Healthcare professionals require advanced image processing software to enhance the quality of clinical decisions. However, any investment in sophisticated local applications would dramatically increase healthcare costs. To address this issue, medical providers are interested in adopting cloud technology. In spite of its multiple advantages, outsourcing computations to an external provider arises several challenges. In fact, security is the major factor hindering the widespread acceptance of this new concept. Recently, various solutions have been suggested to fulfill healthcare demands. Though, ensuring privacy and high performance needs more improvements to meet the healthcare sector requirements. To this end, we propose a framework based on segmentation approach to secure cloud-based medical image processing in the healthcare system.
Cloud Computing and Big Data are the upcoming Information Technology (IT) computing models. These groundbreaking paradigms are leading IT to a new set of rules that aims to change computing resources delivery and exploitation model, thus creating a novel business market that is exponentially growing and attracting more and more investments from both providers and end users that are looking forward to make profits from these innovative models of computing. In the same context, researchers and investigators are wrestling time in order to develop, test and optimize Cloud Computing and Big Data platforms, whereas several studies are ongoing to determine and enhance the essential aspects of these computing models especially compute resources allocation. The processing power scheduling is crucial when it comes to Cloud Computing and Big Data because of the data growth management and delivery design proposed by these new computing models, that requires faster responses from platforms and applications. Hence originates the importance of developing high efficient scheduling algorithms that are compliant with these computing models platforms and infrastructures requirement.
This paper proposes an ontological integration model for credit risk management. It is based on three ontologies; one is global describing credit risk management process and two other locals, the first, describes the credit granting process, and the second presents the concepts necessary for the monitoring of credit system. This paper also presents the technique used for matching between global ontology and local ontologies.
In this paper we present a system for offline recognition cursive Arabic handwritten text which is analytical without explicit segmentation based on Hidden Markov Models (HMMs). Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models. The HMM-based classifiercontains a training module and a recognition module. The training module estimates the parameters of each of the character HMMs uses the Baum-Welchalgorithm. In the recognition phase, feature vectors extracted from an image are passed to a network of word lexicon entries formed of character models. The character sequence providing the maximumlikelihood identifies the recognized entry. If required, the recognition can generate N best output hypotheses rather than just the single best one. To determine the best output hypotheses, the Viterbi algorithm is used.The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition.
Digital humanities require IT Infrastructure and sophisticated analytical tools, including datavisualization, data mining, statistics, text mining and information retrieval. Regarding funding, tobuild a local data center will necessitate substantial investments. Fortunately, there is another optionthat will help researchers take advantage of these IT services to access, use and share informationeasily. Cloud services ideally offer on-demand software and resources over the Internet to read andanalyze ancient documents. More interestingly, billing system is completely flexible and based onresource usage and Quality of Service (QoS) level. In spite of its multiple advantages, outsourcingcomputations to an external provider arises several challenges. Specifically, security is the majorfactor hindering the widespread acceptance of this new concept. As a case study, we review the use ofcloud computing to process digital images safely. Recently, various solutions have been suggested tosecure data processing in cloud environement. Though, ensuring privacy and high performance needsmore improvements to protect the organization's most sensitive data. To this end, we propose aframework based on segmentation and watermarking techniques to ensure data privacy. In this respect,segementation algorithm is used to to protect client's data against untauhorized access, whilewatermarking method determines and maintains ownership. Consequentely, this framework willincrease the speed of […]
