In recent months, coronavirus disease 2019
(COVID-19) has infected millions of people worldwide. In
addition to the clinical tests like reverse transcription-
polymerase chain reaction (RT-PCR), medical imaging
techniques such as computed tomography (CT) can be used as a
rapid technique to detect and evaluate patients infected by
COVID...
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Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is becoming a hot topic due to the direct training of machines with less interaction with a human.The scenario of manual feeding of the machine is changed in the modern era, it will lear...
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Based on environmental, legal, social, and economic factors, reverse logistics and closed-loop supply chain issues have attracted attention among both academia and practitioners. This attention is evident by the vast number of publications in scientific journals which have been published in recent years. Hence, a comprehensive literature review of ...
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Network embedding assigns nodes in a network to low dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods,...
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Wireless Sensors Networks (WSN) is the self-configured wireless network which consists of a huge measure of resource-restrained Sensor Nodes (SN). In WSN, the key parameters are effectual energy utilization and security. The adversary could send false information because of the Malicious Nodes' (MNs') presence. Thus, to shun security threats, it is...
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This article proposes a novel modeling method for the stochastic nonlinear degradation process by using the relevance vector machine (RVM), which can describe the nonlinearity of degradation process more flexibly and accurately. Compared with the existing methods, where degradation processes are modeled as the Wiener process with a nonlinear drift ...
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Automated Machine Learning (AutoML) systems have been shown to efficiently build good models for new datasets. However, it is often not clear how well they can adapt when the data evolves over time. The main goal of this study is to understand the effect of concept drift on the performance of AutoML methods, and which adaptation strategies can ...
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