Πλοήγηση ανά Θέμα "Convolution"
Αποτελέσματα 1-20 από 43
-
An Algorithm for the Closed-Form Solution of Certain Classes of Volterra–Fredholm Integral Equations of Convolution Type
(2022)In this paper, a direct operator method is presented for the exact closed-form solution of certain classes of linear and nonlinear integral Volterra–Fredholm equations of the second kind. The method is based on the existence ... -
Applying a Convolutional Neural Network in an IoT Robotic System for Plant Disease Diagnosis
(2020)Plant diseases are major threat to green product quality and agricultural productivity. Agronomists and farmers often encounter great difficulties in early detection of plant diseases and controlling their potential ... -
Arm Gesture Recognition using a Convolutional Neural Network
(2018)In this paper we present an approach towards arm gesture recognition that uses a Convolutional Neural Network (CNN), which is trained on Discrete Fourier Transform (DFT) images that result from raw sensor readings. More ... -
Assessing image analysis filters as augmented input to convolutional neural networks for image classification
(2018)Convolutional Neural Networks (CNNs) have been proven very effective in image classification and object recognition tasks, often exceeding the performance of traditional image analysis techniques. However, training a CNN ... -
Assessing the effect of human factors in healthcare cyber security practice: An empirical study
(2021)The main goal of this research paper is to address the problem of SPECT myocardial perfusion imaging (MPI) diagnosis, exploring the capabilities of convolutional neural networks (CNN). Up to date, very few research studies ... -
Closed-Form Solution of the Bending Two-Phase Integral Model of Euler-Bernoulli Nanobeams
(2022)Recent developments have shown that the widely used simplified differential model of Eringen’s nonlocal elasticity in nanobeam analysis is not equivalent to the corresponding and initially proposed integral models, the ... -
A Convolutional Neural Network-based explainable classification method of SPECT myocardial perfusion images in nuclear cardiology
(2022)This study targets on the development of an explainable Convolutional Neural Network (CNN) pipeline in the form of a handcrafted CNN to identify patients' coronary artery disease status (normal, ischemia or infarction). ... -
Convolutional neural networks for pose recognition in binary omni-directional images
(2016)In this work, we present a methodology for pose classification of silhouettes using convolutional neural networks. The training set consists exclusively from the synthetic images that are generated from three-dimensional ... -
Convolutional neural networks for toxic comment classification
(2018)Flood of information is produced in a daily basis through the global internet usage arising from the online interactive communications among users. While this situation contributes significantly to the quality of human ... -
Convolutional Variational Autoencoders for Image Clustering
(2021)The problem of data clustering is one of the most fundamental and well studied problems of unsupervised learning. Image clustering, refers to one of the most challenging specifications of clustering, concerning image data. ... -
Credit card fraud detection using a deep learning multistage model
(2022)The banking sector is on the eve of a serious transformation and the thrust behind it is artificial intelligence (AI). Novel AI applications have been already proposed to deal with challenges in the areas of credit scoring, ... -
Deep sensorimotor learning for RGB-D object recognition
(2020)Research findings in cognitive neuroscience establish that humans, early on, develop their understanding of real-world objects by observing others interact with them or by performing active exploration and physical ... -
DeepTSS: multi-branch convolutional neural network for transcription start site identification from CAGE data
(2022)Background: The widespread usage of Cap Analysis of Gene Expression (CAGE) has led to numerous breakthroughs in understanding the transcription mechanisms. Recent evidence in the literature, however, suggests that CAGE ... -
Detection of malignant melanomas in dermoscopic images using convolutional neural network with transfer learning
(2017)In this work, we report the use of convolutional neural networks for the detection of malignant melanomas against nevus skin lesions in a dataset of dermoscopic images of the same magnification. The technique of transfer ... -
Development of Convolutional Neural Networkbased models for bone metastasis classification in nuclear medicine
(2020)Focusing on prostate cancer patients, this research paper addresses the problem of bone metastasis diagnosis, investigating the capabilities of convolutional neural networks (CNN) and transfer learning. Considering the ... -
Early Fusion of Visual Representations of Skeletal Data for Human Activity Recognition
(2022)In this work we present an approach for human activity recognition which is based on skeletal motion, i.e., the motion of skeletal joints in the 3D space. More specifically, we propose the use of 4 well-known image ... -
Efficient Learning Rate Adaptation for Convolutional Neural Network Training
(2019)Convolutional Neural Networks (CNNs) have been established as substantial supervised methods for classification problems in many research fields. However, a large number of parameters have to be tuned to achieve high ... -
Fingerspelled alphabet sign recognition in upper-body videos
(2019)Fingerspelling is a crucial part of sign-based communication, however its recognition remains a challenging and mostly overlooked computer vision problem. To address it, this paper presents a system that recognizes the 24 ... -
A fully convolutional sequence learning approach for cued speech recognition from videos
(2021)Cued Speech constitutes a sign-based communication variant for the speech and hearing impaired, which involves visual information from lip movements combined with hand positional and gestural cues. In this paper, we consider ... -
Fusing Handcrafted and Contextual Features for Human Activity Recognition
(2019)In this paper we present an approach for the recognition of human activity that combines handcrafted features from 3D skeletal data and contextual features learnt by a trained deep Convolutional Neural Network (CNN). Our ...