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Due to the distributed and non-integrated nature of the healthcare systems which results from the application-centric view it leads to a challenging task to manage healthcare data ex change (heterogeneity problem). On the other hand, Blockchain technologies are emerging as promising and cost -effective means to meet some of these requirements due to their inherent design properties, such as secure cryptography and a resilient peer-to-peer network. Likewise, Blockchain-based applications can benefit the healthcare domain via their properties of asset sharing, and audit trails of data access. Existing work mainly pays attention to centralized and blockchain-based mechanisms. But it doesn't realize the increase need for better data interoperability amount multiple healthcare systems and services. This requires shifting from the applicationcentric solutions toward the patient-centric solutions. This paper presents A secure and efficient fram ework based on Blockchain, Cloud, and IoT named Patient -Centric Healthcare Framework (PCH) for better healthcare systems interoperability. A tiered-based architecture (5 tiers) with collaboration is designed for the feasible realization of PCH. Also, the design and implementation aspects start from the layering diagram, system context, and detailed reference architecture that emphasizes the detailed component topology and interactions within the framework. An electronic medical record is used to show how he althcare data is processed with the required security considerations. Then, an evaluation of PCH against the existing Blockchain-based healthcare frameworks is conducted. The results analysis demonstrates that PCH offers practical solutions to protect healthcare data and support efficient data sharing with better interoperability

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Nowadays, organizations strive for business process optimization to survive the current competitive business environment. The widespread use of social media and Web 2.0 applications has dramatically changed the structure of the business process. Such evolution caused an increase in the flow of data and information, which led to knowledge‐intensive business processes. Thus, modeling the knowledge‐intensive processes became essential to make use of the flow of knowledge from social media and to fulfill their demanding requirements. Therefore, knowledge and social dimensions should be integrated into the traditional business process life cycle. This integration yields a large flow of customer knowledge that needs to be efficiently used and managed. Moreover, the use of ontologies for knowledge representation opens up a supplementary view for providing machine‐accessible terminology to processes. Ontologies make a significant contribution to the categorization and organization of the incorporated and unstructured information. This paper proposes a framework that integrates the social business processes with a knowledge base in the form of an ontology to manage and enhance the overall customer experience. The proposed framework aims to enable organizations to improve knowledge support for their customer‐driven business processes. In addition, the extracted knowledge from social media will support organizations in managing the customer knowledge and building up the relationships with customers. The proposed social ontology will ease the querying of existing customer knowledge and inferring new knowledge that enhances optimizing the business process, and customer experience.

Plant diseases are a foremost risk to the safety of food. They have the potential to significantly reduce agricultural products quality and quantity. In agriculture sectors, it is the most prominent challenge to recognize plant diseases. In computer vision, the Convolutional Neural Network (CNN) produces good results when solving image classification tasks. For plant disease diagnosis, many deep learning architectures have been applied. This paper introduces a transfer learning based model for detecting tomato leaf diseases. This study proposes a model of DenseNet201 as a transfer learning-based model and CNN classifier. A comparison study between four deep learning models (VGG16, Inception V3, ResNet152V2 and DenseNet201) done in order to determine the best accuracy in using transfer learning in plant disease detection. The used images dataset contains 22930 photos of tomato leaves in 10 different classes, 9 disorders and one healthy class. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.84% and validation accuracy of 99.30%.