In this paper, a novel transmission scheme based on the discrete-time fractional-order chaotic system for private digital communications is proposed. The fractional-order Modified-Henon map is used. By adjusting the fractional-orders appropriately, it has been shown that the fractional-order Modified-Henon system possesses a chaotic behaviour. The exact synchronization based on the delayed observer is designed. A detailed study of synchronization and robustness of the proposed scheme is next presented. Simulation results are given to highlight the performances of the proposed method.

Forest fires pose a significant threat worldwide, with Algeria being no exception. In 2020 alone, Algeria witnessed devastating forest fires, affecting over 16,000 hectares of land, a phenomenon largely attributed to the impacts of climate change. Understanding the severity of these fires is crucial for effective management and mitigation efforts. This study focuses on the Akfadou forest and its surrounding areas in Algeria, aiming to develop a robust method for mapping fire severity. We employed a comprehensive approach that integrates satellite imagery analysis, machine learning techniques, and geographic information systems (GIS) to assess fire severity. By evaluating various remote sensing attributes from the Sentinel-2 and Planetscope satellites, we compared different methodologies for fire severity classification. Specifically, we examined the effectiveness of reflectance indices-based metrics such as Relative Burn Ratio (RBR) and Difference Burned Area Index for Sentinel-2 (dBIAS2), alongside machine learning algorithms including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), implemented in ArcGIS Pro 3.1.0. Our analysis revealed promising results, particularly in identifying high-severity fire areas. By comparing the output of our methods with ground truth data, we demonstrated the robust performance of our approach, with both SVM and CNN achieving accuracy scores exceeding 0.84. An innovative aspect of our study involved semi-automating the process of training sample labeling using spectral indices rasters and masks. This approach optimizes raster selection for distinct fire severity classes, ensuring accuracy and efficiency in classification. This research contributes to the broader understanding of forest fire dynamics and provides valuable insights for fire management and environmental monitoring efforts in Algeria and similar regions. By accurately mapping fire severity, we can better assess the impacts of climate change and land use changes, facilitating proactive measures to mitigate future fire incidents.

Data Security for end-end transmission is achieved by many different symmetric and asymmetric techniques for message confidentiality, message authentication and key exchange using transport layer security. This paper presents a new encryption system for secure medical images transmission. The hybrid encryption system is based on AES and RSA algorithms. AES is used for data confidentiality, the RSA is used for authentication and the integrity is assured by the basic function of correlation between adjacent pixels in the image. Our encryption system generates a unique password every new session of encryption. Several parameters were used for various tests of our analysis.