This study introduces a novel multi-context approach to water quality assessment by examining the factors influencing Chemical Oxygen Demand in Varthur Lake, Bangalore. Integrating traditional regression techniques wi...
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This study presents the architecture and performance evaluation of a high-capacity free-space optical (FSO) communication system that makes use of dense wavelength division multiplexing (DWDM) and a 1.28 Tb/s link. Th...
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With the advancement of technologies like cloud computing and Internet of things, there are enormous areas in which these can be utilized. One such domain is Smart City concept utilizing or based on the architecture o...
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In recent years, deep fakes have become increasingly prevalent and sophisticated, raising concerns about misinformation. This has led to a surge in research on deep fake creation, detection techniques, and related dat...
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Learners with a limited budget can use supervised data subset selection and active learning techniques to select a smaller training set and reduce the cost of acquiring data and training machine learning (ML) models. ...
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Learners with a limited budget can use supervised data subset selection and active learning techniques to select a smaller training set and reduce the cost of acquiring data and training machine learning (ML) models. However, the resulting high model performance, measured by a data utility function, may not be preserved when some data owners, enabled by the GDPR's right to erasure, request their data to be deleted from the ML model. This raises an important question for learners who are temporarily unable or unwilling to acquire data again: During the initial data acquisition of a training set of size k, can we proactively maximize the data utility after future unknown deletions? We propose that the learner anticipates/estimates the probability that (i) each data owner in the feasible set will independently delete its data or (ii) a number of deletions occur out of k, and justify our proposal with concrete real-world use cases. Then, instead of directly maximizing the data utility function, the learner can maximize the expected or risk-averse post-deletion utility based on the anticipated probabilities. We further propose how to construct these deletion-anticipative data selection (DADS) maximization objectives to preserve monotone submodularity and near-optimality of greedy solutions, how to optimize the objectives and empirically evaluate DADS' performance on real-world datasets. Copyright 2024 by the author(s)
In order to lower death risks, provide the most effective course of treatment, and improve community healthcare, the majority of recent research has concentrated on examining prevalent illnesses in the population. One...
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Outlier is a value that lies outside most of the other values in a dataset. Outlier exploration has a huge importance in almost all the industry applications like medical diagnosis, credit card fraudulence and intrusi...
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Data collection and analysis are critical aspects of various business processes. However, these tasks can be time-consuming, prone to errors, and delays employee productivity when done manually, especially when we hav...
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In today's world, as ecological challenges continue to rise, the importance of environmental monitoring and conservation cannot be overstated. These efforts play a vital role in countering the adverse impacts of h...
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Climate change prognosis is one of the most important fields of study that has attracted much interest in the last decade because of its implication for environmental and social development. The developments in remote...
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