Intelligent Stress Detection Using ECG Signals: Power Spectrum Imaging with Continuous Wavelet Transform and CNN by Rodrigo Mateo-Reyes, Irving A. Cruz-Albarran, Luis A. Morales-Hernandez

“…This study proposes a model based on depth-separable convolutional neural networks (DSCNN) to analyze heart rate variability (HRV) and detect stress. …”
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Asymptotic formula for the moments of Bernoulli convolutions by E. A. Timofeev

“…Asymptotic Formula for the Moments of Bernoulli Convolutions Timofeev E. A. Received February 8, 2016 For each λ, 0 < λ < 1, we define a random variable ∞ Yλ =(1−λ)ξnλn, n=0 where ξn are independent random variables with P{ξn =0}=P{ξn =1}= 1. 2 The distribution of Yλ is called a symmetric Bernoulli convolution. …”
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ANN-SVM-IP: An Innovative Method for Rapidly and Efficiently Detecting and Classifying of External Defects of Apple Fruits by Nashaat M. Hussain Hassan, Mohamed M. Hassan Mahmoud, Mohamed A. Ismeil, M. Mourad Mabrook, A. A. Donkol, A. M. Mabrouk

“…The first phase attempts to detect exterior defects in apples by applying two proposed convolution kernels that were capable of identifying damaged sections of apples. …”
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Deep fusion approach: Combining hyperspectral imaging and ground penetrating radar for accurate cornfield soil moisture mapping by Milad Vahidi, Sanaz Shafian, William Hunter Frame

“…Moreover, the improvement in Lin’s Concordance Correlation from 0.84 to 0.90 for the integrated approach between the models suggests that the ANN more accurately reflects the variability in the true data, enhancing the reliability of the predictions. …”
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Estimates for convolutions in the anisotropic Nikol'skiĭ-Besov spaces by V. I. Burenkov, G. E. García Almeida

“…We obtain various estimates for convolutions in the anisotropic Nikol'skiĭ-Besov spaces of functions of several real variables possessing some common smoothness of, in general, fractional order which may be different with respect to different variables.…”
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On the Convolution Equation Related to the Diamond Klein-Gordon Operator by Amphon Liangprom, Kamsing Nonlaopon

“…We study the distribution eαx(♢+m2)kδ for m≥0, where (♢+m2)k is the diamond Klein-Gordon operator iterated k times, δ is the Dirac delta distribution, x=(x1,x2,…,xn) is a variable in ℝn, and α=(α1,α2,…,αn) is a constant. In particular, we study the application of eαx(♢+m2)kδ for solving the solution of some convolution equation. …”
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Convolutional neural networks for accurate estimation of canopy cover by F. Puig, R. Gonzalez Perea, A. Daccache, M.A. Soriano, J.A. Rodríguez Díaz

“…Canopy Cover (CC) is a key variable in agriculture, providing critical information on crop growth and health. …”
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Adaptive deep SVM for detecting early heart disease among cardiac patients by S. N. Netra, N. N. Srinidhi, E. Naresh

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Predicting species distributions in the open ocean with convolutional neural networks by Morand, Gaétan, Joly, Alexis, Rouyer, Tristan, Lorieul, Titouan, Barde, Julien

“…Indeed, tracking species distributions in the open ocean is particularly challenging due to the scarcity of observations and the complex and variable nature of the ocean system. In this study, we propose a new method that leverages deep learning, specifically convolutional neural networks (CNNs), to capture spatial features of environmental variables. …”
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An intelligent recognition method for electrical work permits based on seed growth strategy and deep neural networks by LIAO Meiying, ZHOU Junhuang, ZHANG Yongjun

“…Then， during text recognition， the method combines DenseNet’s deep feature extraction capabilities with the CTC technique’s mechanism for aligning variable-length sequences， enhancing the recognition performance of character sequences. …”
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Hybrid Clayton-Frank Convolution-Based Bivariate Archimedean Copula by Maxwell Akwasi Boateng, Akoto Yaw Omari-Sasu, Richard Kodzo Avuglah, Nana Kena Frempong

“…This study exploits the closure property of the converse convolution operator to come up with a hybrid Clayton-Frank Archimedean copula for two random variables. …”
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The Laguerre transform of a convolution product of vector-valued functions. by A. O. Muzychuk

“…The main results have been obtained by establishing a relationship between the Laguerre and Laplace transforms over the time variable with respect to the elements of Lebesgue weight  spaces. …”
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Some properties of convolution and spherical analysis on the Euclidean motion group by Uwe Edeke, Unanaowo Bassey

“…Further more, spherical analysis on the Gelfand pair (Rn x SO(n), SO(n)) is presented, including an explicit determination of spherical function for G, when n = 2, by the method of separation of variables. …”
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Complementing Dynamical Downscaling With Super‐Resolution Convolutional Neural Networks by Deeksha Rastogi, Haoran Niu, Linsey Passarella, Salil Mahajan, Shih‐Chieh Kao, Pouya Vahmani, Andrew D. Jones

“…Future work will expand this methodology to downscale additional variables for future climate projections.…”
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Detection and Classification of Sporadic E Using Convolutional Neural Networks by J. A. Ellis, D. J. Emmons, M. B. Cohen

“…Abstract In this work, convolutional neural networks (CNN) are developed to detect and characterize sporadic E (Es), demonstrating an improvement over current methods. …”
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English Text Recognition Based on Convolutional Neural Network (CNN) by Razia Maroof, Irfan Ahmed Usmani, Atruba Feroze

“…Traditional methods attain an imperfect ability to handle such variability and complexity. This study addresses the text recognition problem from images, directing the extraction of text from images with higher accuracy. …”
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Ocean wave power forecasting using convolutional neural networks by Pedro Bento, José Pombo, Maria do Rosário Calado, Sílvio Mariano

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Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea by Daniele Padovano, Arturo Martinez-Rodrigo, José M. Pastor, José J. Rieta, Raul Alcaraz

“…Most of these were based on the heart rate variability (HRV) analysis, but only a few of them have presented a recurrence-based approach. …”
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Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease by Hsin-Yueh Liang, Hsin-Yueh Liang, Kai-Cheng Hsu, Kai-Cheng Hsu, Kai-Cheng Hsu, Shang-Yu Chien, Chen-Yu Yeh, Ting-Hsuan Sun, Meng-Hsuan Liu, Kee Koon Ng

“…The principal predictive feature variables were sex, maximum heart rate, and ST/HR index. …”
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Fault Diagnosis for Imbalanced Datasets Based on Deep Convolution Fuzzy System by Junwei Zhu, Linfang Zhu

“…The BAVAE improves data generation capabilities by introducing autoregressive distributions to learn latent variables, iteratively obtaining complex high-order latent variables, and amplifying inter-class differences through the introduction of feature discrimination loss during training. …”
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