Ransomware attacks are a growing threat to cyber-physical systems (CPS), capable of causing financial losses, downtime, and even physical harm. Using machine learning to detect and prevent these attacks is a promising...
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The Internet of Medical Things (IoMT) revolutionizes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant secur...
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Study buddy is an all-in-one package for your studies. We provide an automatically generated timetable as well as pdfs and video links all for Free for our students of class 10th, 12th and, those appearing for JEE. Th...
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The turnover of a start-up or company depends highly on the employee satisfaction. Onboarding is a way to familiarize the employee with the work environment. The objective of this paper to find the critical tasks of t...
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A novel design of a video see-through super multi-view near-eye display (VST-SMV-NED) with a waveguide-type light source (WLS) and a ferroelectric liquid crystal on silicon (FLCoS) is proposed. The proposed method pre...
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Situational awareness has vital importance for next-generation fighter aircraft, and it enables pilots to assess, anticipate, and respond adept.y to dynamic combat scenarios. Pop-up threats may occur at any moment in ...
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ISBN:
(纸本)9798350349610
Situational awareness has vital importance for next-generation fighter aircraft, and it enables pilots to assess, anticipate, and respond adept.y to dynamic combat scenarios. Pop-up threats may occur at any moment in the uncertainty of warfare. That's why, situational awareness calculations have to be executed in real-time, and the time complexity of situational awareness functions becomes critical. In this work, a machine learning-based clustering method that optimizes the time complexity of situational awareness functions with offline preprocessing of radar cross-section matrices is proposed. Survivability assessment is one of the crucial functionalities of situational awareness, and it plays a key role in modern air warfare. Radar cross-section is the most important low observability feature that is directly related to the survivability of aircraft because the probability of being detected or tracked affects survivability in an unfavorable manner. In order to assess survivability by taking radar cross-section into account through an air mission route, a significant number of iterations are required with raw radar cross-section matrices which represent the radar cross-section value for each pair of aspect azimuth and elevation angles. Clustering radar cross-section matrices into rectangle-like regions is a brilliant idea since stealth aircraft generally have sharp edges, flat surfaces, and rectangular shapes, and using clusters can eliminate the need for a large number of iterations. In this work, we proposed a machine learning-based clustering method for radar cross-section matrices;we showed how clustering radar cross-section matrices reduces the time complexity of survivability assessment calculations for next-generation fighter aircraft. Bias and mean bias error values for each cluster are calculated using the raw radar cross-section matrix as the ground truth and compared with other radar cross-section modeling approaches in the literature. Furthermore, perfo
Privacy Agreements are critically important from the company as well as the user's point of view. Upon surveying, we realise there are three most common issues faced by the user while trying to read their privacy ...
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Robotic Process Automation (RPA) has flourished since the recent wave of digital transformation. To some RPA may sound intimidating and ominous at first, but in retrospect, it is an extremely practical solution for tr...
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Hyperspectral Imaging, employed in satellites for space remote sensing, like HYPSO-1, faces constraints due to few labeled data sets, affecting the training of AI models demanding these ground-truth annotations. In th...
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As the spine innovation of decentralized cryptocurrencies, blockchain has additionally proclaimed numerous applications in different fields, for example, resource allocation in cloud computing, Internet of Things (IoT...
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