scholarly journals Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches

2022 ◽  
Vol 2022 ◽  
pp. 1-15
Author(s):  
Seifeddine Messaoud ◽  
Soulef Bouaafia ◽  
Amna Maraoui ◽  
Lazhar Khriji ◽  
Ahmed Chiheb Ammari ◽  
...  
Keyword(s):  
Deep Learning ◽  
Health Resources ◽  
Disease Diagnosis ◽  
Public Health Issue ◽  
X Ray ◽  
Poor Countries ◽  
Healthcare Center ◽  
Learning Techniques ◽  
Learning Technique ◽  
First Time

At the end of 2019, the infectious coronavirus disease (COVID-19) was reported for the first time in Wuhan, and, since then, it has become a public health issue in China and even worldwide. This pandemic has devastating effects on societies and economies around the world, and poor countries and continents are likely to face particularly serious and long-lasting damage, which could lead to large epidemic outbreaks because of the lack of financial and health resources. The increasing number of COVID-19 tests gives more information about the epidemic spread, and this can help contain the spread to avoid more infection. As COVID-19 keeps spreading, medical products, especially those needed to perform blood tests, will become scarce as a result of the high demand and insufficient supply and logistical means. However, technological tests based on deep learning techniques and medical images could be useful in fighting this pandemic. In this perspective, we propose a COVID-19 disease diagnosis (CDD) tool that implements a deep learning technique to provide automatic symptoms checking and COVID-19 detection. Our CDD scheme implements two main steps. First, the patient’s symptoms are checked, and the infection probability is predicted. Then, based on the infection probability, the patient’s lungs will be diagnosed by an automatic analysis of X-ray or computerized tomography (CT) images, and the presence of the infection will be accordingly confirmed or not. The numerical results prove the efficiency of the proposed scheme by achieving an accuracy value over 90% compared with the other schemes.

scholarly journals A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jin-Woong Lee ◽  
Woon Bae Park ◽  
Jin Hee Lee ◽  
Satendra Pal Singh ◽  
Kee-Sun Sohn
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Inorganic Compounds ◽  
Phase Identification ◽  
X Ray Diffraction ◽  
X Ray ◽  
Learning Techniques ◽  
Learning Technique ◽  
Xrd Patterns

AbstractHere we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We simulate plausible powder X-ray diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification.

Kashin-Beck Disease Diagnosis Based on Deep Learning from Hand X-ray Images

2020 ◽  
pp. 105919
Author(s):  
Jinyuan Dang ◽  
Hu Li ◽  
Kai Niu ◽  
Zhiyuan Xu ◽  
Jianhao Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
X Ray ◽  
Beck Disease

COVID-19 Detection: An Approach Using X-Ray Images and Deep Learning Techniques

2021 ◽  
pp. 7-16
Author(s):  
Akshay Raina ◽  
Shubham Mahajan ◽  
Ch. Vanipriya ◽  
Anil Bhardwaj ◽  
Amit Kant Pandit
Keyword(s):  
Deep Learning ◽  
X Ray ◽  
Learning Techniques

scholarly journals DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sofia B. Dias ◽  
Sofia J. Hadjileontiadou ◽  
José Diniz ◽  
Leontios J. Hadjileontiadis
Keyword(s):  
Online Learning ◽  
Deep Learning ◽  
Predictive Model ◽  
Short Term Memory ◽  
Ground Truth ◽  
Face To Face ◽  
Learning Techniques ◽  
Learning Plans ◽  
First Time

AbstractCoronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and faculty. In the midst of this unprecedented crisis, video conferencing platforms (e.g., Zoom, WebEx, MS Teams) and learning management systems (LMSs), like Moodle, Blackboard and Google Classroom, are being adopted and heavily used as online learning environments (OLEs). However, as such media solely provide the platform for e-interaction, effective methods that can be used to predict the learner’s behavior in the OLEs, which should be available as supportive tools to educators and metacognitive triggers to learners. Here we show, for the first time, that Deep Learning techniques can be used to handle LMS users’ interaction data and form a novel predictive model, namely DeepLMS, that can forecast the quality of interaction (QoI) with LMS. Using Long Short-Term Memory (LSTM) networks, DeepLMS results in average testing Root Mean Square Error (RMSE) $$<0.009$$ < 0.009 , and average correlation coefficient between ground truth and predicted QoI values $$r\ge 0.97$$ r ≥ 0.97 $$(p<0.05)$$ ( p < 0.05 ) , when tested on QoI data from one database pre- and two ones during-Covid-19 pandemic. DeepLMS personalized QoI forecasting scaffolds user’s online learning engagement and provides educators with an evaluation path, additionally to the content-related assessment, enriching the overall view on the learners’ motivation and participation in the learning process.

scholarly journals Full color generation with Fano-type resonant HfO2 nanopillars designed by a deep-learning approach

Nanoscale ◽  
2019 ◽  
Vol 11 (44) ◽  
pp. 21266-21274 ◽  
Author(s):  
Omid Hemmatyar ◽  
Sajjad Abdollahramezani ◽  
Yashar Kiarashinejad ◽  
Mohammadreza Zandehshahvar ◽  
Ali Adibi
Keyword(s):  
Deep Learning ◽  
Fano Resonance ◽  
Learning Approach ◽  
Full Color ◽  
Wide Range ◽  
Learning Technique ◽  
First Time

Here, for the first time to our knowledge, a Fano resonance metasurface made of HfO2 is experimentally demonstrated to generate a wide range of colors. We use a novel deep-learning technique to design and optimize the metasurface.

scholarly journals Deep Learning Technique to Predict Heart Disease using IoT Based ECG Data

2019 ◽  
Vol 9 (2) ◽  
pp. 2559-2562
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Rule Based ◽  
Learning Techniques ◽  
Learning Technique ◽  
Logistic Regression Algorithm ◽  
Target Output

Scientific Knowledge and Electronic devices are growing day by day. In this aspect, many expert systems are involved in the healthcare industry using machine learning algorithms. Deep neural networks beat the machine learning techniques and often take raw data i.e., unrefined data to calculate the target output. Deep learning or feature learning is used to focus on features which is very important and gives a complete understanding of the model generated. Existing methodology used data mining technique like rule based classification algorithm and machine learning algorithm like hybrid logistic regression algorithm to preprocess data and extract meaningful insights of data. This is, however a supervised data. The proposed work is based on unsupervised data that is there is no labelled data and deep neural techniques is deployed to get the target output. Machine learning algorithms are compared with proposed deep learning techniques using TensorFlow and Keras in the aspect of accuracy. Deep learning methodology outfits the existing rule based classification and hybrid logistic regression algorithm in terms of accuracy. The designed methodology is tested on the public MIT-BIH arrhythmia database, classifying four kinds of abnormal beats. The proposed approach based on deep learning technique offered a better performance, improving the results when compared to machine learning approaches of the state-of-the-art

scholarly journals Analysis of Teeline Shorthand Recognition using Machine Learning and Deep Learning Techniques.

2020 ◽  
Vol 9 (4) ◽  
pp. 2133-2138
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Recognition System ◽  
Machine Learning Techniques ◽  
Handheld Devices ◽  
Original Text ◽  
Language Recognition ◽  
Learning Techniques ◽  
Learning Technique ◽  
Short Time

In order to take notes of the speech delivered by the VIPs in the short time short hand language is employed. Mainly there are two shorthand languages namely Pitman and Teeline. An automatic shorthand language recognition system is essential in order to make use of the handheld devices for speedy conversion to the original text. The paper addresses and compares the recognition of the Teeline alphabets using the Machine learning (SVM and KNN) and deep learning (CNN) techniques. The dataset has been prepared using the digital pen and the same is processed and stored using the android application. The prepared dataset is fed to the proposed system and accuracy of recognition is compared. Deep learning technique gave higher accuracy compared to machine learning techniques. MATLAB 2018b platform is used for implementation of the experimental setup.

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