AI models are reshaping software performance testing. Machine Learning and Deep Learning algorithms automate test scenario generation, enable real-time monitoring, and predict performance issues. They facilitate dynamic load balancing, anomaly detection, testing automation, and offer performance optimization recommendations.
This article considers non-parametric models based on feature space modeling in the context of machine learning. The main machine learning models, their advantages and disadvantages are analyzed. The term “non-parametric feature space modeling model” has been considered in detail and compared with other machine learning models. The advantages of these models are justified in comparison with other approaches. The paper contains an analysis that confirms the advantages of using non-parametric feature space modeling models in machine learning tasks.
Generative AI models have started to transform the software industry, driving innovation, and automating tasks previously deemed complex. This overview delves into the evolution, applications, and potential of these models, highlighting their capability to create novel content, enhance software development processes, and forecast industry trends. As these models continue to mature, the software industry stands on the cusp of a redefined future where human-machine collaboration will shape ground-breaking advancements.
The main issues considered in this article: the development of recreational areas during the period of overcoming the consequences of the war on the example of Ukraine; the importance and content of pre-project studies regarding the implementation of programs and projects of recreational development of territories; the need to use methods of analysis and comparison when researching the experience of successful countries in the field of recreational development, in particular Southern Europe; attraction of investments, loans, budgetary and extra-budgetary funds for the innovative development of recreational areas; improvement of tax legislation with the aim of providing temporary preferences to recreational areas for the implementation of necessary programs and projects.
Along recent years an approach of Artificial Locust Swarm Routing (ALSR) algorithm was developed for flights planning in Europe and for rules provided in EuroControl area of responsibility. Most researches were strictly related to IFR type of flights. However, there is a big area of interest in private aviation for short local flights under the top of VFR flight levels (FL195 and below). These flights have much specificity for planning and ALSR algorithm is found capable of solving these problems also, using this same idea of the algorithm like it was developed for IFR flights. One may see that the algorithm shows rather good results for such difficult area like Swiss Alps valleys and canyons.
One of the most expected and the most desired implementation of modern technologies is the concept of a Smart City. Meanwhile there are many buzz-words known to describe the ability of implementation of many approaches to provide control for Smart City. Many of such technologies had become known for Industry 4.0 concept, including 5G mobile Internet and autonomous aerial vehicles (so called drones). Nevertheless, there is an open question of how to provide and support limits where and when UAV (unmanned aerial vehicle) should be able or not to fly over the city. Naturally, the self-driven drones equipped with a batch of sensors and able to avoid some obstacles would not be enough for everyday use within Smart City. Current paper is focused on discussion of possible solution to provide UAV with up-to-date data on restrictions, that may be required in a modern city.