In the current era of big data, high volumes of a wide variety of valuable data of different veracity are generated or collected at a high velocity. A rich source of these big data is the stock market. Since the inception of the stock market, people have been trying to "beat" it for the purpose of monetary gain. A stock market is an exchange where people trade shares of companies, also called stocks. The purpose of the exchange is to make it easy to match buyers and sellers together to make transactions. The usual goal of someone participating in the stock market it to generate profit through the buying and selling of stocks. The main way people accomplish this is by buying a stock, waiting anywhere from seconds to decades, and then hopefully selling for more than they bought it for. This is where the common term "buy low, sell high" comes from. There are many factors (e.g., hurricanes) that may affect the stock price. In this paper, we present a computational intelligent tool that applies fuzzy logic-based data analytics to predict the effect of hurricanes on the stock market.

Recent developments in smart assistants and smart home automation are lately attracting the interest and curiosity of consumers and researchers. Speech enabled virtual assistants (often named smart speakers) offer a wide variety of network-oriented services and, in some cases, can connect to smart environments, thus enhancing them with new and effective user interfaces. However, such devices also reveal new needs and some weaknesses. In particular, they represent faceless and blind assistants, unable to show a face, and therefore an emotion, and unable to `see' the user. As a consequence, the interaction is impaired and, in some cases, ineffective. Moreover, most of those devices heavily rely on cloud-based services, thus transmitting potentially sensitive data to remote servers. To overcome such issues, in this paper we combine some of the most advanced techniques in computer vision, deep learning, speech generation and recognition, and artificial intelligence, into a virtual assistant architecture for smart home automation systems. The proposed assistant is effective and resource-efficient, interactive and customizable, and the realized prototype runs on a low-cost, small-sized, Raspberry PI 3 device. For testing purposes, the system was integrated with an open source home automation environment and ran for several days, while people were encouraged to interact with it, and proved to be accurate, reliable and appealing.