The growing demand for online programming education underscores the necessity for engaging and efficient learning tools. While interactive exercises enhance student engagement and outcomes, creating them presents chal...
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This paper reports on a single-fed compact slotted implantable patch antenna for biomedical applications. The proposed antenna operates at 915 MHz in the industrial, scientific, and medical (ISM) band. The antenna des...
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The McEliece cryptosystem has emerged as a finalist in Round 4 of the NIST Post-Quantum Cryptography (PQC) competition. The Shor algorithm underscores the potential vulnerability of cryptographic primitives to quantum...
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Recent statistics indicate that electromagnetic metamaterial absorbers are of great interest due to their ability to absorb nearly all incoming electromagnetic waves within a specific frequency range. The primary fact...
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Retinal vessel segmentation in Optical Coherence Tomography Angiography (OCTA) images constitutes a crucial task in ophthalmology and medical image analysis. It involves identifying and delineating blood vessels withi...
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Fetal arrhythmias can lead to cardiac failure or death;thus, early detection is crucial but challenged by noise and artifacts. This paper investigates fetal arrhythmia detection using time, frequency, and non-linear H...
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Thyroid disease represents a significant contributor to challenges in both medical diagnosis and the prediction of its onset, making it a complex area of study within medical research. This research thoroughly analyse...
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Classification and regression algorithms based on k-nearest neighbors (kNN) are often ranked among the top-10 Machine learning algorithms, due to their performance, flexibility, interpretability, non-parametric nature...
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Classification and regression algorithms based on k-nearest neighbors (kNN) are often ranked among the top-10 Machine learning algorithms, due to their performance, flexibility, interpretability, non-parametric nature, and computational efficiency. Nevertheless, in existing kNN algorithms, the kNN radius, which plays a major role in the quality of kNN estimates, is independent of any weights associated with the training samples in a kNN-neighborhood. This omission, besides limiting the performance and flexibility of kNN, causes difficulties in correcting for covariate shift (e.g., selection bias) in the training data, taking advantage of unlabeled data, domain adaptation and transfer learning. We propose a new weighted kNN algorithm that, given training samples, each associated with two weights, called consensus and relevance (which may depend on the query on hand as well), and a request for an estimate of the posterior at a query, works as follows. First, it determines the kNN neighborhood as the training samples within the kth relevance-weighted order statistic of the distances of the training samples from the query. Second, it uses the training samples in this neighborhood to produce the desired estimate of the posterior (output label or value) via consensus-weighted aggregation as in existing kNN rules. Furthermore, we show that kNN algorithms are affected by covariate shift, and that the commonly used sample reweighing technique does not correct covariate shift in existing kNN algorithms. We then show how to mitigate covariate shift in kNN decision rules by using instead our proposed consensus-relevance kNN algorithm with relevance weights determined by the amount of covariate shift (e.g., the ratio of sample probability densities before and after the shift). Finally, we provide experimental results, using 197 real datasets, demonstrating that the proposed approach is slightly better (in terms of F-1 score) on average than competing benchmark approaches for mit
There is an urgent need to control global warming caused by humans to achieve a sustainable ***_(2) levels are rising steadily,and while countries worldwide are actively moving toward the sustainability goals proposed...
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There is an urgent need to control global warming caused by humans to achieve a sustainable ***_(2) levels are rising steadily,and while countries worldwide are actively moving toward the sustainability goals proposed during the Paris Agreement in 2015,we are still a long way to go from achieving a sustainable mode of global *** increased popularity of cryptocurrencies since the introduction of Bitcoin in 2009 has been accompanied by an increasing trend in greenhouse gas emissions and high electrical energy *** energy tracking studies(e.g.,Digiconomist and the Cambridge Bitcoin Energy Consumption Index(CBECI))have estimated energy consumption ranges from 29.96 TWh to 135.12 TWh and 26.41 TWh to 176.98 TWh,respectively for Bitcoin as of July 2021,which are equivalent to the energy consumption of countries such as Sweden and *** latest estimate by Digiconomist on carbon footprints shows a 64.18 MtCO_(2) emission by Bitcoin as of July 2021,close to the emissions by Greece and *** review compiles estimates made by various studies from 2018 to *** compare the energy consumption and carbon footprints of these cryptocurrencies with countries around the world and centralized transaction methods such as *** identify the problems associated with cryptocurrencies and propose solutions that can help reduce their energy consumption and carbon ***,we present case studies on cryptocurrency networks,namely,Ethereum 2.0 and Pi Network,with a discussion on how they can solve some of the challenges we have identified.
In this work, a novel output feedback control strategy for Euler-Lagrange systems that addresses the inherent uncertainties of nonlinear dynamics and feedback limitations is presented. The proposed method employs the ...
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