A deep learning based prediction approach for the supercritical airfoil at transonic speeds

2021 ◽  
Vol 33 (8) ◽  
pp. 086109
Author(s):  
Di Sun ◽  
Zirui Wang ◽  
Feng Qu ◽  
Junqiang Bai
Keyword(s):  
Deep Learning ◽  
Supercritical Airfoil ◽  
Prediction Approach

scholarly journals A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings

2020 ◽  
Vol 25 (3) ◽  
pp. 1243-1254 ◽  
Author(s):  
Cheng Cheng ◽  
Guijun Ma ◽  
Yong Zhang ◽  
Mingyang Sun ◽  
Fei Teng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Useful Life ◽  
Prediction Approach

scholarly journals Passive Diagnosis of Mental Health Disorders Incorporating an Empathic Dialogue System

2019 ◽  
Author(s):  
Fionn Delahunty ◽  
Mihael Arcan ◽  
Robert Johansson
Keyword(s):  
Mental Health ◽  
Deep Learning ◽  
Mental Health Disorders ◽  
Screening Methods ◽  
Dialogue System ◽  
System P ◽  
Data Limitations ◽  
Textual Data ◽  
Health Disorders ◽  
Prediction Approach

Depression and anxiety are the two most prevalent mental health disorders worldwide, impacting the lives of millions of people each year. Current screening methods require individuals to manually complete psychometric questionnaires. In this work we develop a deep learning approach to predict psychometric scores given textual data through the use of psycholinguistics features. Data is collected via a dialogue system, were we develop and incorporate an approach to model empathy. Which aims to allow for appropriate use of these systems in a clinical setting. Following a public evaluation, we demonstrate that our approach to model empathy can out perform a similarly trained non empathic approach. Additionally, we show that our deep learning prediction approach performed well on evaluation data, but has difficulty generalizing to experimentally collected data. Limitations and implications as a result of this work are discussed.

A CNN With Deep Learning for Non-Equilibrium Characterization of Al-Sm Melt Infusion Into a B4C Packed Bed

2021 ◽  
Author(s):  
Julio Aguilar ◽  
Laura Sandoval ◽  
Arturo Rodriguez ◽  
Sanjay Shantha Kumar ◽  
Jose Terrazas ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Temperature ◽  
Convolutional Neural Network ◽  
Computational Cost ◽  
Packed Bed ◽  
Reaction Products ◽  
Training Time ◽  
Ti Alloys ◽  
Prediction Approach

Abstract In seeking predictability of characterizing materials for ultra-high temperature materials for hypersonic vehicles, the use of the convolutional neural network for characterizing the behavior of liquid Al-Sm-X (Hf, Zr, Ti) alloys within a B4C packed to determine the reaction products for which they are usually done with the scanning electron microscope (SEM) or X-ray diffraction (XRD) at ultra-high temperatures (> 1600°C). Our goal is to predict ultimately the products as liquid Al-Sm-X (Hf, Zr, Ti) alloys infiltrate into a B4C packed bed. Material characterization determines the processing path and final species from the reacting infusion consisting of fluid flow through porous channels, consumption of elemental components, and reaction forming boride and carbide precipitates. Since characterization is time-consuming, an expert in this field is required; our approach is to characterize and track these species using a Convolutional Neural Network (CNN) to facilitate and automate analysis of images. Although Deep Learning seems to provide an automated prediction approach, some of these challenges faced under this research are difficult to overcome. These challenges include data required, accuracy, training time, and computational cost requirements for a CNN. Our approach was to perform experiments on high-temperature metal infusion under B4C Packed Bed infiltration in a parametric matrix of cases. We characterized images using SEM and XRD images and run/optimize our CNN, which yields an innovative method for characterization via Deep Learning compared to traditional practices.

A Prediction Approach Based on Self-Training and Deep Learning for Biological Data

2020 ◽  
Vol 10 (4) ◽  
pp. 50-64
Author(s):  
Mohamed Nadjib Boufenara ◽  
Mahmoud Boufaida ◽  
Mohamed Lamine Berkane
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Biological Data ◽  
Unlabeled Data ◽  
Learning Methods ◽  
Deep Learning Algorithm ◽  
Prediction Approach ◽  
F Measure

With the exponential growth of biological data, labeling this kind of data becomes difficult and costly. Although unlabeled data are comparatively more plentiful than labeled ones, most supervised learning methods are not designed to use unlabeled data. Semi-supervised learning methods are motivated by the availability of large unlabeled datasets rather than a small amount of labeled examples. However, incorporating unlabeled data into learning does not guarantee an improvement in classification performance. This paper introduces an approach based on a model of semi-supervised learning, which is the self-training with a deep learning algorithm to predict missing classes from labeled and unlabeled data. In order to assess the performance of the proposed approach, two datasets are used with four performance measures: precision, recall, F-measure, and area under the ROC curve (AUC).

Real-time multiple spatiotemporal action localization and prediction approach using deep learning

2020 ◽  
Vol 128 ◽  
pp. 331-344
Author(s):  
Ahmed Ali Hammam ◽  
Mona M. Soliman ◽  
Aboul Ella Hassanien
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Action Localization ◽  
Prediction Approach

scholarly journals Deep learning for prediction of depressive symptoms in a large textual dataset

2021 ◽  
Author(s):  
Md Zia Uddin ◽  
Kim Kristoffer Dysthe ◽  
Asbjørn Følstad ◽  
Petter Bae Brandtzaeg
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Depressive Symptoms ◽  
Intelligent Systems ◽  
Superior Performance ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Information Channel ◽  
Symptoms Of Depression ◽  
Prediction Approach

AbstractDepression is a common illness worldwide with potentially severe implications. Early identification of depressive symptoms is a crucial first step towards assessment, intervention, and relapse prevention. With an increase in data sets with relevance for depression, and the advancement of machine learning, there is a potential to develop intelligent systems to detect symptoms of depression in written material. This work proposes an efficient approach using Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to identify texts describing self-perceived symptoms of depression. The approach is applied on a large dataset from a public online information channel for young people in Norway. The dataset consists of youth’s own text-based questions on this information channel. Features are then provided from a one-hot process on robust features extracted from the reflection of possible symptoms of depression pre-defined by medical and psychological experts. The features are better than conventional approaches, which are mostly based on the word frequencies (i.e., some topmost frequent words are chosen as features from the whole text dataset and applied to model the underlying events in any text message) rather than symptoms. Then, a deep learning approach is applied (i.e., RNN) to train the time-sequential features discriminating texts describing depression symptoms from posts with no such descriptions (non-depression posts). Finally, the trained RNN is used to automatically predict depression posts. The system is compared against conventional approaches where it achieved superior performance than others. The linear discriminant space clearly reveals the robustness of the features by generating better clustering than other traditional features. Besides, since the features are based on the possible symptoms of depression, the system may generate meaningful explanations of the decision from machine learning models using an explainable Artificial Intelligence (XAI) algorithm called Local Interpretable Model-Agnostic Explanations (LIME). The proposed depression symptom feature-based approach shows superior performance compared to the traditional general word frequency-based approaches where frequency of the features gets more importance than the specific symptoms of depression. Although the proposed approach is applied on a Norwegian dataset, a similar robust approach can be applied on other depression datasets developed in other languages with proper annotations and symptom-based feature extraction. Thus, the depression prediction approach can be adopted to contribute to develop better mental health care technologies such as intelligent chatbots.

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