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Globally, many research works are going on to study the infectious nature of COVID-19 and every day we learn something new about it through the flooding of the huge data that are accumulating hourly rather than daily which instantly opens hot research topics for artificial intelligence researchers. However, the public’s concern by now is to find answers for two questions; 1) when this COVID-19 pandemic will be over? and 2) After coming to its end, will COVID-19 return again in what is known as a second rebound of the pandemic?. This research developed a predictive model that can estimate the expected period of time that the virus can possibly stopped and the risk of a second rebound of COVID-19 pandemic. Therefore, this study considered SARIMA model to predict the spread of the virus on several selected countries and is used for pandemic life cycle and end date predictions. The study can be applied to predict the same for other countries as the nature of the virus is the same everywhere. The advantages of this study are that it helps the governments in making decisions and planning now for the future, reduces anxiety and prepares the mentality of people for the next phases of the pandemic. The most striking finding to emerge from this experimental and simulation study is that the proposed algorithm show that the expected COVID-19 infections for the top countries of highest number of confirmed case will slowdown in October, 2020. Moreover, our study forecasts that there may be a second rebound of the pandemic in a year time, if the current taken precautions are eased completely. We have to consider the uncertain nature of the current COVID-19 pandemic and the growing inter-connected and complex world, what are ultimately required are the flexibility, robustness and resilience to cope up the unexpected future events and scenarios.

Safe driving and reducing the number of accidents victims have been the main motivations for researchers and automotive companies for decades. Today, humanity is very close to make the old dream of fully autonomous vehicles a reality, thanks to the rapid spread of AI (artificial intelligence) and the evolution of semiconductor technologies. But the real problem here is the increasing demand for computational power and that of course will increase power requirements, hence it will not be suitable for autonomous driving applications. GPU is not suitable for solving this problem due to its power consumption as well as heat generation. On the other hand, CPU also does not satisfy the performance requirements. For the above condition, FPGA (Field Programmable Gate Array) has drawn attention as a hardware accelerator since it features high performance with low power consumption. This paper reviews the common solutions involving artificial intelligence implemented on FPGA for autonomous vehicle applications. Research, development, and current trends related to the topic are emphasized.

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This paper presents a literature review about Particle Swarm Optimization (PSO), Firework, Firefly, Clonal Selection, and Cuckoo Search algorithms, which are among the most common natural-inspired optimization algorithms. These algorithms were tried on different benchmark functions. The obtained results were analyzed, and the performance was compared. The results showed that PSO and Firefly Search algorithms provided the best performance in the studied cases.