ABSTRACT
The global aviation industry has been massively impacted by the COVID crisis which lead to a collapse of the traffic and urged the need for a climate-neutral aviation. The sector is undergoing great mutations with the emergence of an urban air mobility based on eVTOL modules, electric and hydrogen airplanes for the short -haul transport and fuel efficient engines combined with sustainable aviation fuels for the long-haul flights. For years, Safran Data Systems's turn-key solutions for data collection, recording, transmission and processing have given aircraft manufacturers an edge on their flight test campaigns. Based on its expertise Safran Data Systems aims at assisting aircraft manufacturers in the validation and certification of tomorrow's concepts by expanding its portfolio on flight test instrumentation solutions for electric, hydrogen and emission efficient aircrafts. Proof-of-concepts are conducted to explore new innovative solutions such as HVDC electrical systems monitoring. © 2021 International Foundation for Telemetering. All rights reserved.
ABSTRACT
This paper presents a model based on a Deep Learning approach to aid in improving our assessment of the risk of death in COVID-19 patients only based on their clinical record when admitted. It is aimed to provide an alternative fast tool for doctors and researchers to focus on a rapid selection of patients with a high likelihood of death, which is a critical aspect having in mind the growing infection dynamics of the current and perhaps future pandemics. The dataset used in this research is open-access, available for algorithm benchmarking, and represents the knowledge of the cases examined in Mexico before the immunization campaigns. The massive amount of information used to feed the algorithm provides robustness and aids in detecting the principal patterns involved in the data. The model is based on a Deep Neuronal Network, which uses different activation functions and several neurons in each hidden layer for getting a stable performance, and was tested in both a validation set and test set, obtaining a satisfactory and reliable accuracy of about 93% for the survival prediction.
ABSTRACT
Nowadays, the evolution of artificial intelligence is present in almost every field of the modern science and technology, because it is a powerful tool for analysis and quick decision making, enabling capacity to automate various processes in companies, government institutions, or research groups through one of the preferred techniques for automated software processes. Thus, the use of this kind tool has allowed us to develop a Chatbot (robot) that has the ability to simulate the conversation with a human being, to answer very specific questions [1]. Artificial intelligence has the ability to acquire, and achieve goals in the computer world, trying to emulate the way human intelligence process information. Currently, there are several creations in Chatbot that allows automating recurring processes, can decrease the response times of an activity, guarantee the availability of the service, and or omit the presence of a person, and it is very useful for process where there is a certain type of prevented behavior. In the health area, it is essential to have an assistant who knows how to give information at any time of the day, specially in a worldwide pandemic crises, but in funny hours is more complicated to have an online attendant to answer specific questions about the illness. The present work shows the results of an implementation of a Chatbot based on Artificial Intelligence to help the Medical Department at UDLA University in Ecuador, to detect possible cases of COVID-19, and for help to alleviate the saturated health system, and more importantly to collect information to prevent the dissemination of the infectious disease, we have created a platform to interact with possible infected persons who get the support from the University facilities. © 2020 IEEE.