Search results for "Deep Learning"

Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity

2020

Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a binary classification problem. Kinematic data, namely a number of raw signals in the sagittal plane, collected by the Vicon motion capture system (Oxford Metrics Group, UK) were employed as predictors. Therefore, the input data for the predictive model are presented as a multi-channel time series. Deep learning techniques, namely five convolutional neural network (CNN) models were applied to the binary classification analysis, based on a Multi-Layer Perceptron (MLP) classifi…

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks

2020

Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The system sends the ECG signal to a Fog layer service by using the LoRa communication protocol. Also, it includes an AI algorithm based on deep learning for the detection of Atrial Fibrillation and other heart rhythms. The automatic detection of arrhythmias can be complementary to the diagnosis made by the physician, achieving a better clinical vision that improves therapeutic decision making. The performance of the proposed system is evaluated on a…

Analysis and Comparison of Deep Learning Networks for Supporting Sentiment Mining in Text Corpora

2020

In this paper, we tackle the problem of the irony and sarcasm detection for the Italian language to contribute to the enrichment of the sentiment analysis field. We analyze and compare five deep-learning systems. Results show the high suitability of such systems to face the problem by achieving 93% of F1-Score in the best case. Furthermore, we briefly analyze the model architectures in order to choose the best compromise between performances and complexity.

Supporting Emotion Automatic Detection and Analysis over Real-Life Text Corpora via Deep Learning: Model, Methodology, and Framework

2021

This paper describes an approach for supporting automatic satire detection through effective deep learning (DL) architecture that has been shown to be useful for addressing sarcasm/irony detection problems. We both trained and tested the system exploiting articles derived from two important satiric blogs, Lercio and IlFattoQuotidiano, and significant Italian newspapers.

A Controllable Text Simplification System for the Italian Language

2021

Text simplification is a non-trivial task that aims at reducing the linguistic complexity of written texts. Researchers have studied the problem by proposing new methodologies for addressing the English language, but other languages, like the Italian one, are almost unexplored. In this paper, we give a contribution to the enhancement of the Automated Text Simplification research by presenting a deep learning-based system, inspired by a state of the art system for the English language, capable of simplifying Italian texts. The system has been trained and tested by leveraging the Italian version of Newsela; it has shown promising results by achieving a SARI value of 30.17.

Canonical Retina-to-Cortex Vision Model Ready for Automatic Differentiation

2020

Canonical vision models of the retina-to-V1 cortex pathway consist of cascades of several Linear+Nonlinear layers. In this setting, parameter tuning is the key to obtain a sensible behavior when putting all these multiple layers to work together. Conventional tuning of these neural models very much depends on the explicit computation of the derivatives of the response with regard to the parameters. And, in general, this is not an easy task. Automatic differentiation is a tool developed by the deep learning community to solve similar problems without the need of explicit computation of the analytic derivatives. Therefore, implementations of canonical visual neuroscience models that are ready…

Classification par méthodes d’apprentissage supervisé et faiblement superviséd’images multimodales pour l’aide au diagnostic du lentigo malin en derm…

2021

Carried out in collaboration with the Saint-Étienne University Hospital, this work provides additional information to help the skin diagnosis by providing new decision methods on Lentigo Maligna and Lentigo Maligna Melanoma pathologies. To this end, the modalities regularly used in clinical conditions are made available to this work and are orchestrated within a multimodal process. Among image modalities, may be mentioned the clinical photography, the dermatoscopy, and the confocal reflectance microscopy. Initially, the first steps of this manuscript focus on reflectance confocal microscopy as the work in computer diagnostic assistance is relatively underdeveloped, in particular on the dete…

CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study

2020

Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric magnetic resonance imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the central gland (CG) and peripheral zone (PZ) can guide toward differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on deep learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability o…

2021

In COVID-19 related infodemic, social media becomes a medium for wrongdoers to spread rumors, fake news, hoaxes, conspiracies, astroturf memes, clickbait, satire, smear campaigns, and other forms of deception. It puts a tremendous strain on society by damaging reputation, public trust, freedom of expression, journalism, justice, truth, and democracy. Therefore, it is of paramount importance to detect and contain unreliable information. Multiple techniques have been proposed to detect fake news propagation in tweets based on tweets content, propagation on the network of users, and the profile of the news generators. Generating human-like content allows deceiving content-based methods. Networ…

Anomaly Detection in Traffic Surveillance Videos Using Deep Learning

2022

In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and accidents. This research deals with accidents in traffic videos. In the modern world, video traffic surveillance cameras (VTSS) are used for traffic surveillance and monitoring. As the population is increasing drastically, the likelihood of accidents is also increasing. The VTSS is used to detect abno…