Search results for "convolutional"
showing 10 items of 186 documents
Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis
2021
In order to analyse the sports psychology of athletes and to identify the psychology of athletes in their movements, a human action recognition (HAR) algorithm has been designed in this study. First, a HAR model is established based on the convolutional neural network (CNN) to classify the current action state by analysing the action information of a task in the collected videos. Secondly, the psychology of basketball players displaying fake actions during the offensive and defensive process is investigated by combining with related sports psychological theories. Then, the psychology of athletes is also analysed through the collected videos, so as to predict the next response action of the …
Hyperspectral image classification using CNN: Application to industrial food packaging
2021
Abstract During food tray packaging, some contamination may exist due to the presence of undesired objects. It is essential to detect anomalies during the packaging process in order to discard the faulty tray and avoid human consumption. This study demonstrates the on-line classification feasibility when using hyperspectral imaging systems for real-time food packaging control by using Convolutional Neural Networks (CNN) as a classifier in heat-sealed food trays. A hyperspectral camera is used to capture individual food tray information and fed to a CNN classifier to detect faulty food trays with object contamination. The proposed system is able to detect up to eleven different contamination…
Use of machine learning approaches to improve non-invasive skin melanoma diagnostic method in spectral range 450 - 950nm
2020
Non-invasive skin cancer diagnostic methods develop rapidly thanks to Deep Learning and Convolutional Neural Networks (CNN). Currently, two types of diagnostics are popular: (a) using single image taken under white illumination and (b) using multiple images taken in narrow spectral bands. The first method is easier to implement, but it is limited in accuracy. The second method is more sensitive, because it is possible to use illumination considering the absorption bands of the skin chromophores and the optical properties of the skin. Currently CNN use a single white light image, due to the availability of large datasets with lesion images. Since CNN processing and analysis requires a large …
Convolutional Matrix Factorization for Recommendation Explanation
2018
In this paper, we introduce a novel recommendation model, which harnesses a convolutional neural network to mine meaningful information from customer reviews, and integrates it with matrix factorization algorithm seamlessly. It is a valid method to improve the transparency of CF algorithms.
Segmentation-Free Estimation of Aortic Diameters from MRI Using Deep Learning
2021
Accurate and reproducible measurements of the aortic diameters are crucial for the diagnosis of cardiovascular diseases and for therapeutic decision making. Currently, these measurements are manually performed by healthcare professionals, being time consuming, highly variable, and suffering from lack of reproducibility. In this work we propose a supervised deep-learning method for the direct estimation of aortic diameters. The approach is devised and tested over 100 magnetic resonance angiography scans without contrast agent. All data was expert-annotated at six aortic locations typically used in clinical practice. Our approach makes use of a 3D+2D convolutional neural network (CNN) that ta…
On the Robustness of Deep Features for Audio Event Classification in Adverse Environments
2018
Deep features, responses to complex input patterns learned within deep neural networks, have recently shown great performance in image recognition tasks, motivating their use for audio analysis tasks as well. These features provide multiple levels of abstraction which permit to select a sufficiently generalized layer to identify classes not seen during training. The generalization capability of such features is very useful due to the lack of complete labeled audio datasets. However, as opposed to classical hand-crafted features such as Mel-frequency cepstral coefficients (MFCCs), the performance impact of having an acoustically adverse environment has not been evaluated in detail. In this p…
Toward Self-Supervised Feature Learning for Online Diagnosis of Multiple Faults in Electric Powertrains
2021
This article proposes a novel online fault diagnosis scheme for industrial powertrains without using historical faulty or labeled training data. The proposed method combines a one-class support vector machine (SVM) based anomaly detection and supervised convolutional neural network (CNN) algorithms to online detect multiple faults and fault severities under variable speeds and loads. The one-class SVM algorithm is to derive a score for defining faults or health classes in the first stage, and the resulting health classes are used as the training data for the CNN-based classifier in the second stage. Within this framework, the self-supervised learning of the proposed CNN algorithm allows the…
Gravitational-wave parameter inference using Deep Learning
2021
We explore machine learning methods to detect gravitational waves (GW) from binary black hole (BBH) mergers using deep learning (DL) algorithms. The DL networks are trained with gravitational waveforms obtained from BBH mergers with component masses randomly sampled in the range from 5 to 100 solar masses and luminosity distances from 100 Mpc to, at least, 2000 Mpc. The GW signal waveforms are injected in public data from the O2 run of the Advanced LIGO and Advanced Virgo detectors, in time windows that do not coincide with those of known detected signals, and the data from each detector in the Advanced LIGO and Advanced Virgo network is combined into a unique RGB image. We show that a clas…
Automatic Segmentation Using a Hybrid Dense Network Integrated With an 3D-Atrous Spatial Pyramid Pooling Module for Computed Tomography (CT) Imaging
2020
Computed tomography (CT) with a contrast-enhanced imaging technique is extensively proposed for the assessment and segmentation of multiple organs, especially organs at risk. It is an important factor involved in the decision making in clinical applications. Automatic segmentation and extraction of abdominal organs, such as thoracic organs at risk, from CT images are challenging tasks due to the low contrast of pixel values surrounding other organs. Various deep learning models based on 2D and 3D convolutional neural networks have been proposed for the segmentation of medical images because of their automatic feature extraction capability based on large labeled datasets. In this paper, we p…
Improving Irony and Stereotype Spreaders Detection using Data Augmentation and Convolutional Neural Network
2022
In this paper we describe a deep learning model based on a Data Augmentation (DA) layer followed by a Convolutional Neural Network (CNN). The proposed model was developed by our team for the Profiling Irony and Stereotype Spreaders (ISSs) task proposed by the PAN 2022 organizers. As a first step, to classify an author as ISS or not (nISS), we developed a DA layer that expands each sample in the dataset provided. Using this augmented dataset we trained the CNN. Then, to submit our predictions, we apply our DA layer on the samples within the unlabeled test set too. Finally we fed our trained CNN with the augmented test set to generate our final predictions. To develop and test our model we us…