Πλοήγηση ανά Θέμα "Convolutional neural network"
Αποτελέσματα 1-20 από 38
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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 ... -
Artificial intelligence implementations on the blockchain. Use cases and future applications
(2019)An exemplary paradigm of how an AI can be a disruptive technological paragon via the utilization of blockchain comes straight from the world of deep learning. Data scientists have long struggled to maintain the quality of ... -
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 ... -
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 learning and change detection for fall recognition
(2019)Early fall detection is a crucial research challenge since the time delay from fall to first aid is a key factor that determines the consequences of a fall. Wearable sensors allow a reliable way for daily-life activities ... -
Deep Learning Models for Yoga Pose Monitoring
(2022)Activity recognition is the process of continuously monitoring a person’s activity and movement. Human posture recognition can be utilized to assemble a self-guidance practice framework that permits individuals to accurately ... -
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 ... -
Deep View2View Mapping for View-Invariant Lipreading
(2019)Recently, visual-only and audio-visual speech recognition have made significant progress thanks to deep-learning based, trainable visual front-ends (VFEs), with most research focusing on frontal or near-frontal face videos. ... -
Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification
(2018)This paper proposes a novel methodology for automatic detection and localization of gastrointestinal (GI) anomalies in endoscopic video frame sequences. Training is performed with weakly annotated images, using only ... -
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 ... -
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 ... -
Exploring ROI size in deep learning based lipreading
(2017)Automatic speechreading systems have increasingly exploited deep learning advances, resulting in dramatic gains over traditional methods. State-of-the-art systems typically employ convolutional neural networks (CNNs), ... -
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 ... -
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 ...