Search

Blockchain has attracted a lot of interest since its publication because to its unique characteristics of immutability, decentralization, smart contract, and consensus mechanism. Today’s healthcare systems are facing many issues in the era of digital health transformation and the growth of electronic health records. Blockchain has the potential to provide solutions to a variety of electronic health record (EHR)-related challenges, including data management, security, data sharing and patient privacy. This paper represents a blockchain-based healthcare industry survey; it includes many research publications in high-ranking scientific journals in recent years from 2016 to 2022. We adopt the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach and aspects. This work is based on five databases: Elsevier, Springer, IEEE Xplore, PubMed and MDPI. The number of studies included in this study was 56. We found that researchers attempted to use blockchain technology to assist patients and healthcare providers in diagnosis and data processing, as well as including multiple entities. Our study discusses the potential of blockchain technology, its roles and benefits in healthcare, aiming to solve several problems such as data management, data sharing, access control, data security and privacy, which are missed in the conventional healthcare system.

With the growing demand for water resources, the need for monitoring has become a necessity for rational and sustainable use of this resource. Water meter data collection is an essential step toward this goal. In Morocco, this task is performed manually at most once a month due to constraints related to the cost and time. In general, the consumption is estimated and calculated based on the average consumption recorded in the previous months. This causes many claims from customers because of higher invoices, which does not reflect reality. In this paper, we propose a fully AI-based system to automate water meter data collection, which is composed of a Recognition System (RS) and a web services platform. This framework offers multiple services for both customers and water service providers, such as consumption monitoring, detecting water leaks, visualizing water consumption, and potable water coverage in a geographic map. It also provides a powerful tool to help ensure accurate decision making with multiple reporting services. The main component of the RS is the Convolutional Neural Network model trained on a proposed MR-AMR (Moroccan Automatic Meter Reading) dataset. In the model test phase, we achieved an accuracy of 98.70%. Our system was tested and validated by experiments.

Along with the significant advantages of cloud computing paradigm, the number of enterprises, which expect to move a legacy system towards a cloud, is steadily increasing. Unfortunately, this move is not straightforward. There are many challenges to take up. The applications are often written with the outdated technologies. While some enterprises redevelop applications with a specific Cloud provider in mind, others try to move the legacy systems, either because the organization wants to keep the past investments, or because the legacy systems hold important data. Migrating the legacy systems to the Cloud introduces technical and business challenges. This paper aims to study deeply and to compare existing Cloud migration methods, based on Model Driven Engineering (MDE) approach to highlight the strengths and weaknesses of each one. Finally, we have proposed a Cloud legacy system Migration Method relied on Architecture Driven Modernization (ADM), and explained its working process.