously learning multiple neural network systems to medical pattern classification tasks. In our earlier work, a hybrid neural network architecture has been developed for on-line learning and probability estimation tasks. The network has been shown to be capable of asymptotically achieving the Bayes optimal classification rates, on-line, in a number of benchmark classification experiments. In the context of pattern classification, however, the concept of multiple classifier systems has been proposed to improve the performance of a single classifier. Thus, three decision combination algorithms have been implemented to produce a multiple neural network classifier system. Here the applicability of the system is assessed using patient records in two medical domains. The first task is the prognosis of patients admitted to coronary care units; whereas the second is the prediction of survival in trauma patients. The results are compared with those from logistic regression models, and implications of the system as a useful clinical diagnostic tool are discussed.
Comparative effectiveness research (CER) is an important branch of pharmacoeconomics that systematically studies and evaluates the cost-effectiveness of medical interventions. CER plays instrumental roles in guiding government public health policy programs and insurance. Countries throughout the world use different methods of CER to help make medical decisions based on providing optimal therapy at a reduced cost. Expenses to the healthcare system continue to rise, and CER is one-way in which expenses could be curbed in the future by applying cost-effectiveness evidence to clinical decisions. China, India, South Korea, and the United Kingdom are of essential focus because these country's economies and health care expenses continue to expand. The structures and use of CER are diverse throughout these countries, and each is of prime importance. By conducting this thorough comparison of CER in different nations, strategies and organizational setups from different countries can be applied to help guide public health and medical decision-making in order to continue to expand the establishment and role of CER programs. The patient-centered medical home has been created to help reduce costs in the primary care sector and to help improve the effectiveness of therapy. Barriers to CER are also important as many stakeholders need to be able to work together to provide the best CER evidence. The advancement of CER in multiple countries throughout the world provides a possible way of reducing costs to the healthcare system in an age of expanding expenses.