The medical device used for auscultation is known as a stethoscope. An ordinary acoustics-based stethoscope is limited in its ability to provide high-quality sound in noisy environments. To deal with this problem digi...
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Physical or geographic location proves to be an important feature in many data science models, because many diverse natural and social phenomenon have a spatial component. Spatial autocorrelation measures the extent t...
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ISBN:
(数字)9781728183169
ISBN:
(纸本)9781728183176
Physical or geographic location proves to be an important feature in many data science models, because many diverse natural and social phenomenon have a spatial component. Spatial autocorrelation measures the extent to which locally adjacent observations of the same phenomenon are correlated. Although statistics like Moran's I and Geary's C are widely used to measure spatial autocorrelation, they are slow: all popular methods run in Ω(n 2 ) time, rendering them unusable for large data sets, or long time-courses with moderate numbers of points. We propose a new S A statistic based on the notion that the variance observed when merging pairs of nearby clusters should increase slowly for spatially autocorrelated variables. We give a linear-time algorithm to calculate S A for a variable with an input agglomeration order (available at https://***/aamgalan/spatial_autocorrelation). For a typical dataset of n ≈ 63,000 points, our S A autocorrelation measure can be computed in 1 second, versus 2 hours or more for Moran's I and Geary's C. Through simulation studies, we demonstrate that S A identifies spatial correlations in variables generated with spatially-dependent model half an order of magnitude earlier than either Moran's I or Geary's C. Finally, we prove several theoretical properties of S A : namely that it behaves as a true correlation statistic, and is invariant under addition or multiplication by a constant.
Identifying the process of participatory design (PD) approach to cater for students' mutual needs in recognizing their educational game design aspirations for schools and higher education and applying their game k...
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Recent advancements in semi-supervised deep learning have introduced effective strategies for leveraging both labeled and unlabeled data to improve classification performance. This work proposes a semi-supervised fram...
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Data-intensive applications impact many domains, and their steadily increasing size and complexity demands high-performance, highly usable environments. We integrate a set of ideas developed in various data science an...
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There are medical hair removal and esthetic's hair removal in the current hair removal approach. Hair loss damages the tissues from which hair grows (hair cells, dermal papilla). Esthetic's hair removal has mo...
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The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empi...
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ISBN:
(纸本)9781665491075
The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empirical results indicate that LSTM is the optimal model for predicting Bitcoin price and trend with the prediction accuracy of 88.9%. Our study serves as a stepping stone for novice cryptocurrency investors and future studies of more advanced and sophisticated algorithms. Finally, given that the ideal model for predicting the price of cryptocurrencies is still a topic of controversy, the findings of this study will serve as a valuable empirical resource for future studies.
The detection and recognition of automatic license plates (ALPs) is an important task for traffic surveillance and parking management systems, as well as for sustaining the flow of modern civic life. It’s been sugges...
The detection and recognition of automatic license plates (ALPs) is an important task for traffic surveillance and parking management systems, as well as for sustaining the flow of modern civic life. It’s been suggested that there are a variety of ways to detect and recognize ALPs thus far Image processing and machine learning techniques are typically used in these approaches. For object detection and license plate identification, this article reviews most of the approaches. In order to improve the suitability of the input pictures for subsequent processing, many pre-processing approaches including Gaussian filtering and adaptive image contrast augmentation were examined. Deep semantic segmentation networks and deep learning techniques are also utilized to find the license plate areas in the input picture… For example, deep encoder-decoder network architecture and convolutional neural network (CNN) models used to recognize license plate.
Metastasis is the major cause of death in breast cancer patients. The interaction with mesenchymal stem/stromal cells promotes breast cancer metastasis. To investigate cellular engulfment in vitro, we have developed a...
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Mechanical ventilators are the instruments that assist breathing of the patients having respiratory diseases e.g., pneumonia and coronavirus disease 2019 (COVID-19). This paper presents a modified lung model under vol...
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