The rapid evolution of e-learning platforms, propelled by advancements in artificial intelligence (AI) and machine learning (ML), presents a transformative potential in education. This dynamic landscape necessitates an exploration of AI/ML integration in adaptive learning systems to enhance educational outcomes. This study aims to map the current utilization of AI/ML in e-learning for adaptive learning, elucidating the benefits and challenges of such integration and assessing its impact on student engagement, retention, and performance. A comprehensive literature review was conducted, focusing on articles published from 2010 onwards, to document the integration of AI/ML in e-learning. The review analyzed 63 articles, employing a systematic approach to evaluate the deployment of adaptive learning algorithms and their educational implications. Findings reveal that AI/ML algorithms are instrumental in personalizing learning experiences. These technologies have been shown to optimize learning paths, enhance engagement, and improve academic performance, with some studies reporting increased test scores. The integration of AI/ML in e-learning platforms significantly contributes to the personalization and effectiveness of the educational process. Despite challenges like data privacy and the complexity of AI/ML systems, the results underscore the potential of adaptive learning to revolutionize education by catering to individual learner needs.

Recent technological advancements in e-learning platforms have made it easy to store and manage students’ related data, such as personal details, initial grade, intermediate grades, final grades, and many other parameters. These data can be efficiently processed and analyzed by intelligent techniques and algorithms to generate useful insights into the students’ performance, such as to identify the factors impacting the progress of successful students or the performance of the students who are struggling in their courses and are at risk of failing. Such a framework is scarce in the current literature. This study proposes an interpretable framework to generate useful insights from the data produced by e-learning platforms using machine learning algorithms. The proposed framework incorporates predictive models, as well as regression and classification models to analyze multiple factors of student performance. Classification models are used to systematize normal and at-risk students based on their academic performance, with high precision and accuracy. Regression analysis is performed to determine the inherent linear and nonlinear relationships between the academic outcomes of the students acting as the target or independent variables and the performance indicative features acting as dependent variables. For further analysis, a predictive modeling problem is considered, where the performance of the students is anticipated based on their commitment to a specific course, their performance for the whole course, and their final grades. The efficiency of the proposed framework is also optimized by reliably tuning the algorithmic parameters. Furthermore, the performance is accelerated by empowering the system with a GPU-based infrastructure. Results reveal that the proposed interpretable framework is highly accurate and precise and can identify factors that play a vital role in the students’ success or failure.

Floods are natural phenomena that profoundly affect the development of human society, as they are the most widespread disasters in the world and they cause the greatest property and human loss. The flood warning system presented in this paper provides a reliable and effective solution, which is complementary to existing traditional warning systems; it employs information and communication technology and specialized devices.

Natural disasters profoundly affect the development of human society, they are the most pervasive disasters in the world and they cause the greatest property and human loss. Considering the natural disasters that have struck Romania these years, we believe that is it is absolutely necessary to develop a spatial [elements] decision support system, which would prevent - as much as possible - natural disasters from occurring or would help mitigate their effects. All these objectives are unattainable without effectively applying information and communication technology in the field of natural disasters.

In this paper, we present the benefit of using deep learning time-series analysis techniques in order to reduce computing resource usage, with the final goal of having greener and more sustainable data centers. Modern enterprises and agile ways-of-working have led to a complete revolution of the way that software engineers develop and deploy software, with the proliferation of container-based technology, such as Kubernetes and Docker. Modern systems tend to use up a large amount of resources, even when idle, and intelligent scaling is one of the methods that could be used to prevent waste. We have developed a system for predicting and influencing computer resource usage based on historical data of real production software systems at CERN, allowing us to scale down the number of machines or containers running a certain service during periods that have been identified as idle. The system leverages recurring neural network models in order to accurately predict the future usage of a software system given its past activity. Using the data obtained from conducting several experiments with the forecasted data, we present the potential reductions on the carbon footprint of these computing services, from the perspective of CPU usage. The results show significant improvements to the computing power usage of the service (60% to 80%) as opposed to just keeping machines running or using simple heuristics that do not look too far into the past.