Communication networks are expanding rapidly and becoming increasingly complex. As a consequence, the conventional rule-based algorithms or protocols may no longer perform at their best efficiencies in these networks. Machine learning (ML) has recently been applied to solve complex problems in many fields, including finance, health care, and business. ML algorithms can offer computational models that can solve complex communication network problems and consequently improve performance. This paper reviews the recent trends in the application of ML models in communication networks for prediction, intrusion detection, route and path assignment, Quality of Service improvement, and resource management. A review of the recent literature reveals extensive opportunities for researchers to exploit the advantages of ML in solving complex performance issues in a network, especially with the advancement of softwaredefined networks and 5G.

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Landsat 8 was launched in 2013 by the National Aeronautics and Space Administration (NASA). On board of the Landsat 8 is the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). Data for visible, panchromatic band, short-wave infrared spectral bands are collected by the OLI while TIRS collect images in the thermal region. As data for Landsat 8 is available to be used for public, researchers have utilized the data for numerous applications. However, to the best of our knowledge, there is yet a review paper on the various applications of Landsat 8 data. Hence, this paper presented an innovative survey on Landsat 8 data in the application of agriculture and forestry, land use and mapping, geology, hydrology, coastal resources and environmental monitoring. The potential of utilizing Landsat 8 data for power utility companies is also discussed in this paper. As Landsat 8 data is predicted to be available for more years to come, this paper provides insight for researchers to utilize the data better for their research.

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