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 et Big Data sont les prochains modèles informatiques. Ces paradigmes révolutionnaires conduisent l'informatique à un nouveau jeu de règles qui vise à changer la livraison des ressources informatiques et le modèle d'exploitation, créant ainsi un monde d'affaires nouveau qui croît de façon exponentielle et attire de plus en plus d'investissements des fournisseurs et des utilisateurs finaux qui attendent Amener profit de ces modèles innovants de l'informatique. Dans le même contexte, les chercheurs combattent pour développer, tester et optimiser les plates-formes Cloud Computing et Big Data, alors que plusieurs études sont en cours pour déterminer et améliorer les aspects essentiels de ces modèles informatiques, en particulier l'allocation des ressources. La planification de la puissance de traitement est cruciale quand il s'agit de Cloud Computing et Big Data en raison de la gestion de la croissance des données et la conception de livraison proposée par ces nouveaux modèles informatiques, qui nécessite des réponses plus rapides des plates-formes et des applications. D'où l'origine de l'importance de développer des algorithmes d'ordonnancement efficaces qui sont conformes à ces plates-formes de modèles informatiques et aux exigences d'infrastructure.
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 […]
