Impact of Blockchain technology in Healthcare

The current healthcare systems are facing many issues in terms of data management, data sharing, information security and patient privacy, data immutability, trust, and transparency. In addition, the multiple existing healthcare systems are centralized which complicates the healthcare professionals, patients in managing their data and causes several problems. Blockchain technology as a decentralized peer-to-peer network has the power to digitalize and transform the manner that the data are managed in the healthcare industry, in this regard, is one such domain that might benefit from Blockchain technology in different manners. This paper aims to improve a review of recent works on Blockchain-based healthcare applications

Applying Deep Learning and Computer Vision Techniques for an e-Sport and Smart Coaching System Using a Multiview Dataset: Case of Shotokan Karate

Smart coaching and e-sport platforms have shown a great interest in the recent research studies. Through this study, we aim to globalize the practice of sport, especially Shotokan Karate, by connecting participants and coaches on an international scale through the integration of Artificial Intelligence techniques such as computer vision and deep learning, to give the possibility of carrying out national and international virtual training courses without logistical constraints. The proposed work aims to apply the latest action detection, action recognition, and action classification methods for different Karate movements using the LSTM and the ST-GCN algorithms and proposes these movements in 3D using Video Inference for Human Body Pose and Shape Estimation (VIBE). Our proper Multiview Dataset contains pose estimations of a set of basic movements captured by a karate Shotokan expert (6th DAN Black Belt) from three views (Front view, Left view, and Right view) using OpenPose and FastPose for human body keypoint detection. The current study sets out to detect, recognize, classify and score different participants' movements. We achieved greater than 96% recognition accuracy of this dataset using the LSTM algorithm, and 91.01% using the ST-GCN algorithm

Design and implementation of a New Blockchain-based digital health passport: A Moroccan case study

In the context of COVID-19 pandemic, the Moroccan Interior and Health Ministries have proposed to use the health pass with a QR code to identify vaccinated people. Additionally, the government suggested a mobile application to control the health passport authenticity. However, the key problem is the possibility of anyone scanning the QR code and figuring out citizens' private information, causing severe issues about individual privacy. In this work, the main contribution is integrating a private Blockchain-based digital health passport to ensure high protection of sensitive information, security and privacy among all the actors (Government, Ministry of Interior, Ministry of Health, verifiers) that comply with the CNDP (National Commission for the Control of Personal Data Protection) and the Moroccan Law 09–08. In our proposed architectural framework solution, we identify two types of actors: authorized and unauthorized, to limit and control access to the citizens' personal information. Besides, to preserve individuals' privacy, we adopt on-chain and off-chain storage (Interplanetary File Systems IPFS). In our case, smart contracts improve security and privacy in the health passport verification process. Our system implementation describes the proposed solution to grant individual privacy. To verify and validate our approach, we used Remix-IDE and Ethereum Blockchain to build smart contracts

Real-time detection of MAC layer misbehavior in mobile ad hoc networks

The MAC layer misbehavior of the IEEE 802.11 standard can have a negative impact on the wireless network’s performance, similar to the effects of denial of service attacks. The goal of this misbehavior was handling the protocol to increase the greedy nodes transmission rate at the expense of the other honest nodes. In fact, nodes in IEEE 802.11 standard should wait for a random backoff interval time to access to the channel before initiating any transmission. Greedy nodes use a malicious technique to reduce the channel waiting time and occupy the channel. This paper introduces a new scheme to detect such malicious behavior, which is based on statistical process control (SPC) borrowed from the industrial field in a quality management context. To the best of our knowledge, this approach has not been proposed in state of the art, reports concerning the detection of greedy behaviors in mobile ad hoc networks. The approach has the power to identify greedy nodes in real time by using a graphical tool called «control chart» that measures the throughput and the inter-packet interval time for each node, and raises an alert if this measure is over a defined threshold. The validation of all obtained results is performed in the network simulator NS2