A popular unsupervised learning method, known as clustering, is extensively used in data mining, machine learning and pattern recognition. The procedure involves grouping of single and distinct points in a group in such a way that they are either similar to each other or dissimilar to points of other clusters. Traditional clustering methods are greatly challenged by the recent massive growth of data. Therefore, several research works proposed novel designs for clustering methods that leverage the benefits of Big Data platforms, such as Apache Spark, which is designed for fast and distributed massive data processing. However, Spark-based clustering research is still in its early days. In this systematic survey, we investigate the existing Spark-based clustering methods in terms of their support to the characteristics Big Data. Moreover, we propose a new taxonomy for the Spark-based clustering methods. To the best of our knowledge, no survey has been conducted on Spark-based clustering of Big Data. Therefore, this survey aims to present a comprehensive summary of the previous studies in the field of Big Data clustering using Apache Spark during the span of 2010–2020. This survey also highlights the new research directions in the field of clustering massive data.
Both software-defined networking and big data have gained approval and preferences from both industry and academia. These two important realms have conventionally been addressed independently in wireless cellular networks. The discussion taken into consideration in this study was to analyze the wireless cellular technologies with the contrast of efficient and enhanced spectral densities at a reduced cost. To accomplish the goal of this study, Welch's method has been used as the core subject. With the aid of previous research and classical techniques, this study has identified that the spectral densities can be enhanced at reduced costs with the help of the power spectral estimation methods. The Welch method gives the result on power spectrum estimation. By reducing the effect of noise, the Welch method is used to calculate the power spectral density of a signal. When data length is increased, Welch's method is considered the best as a conclusion to this paper because excellent results are yielded by it in the area of power spectral density estimation.
BACKGROUND The Internet of Things (IoT) functions through a wireless medium and holds the capability of transmitting data over the network without the considerable need for human interaction. The Covid-19 pandemic outbreak has transformed the functioning of healthcare centers and other associated circles. The remnants of the Covid-19 pandemic outbreak, like long Covid or post- Covid-19 syndrome, have necessitated the need to observe, detect and control specific healthcare fields to save lives. Because of this, these centers put in great effort and spent considerable resources on developing portable, cost-effective, and user-friendly IoT. The IoT-based systems can help continuously survey the population to detect any variants of the Covid-19 virus and to gather specific patient data in categorizing post-Covid-19 syndrome patients. This paper discusses the applications of IoT during a pandemic and post-pandemic too. OBJECTIVE This paper discusses the applications of IoT during a pandemic and post-pandemic too. METHODS The study also proposes a model based on IoT for handling population surveys during the present post-pandemic situation. RESULTS The Internet of Things concept is a revolutionary technological advancement closely linked with the wave of the ICT revolution. IoT assists the PHS technology in providing earlier and safer preventive care with minimum medical costs, better patient-oriented exercise, and greater sustainability. CONCLUSIONS IoT has allowed PHS to have the ability to improve daily lives in many revolutionarily different ways. CLINICALTRIAL n/a
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