The bitcoin market reaction to the launch of central bank digital currencies

International audienc

Implementing a Symmetric Lightweight Cryptosystem in Highly Constrained IoT Devices by Using a Chaotic S-Box

In the Internet of Things (IoT), a lot of constrained devices are interconnected. The data collected from those devices can be the target of cyberattacks. In this paper, a lightweight cryptosystem that can be efficiently implemented in highly constrained IOT devices is proposed. The algorithm is mainly based on Advanced Encryption Standard (AES) and a new chaotic S-box. Since its adoption by the IEEE 802.15.4 protocol, AES in embedded platforms have been increasingly used. The main cryptographic properties of the generated S-box have been validated. The randomness of the generated S-box has been confirmed by the NIST tests. Experimental results and security analysis demonstrated that the cryptosystem can, on the one hand, reach good encryption results and respects the limitation of the sensor’s resources, on the other hand. So the proposed solution could be reliably applied in image encryption and secure communication between networked smart objects

Implementing a Symmetric Lightweight Cryptosystem in Highly Constrained IoT Devices by Using a Chaotic S-Box

In the Internet of Things (IoT), a lot of constrained devices are interconnected. The data collected from those devices can be the target of cyberattacks. In this paper, a lightweight cryptosystem that can be efficiently implemented in highly constrained IOT devices is proposed. The algorithm is mainly based on Advanced Encryption Standard (AES) and a new chaotic S-box. Since its adoption by the IEEE 802.15.4 protocol, AES in embedded platforms have been increasingly used. The main cryptographic properties of the generated S-box have been validated. The randomness of the generated S-box has been confirmed by the NIST tests. Experimental results and security analysis demonstrated that the cryptosystem can, on the one hand, reach good encryption results and respects the limitation of the sensor’s resources, on the other hand. So the proposed solution could be reliably applied in image encryption and secure communication between networked smart objects

Managing natural resource prices in a geopolitical risk environment

International audienc

Portfolio optimization through hybrid deep learning and genetic algorithms vine Copula-GARCH-EVT-CVaR model

International audienceThis study investigates the potential benefits of using the Conditional Value at Risk (CVaR) portfolio optimization approach with a GARCH model, Extreme Value Theory (EVT), and Vine Copula to obtain the optimal allocation decision for a portfolio consisting of Bitcoin, gold, oil, and stock indices. First, we fit a suitable GARCH model to the return series for each asset, followed by employing the Generalized Pareto Distribution (GPD) to model the innovation tails. Next, we construct a Vine Copula-GARCH-EVT model to capture the interdependence structure between the assets. To refine risk assessment, we combine our model with a Monte Carlo simulation and Mean-CVaR model to optimize the portfolio. In addition, we utilize a novel version of deep machine learning's genetic algorithm to address the optimization decision. This research contributes new evidence to the CVaR portfolio optimization approach and provides insights for portfolio managers seeking to optimize multi-asset portfolios

The European Central Bank and green finance: How would the green quantitative easing affect the investors' behavior during times of crisis?

International audienc

A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network

Brain tumor is a severe cancer and a life-threatening disease. Thus, early detection is crucial in the process of treatment. Recent progress in the field of deep learning has contributed enormously to the health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as a deep learning approach to detect brain tumors using MRI images. Due to the limited dataset, deep learning algorithms and CNNs should be improved to be more efficient. Thus, one of the most known techniques used to improve model performance is Data Augmentation. This paper presents a detailed review of various CNN architectures and highlights the characteristics of particular models such as ResNet, AlexNet, and VGG. After that, we provide an efficient method for detecting brain tumors using magnetic resonance imaging (MRI) datasets based on CNN and data augmentation. Evaluation metrics values of the proposed solution prove that it succeeded in being a contribution to previous studies in terms of both deep architectural design and high detection success

A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network

Brain tumor is a severe cancer and a life-threatening disease. Thus, early detection is crucial in the process of treatment. Recent progress in the field of deep learning has contributed enormously to the health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as a deep learning approach to detect brain tumors using MRI images. Due to the limited dataset, deep learning algorithms and CNNs should be improved to be more efficient. Thus, one of the most known techniques used to improve model performance is Data Augmentation. This paper presents a detailed review of various CNN architectures and highlights the characteristics of particular models such as ResNet, AlexNet, and VGG. After that, we provide an efficient method for detecting brain tumors using magnetic resonance imaging (MRI) datasets based on CNN and data augmentation. Evaluation metrics values of the proposed solution prove that it succeeded in being a contribution to previous studies in terms of both deep architectural design and high detection success

American hedge funds industry, market timing and COVID-19 crisis

International audienc