The research areas in the field of UAVs have increased considerably during the last years, the research in this field is driven by the specific needs of each organization that conduct the research, There are two main research areas, the first is the operational and it is conducted by the governmental institutions and the universities, and the second is the technological and it is conducted mainly by the companies, This paper discusses the current technological research topics in the field of UAVs, focusing on the fuzzy-logic based methods which are employed in many control problems to increase the level of autonomy, the fuzzy-logic is considered as a promising hot subject which contains many active research topics and multiple potential tools for solving complex control problems to extend the UAV capabilities to perform different functions like Optimal path planning, Collision avoidance, Trajectory motion and path following autonomously without the need of the human pilot with the minimum human supervision, the paper illustrates the different levels, functions and challenges of autonomy and a comparative analysis is conducted to analyze four potential directions which are considered to be promising areas for fuzzy-logic based approaches. It also highlights the two main areas of AI research in the field of UAV autonomous flight, 1) the imitation of the human pilot and 2) the high-level applications like image evaluation, and how to tackle some of the problems in these areas with aid of fuzzy-logic based machine learning algorithms.
The rapidly spreading COVID-19 disease had already infected more than 190 countries. As a result of this scenario, nations everywhere monitored confirmed cases of infection, cures, and fatalities and made predictions about what the future would hold. In the event of a pandemic, governments had set limit rules for the spread of the virus and save lives. Multiple computer methods existed for forecasting epidemic time series. Deep learning was one of the most promising methods for time-series prediction. In this research, we propose a model for predicting the spread of COVID-19 in Egypt based on deep learning sequence-to-sequence regression, which makes use of data on the population mobility reports. The presented model utilized a new combined dataset from two different sources. The first source is Google population mobility reports, and the second source is the number of infected cases reported daily “world in data” website. The suggested model could predict new cases of COVID-19 infection within 3–7 days with the least amount of prediction error. The proposed model achieved 96.69% accuracy for 3 days of prediction. This study is noteworthy since it is one of the first trials to estimate the daily influx of new COVID-19 infections using population mobility data instead of daily infection rates.
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