Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential for using it in many other applications in genome analysis.
Due to the outbreak of Covid-19 pandemic, activities in most sectors- be it business, education or even healthcare- are taking place in an online rather than in an inline style, and as a result, Internet traffic has increased drastically. Recent studies have highlighted that internet traffic has grown by 70% to 300% since March 2020. According to a recent CNN news article (
https://www.cnn.com/2020/03/19/tech/netflix-internet-overload-eu/index.html
), popular content providers such as Netflix and YouTube are slowing down in North-America and Europe to keep the internet from breaking. With that being addressed, the existing network deployment and solutions, even with the fifth generation mobile communication (5G) partial deployment, are currently under a huge burden. This work intends to review the integration of two of the most innovative network research areas, Software-defined Networks (SDN) and the Internet of Things (IoT). The IoT aims to interface questions over the Internet while the SDN offers orchestration for network management by decoupling the control plane and the data plane. In this article, we present the state of the art of Software-defined networking and the Internet of Things discussing the integrated architectures, challenges, and designs. Also, we discuss two proposals targeting the QoS Key Performance Indicators (KPIs) in IoT via SDN mobile edge computing along with a few directions of possible research that could fill in gaps in these domains.
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