Within the dynamic landscape of contemporary information systems, the paramount importance of data protection for organizations and businesses is underscored, particularly in light of the evolving challenges posed by the Covid-19 pandemic. As companies increasingly rely on remote access solutions to navigate the pandemic’s impact and sustain growth, the concomitant rise in security breaches necessitates a profound reevaluation of network and information system fortifications. To address the imperative for heightened data security, a transformative initiative is required, emphasizing innovation in cryptography as the linchpin for preserving the confidentiality, integrity, and authenticity of information. This document serves as an introduction to the Sagittarius encryption method, a sophisticated cryptographic approach inspired by the Sagittarius constellation, a prominent grouping of stars in the celestial sphere. The objective is to harness the symbolic strength of this constellation to develop a hybrid algorithm capable of addressing security concerns associated with remote access while providing a comprehensive shield for data integrity, confidentiality, and authenticity. The Sagittarius encryption method represents a progressive stride towards fortifying the digital defenses of organizations, acknowledging the non-negotiable importance of preserving the sanctity of information in an ever-evolving technological landscape.
Intelligent Tutoring Systems (ITSs) are advanced educational technologies that incorporate Artificial Intelligence (AI) techniques to offer instruction adapted to the student’s individual needs. The main contribution of this paper is to provide an up-to-date understanding of the integration of AI techniques in ITSs by conducting a systematic review. The review focused on answering the following research questions: What AI techniques are integrated into ITSs? What are the objectives of employing AI techniques in ITSs? What are students’ specific attributes associated with ITSs? Research papers from 2021 to 2023 (the last three years) were extracted from the Scopus database, and 21 papers were included in the study based on the inclusion and exclusion criteria. The systematic review was conducted based on the PRISMA 2020 statement. The study reveals that Machine Learning is the predominant category deployed in the reviewed ITSs, followed by Natural Language Processing (NLP) and Bayesian techniques. The primary objectives motivating the adoption of AI include predicting student performance and generating adaptive feedback/hints. Notably, student characteristics such as performance, behavior, and prior knowledge emerge as crucial considerations within ITSs.
According to the World Health Organization, the number of deaths due to road accidents keeps increasing every year, and over 90
In this work, we proposed a new algorithm for optimizing the parameter generalized Legendre moments invariants using the artificial bee colony (ABC) optimization algorithm and support vector machines (SVM) to analysis the performance of reconstruction and classification of images, where we can derive the new set of invariants based on the geometric basis and the generalized Legendre polynomials. However, it was very important to optimize the parameter in order to obtain a good result. We presented a systematic selection method to find the optimal values of fractional parameters for image analysis applications. Equally important, we introduced an adaptive scheme defining fractional parameters based on the characteristics of the image. The use of the (ABC) algorithm to determine the optimal parameter of Legendre polynomials (LPs), this which improves the quality of the of the reconstruction. The results obtained show, on the one hand that the method based on the ABC algorithm offers better image reconstruction on the other hand, the comparison with other algorithms indicates the power of the proposed method. Finally, the method proposed is inexpensive in computationally and very fast, it is useful in several computer vision applications.
Container terminals play a pivotal role in global trade and are crucial for a country’s economy. They serve as vital links in global supply chains, connecting continents and facilitating exchange. The optimization of container terminal processes is essential for attracting investment, enhancing productivity, and contributing to the country’s overall GDP. Amidst increasing cargo volume and complexity, efficient terminal operations are paramount. This research identifies and analyzes the significant actors involved in container terminal processes, including shipping lines, terminal operators, logistics companies, and regulatory actors. The study seeks to uncover inefficiencies and bottlenecks in current practices by modeling their interactions and roles through BPMN techniques. This research offers valuable insights into streamlining operations, reducing turnaround times, and increasing terminal productivity.
