For most real-world data streams, the concept about which data is obtained may shift from time to time, a phenomenon known as concept drift. For most real-world applications such as nonstationary time-series data, concept drift often occurs in a cyclic fashion, and previously seen concepts will reappear, which supports a unique kind of concept drift known as recurring concepts. A cyclically drifting concept exhibits a tendency to return to previously visited states. Existing machine learning algorithms handle recurring concepts by retraining a learning model if concept is detected, leading to the loss of information if the concept was well learned by the learning model, and the concept will recur again in the next learning phase. A common remedy for most machine learning algorithms is to retain and reuse previously learned models, but the process is time-consuming and computationally prohibitive in nonstationary environments to appropriately select any optimal ensemble classifier capable of accurately adapting to recurring concepts. To learn streaming data, fast and accurate machine learning algorithms are needed for time-dependent applications. Most of the existing algorithms designed to handle concept drift do not take into account the presence of recurring concept drift. To accurately and efficiently handle recurring concepts with minimum computational overheads, we propose a novel and evolving ensemble method called Recurrent Adaptive Classifier Ensemble (RACE). The algorithm preserves an archive of previously learned models that are diverse and always trains both new and existing classifiers. The empirical experiments conducted on synthetic and real-world data stream benchmarks show that RACE significantly adapts to recurring concepts more accurately than some state-of-the-art ensemble classifiers based on classifier reuse.

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The COVID -19 outbreak since inception has put the whole world in an unprecedented difficult situation by bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spreading across 212 countries globally, an increasing number of infected cases and death tolls rose to 117, 469, 698, and 2,605,637 (as of March 7, 2021), this remains a real threat to the public health system. This paper presents a novel design for the frequency-domain reconfigurable antenna at Ku and K-bands for satellite-internet of thing (IoT) tracking applications. Four reconfigurable antenna is proposed with the use of four different switch mechanisms. Furthermore, switches are used to change resonance frequency to Ku- and K-bands on the antenna surface with four stages. With the help of the 3D electromagnetic computer simulation technology (CST) studio suite, we model the proposed antenna, perform the simulation with a frequency-domain solver, and validate the results with a time-domain solver with both results obtained in agreement as the proposed reconfigurable antenna operates over a wide frequency range for the satellite-IoT network to track COVID-19 pandemic. The COVID -19 outbreak since inception has put the whole world in an unprecedented difficult situation by bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spreading across 212 countries globally, an increasing number of infected cases and death tolls rose to 117, 469, 698, and 2,605,637 (as of March 7, 2021), this remains a real threat to the public health system. This paper presents a novel design for the frequency-domain reconfigurable antenna at Ku and K-bands for satellite-internet of thing (IoT) tracking applications. Four reconfigurable antenna is proposed with the use of four different switch mechanisms. Furthermore, switches are used to change resonance frequency to Ku- and K-bands on the antenna surface with four stages. With the help of the 3D electromagnetic computer simulation technology (CST) studio suite, we model the proposed antenna, perform the simulation with a frequencydomain solver, and validate the results with a time-domain solver with both results obtained in agreement as the proposed reconfigurable antenna operates over a wide frequency range for the satellite-IoT network to track COVID-19 pandemic.