Corona Virus Disease 2019 Respiratory Cycle Detection Based on Convolutional Neural Network

2021 ◽  
Author(s):  
Jing Wang ◽  
Ping Chen ◽  
Cheng Zhang ◽  
Yi Kang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Virus Disease ◽  
Respiratory Cycle ◽  
Corona Virus ◽  
Cycle Detection

scholarly journals SISTEM PRESENSI MAHASISWA OTOMATIS PADA ZOOM MEETING MENGGUNAKAN FACE RECOGNITION DENGAN METODE CONVULITIONAL NEURAL NETWORK BERBASIS WEB

2021 ◽  
Vol 5 (2) ◽  
pp. 785-793
Author(s):  
Sujud Satwikayana ◽  
Suryo Adi Wibowo ◽  
Nurlaily Vendyansyah
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Virus Disease ◽  
Training Data ◽  
Corona Virus

Dalam rangka pencegahan perkembangan dan penyebaran Corona Virus Disease (COVID-19), Kementerian Pendidikan dan Kebudayaan mengeluarkan SE Mendikbud Tahun 2020 tentang Pembelajaran secara Daring dan Bekerja dari Rumah dalam rangka Pencegahan Penyebaran COVID-19. Pembelajaran secara daring dan bekerja dari rumah bagi para tenaga pendidik merupakan perubahan yang harus dilakukan untuk tetap mengajar mahasiswa. Ketika melakukan pembelajaran secara daring tentunya memerlukan media sebagai sarananya. Survei terbaru yang dilakukan oleh Lembaga Arus Survei Indonesia (ASI) terkait penggunaan media video call dalam pembelajaran daring, mayoritas publik menggunakan aplikasi Zoom (57,2 %), disusul Google Meet (18,5 %), Cisco Webex (8,3 %), U Meet Me (5,0 %), Microsoft Teams (2,0 %), dan lainnya (2,2 %). Sisanya 6,9 % mengaku tidak tahu atau tidak jawab. Presensi sangat penting untuk mengetahui dan mengontrol kehadiran peserta didik dalam proses belajar mengajar. Saat ini presensi dalam perkuliahan daring masih dilakukan secara manual. Untuk itu perlu dibuat sistem pencatatan kehadiran berbasis face recognition secara otomatis. Dalam penelitian ini metode yang digunakan untuk face recognition adalah Convolutional Neural Network (CNN). Metode diimplementasikan dengan bantuan library Keras untuk proses training data. Hasil dari penelitian ini adalah sistem berbasis web yang dapat mendeteksi wajah mahasiswa yang berpartisipasi dalam ruang Zoom meeting. Pengujian yang dilakukan kepada 10 orang relawan munggunakan model hasil training data metode  CNN dari total 150 kali uji coba, total benar sebanyak 138 kali dan total salah sebanyak 12 kali, menunjukkan kinerja pengenalan wajah meraih rata-rata tingkat akurasi benar sebesar 92,00 % dan salah sebesar 8,00 % yang berarti sudah menghasilkan kecocokan yang baik.

scholarly journals Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression to Detect COVID-19 in Chest CT Images

2021 ◽  
Vol 18 (11) ◽  
Author(s):  
Houssam BENBRAHIM ◽  
Hanaa HACHIMI ◽  
Aouatif AMINE
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Virus Disease ◽  
Ct Images ◽  
Chest Ct ◽  
Apache Spark ◽  
Difficult Situation ◽  
Novel Coronavirus

The SARS-CoV-2 (COVID-19) has propagated rapidly around the world, and it became a global pandemic. It has generated a catastrophic effect on public health. Thus, it is crucial to discover positive cases as early as possible to treat touched patients fastly. Chest CT is one of the methods that play a significant role in diagnosing 2019-nCoV acute respiratory disease. The implementation of advanced deep learning techniques combined with radiological imaging can be helpful for the precise detection of the novel coronavirus. It can also be assistive to surmount the difficult situation of the lack of medical skills and specialized doctors in remote regions. This paper presented Deep Transfer Learning Pipelines with Apache Spark and KerasTensorFlow combined with the Logistic Regression algorithm for automatic COVID-19 detection in chest CT images, using Convolutional Neural Network (CNN) based models VGG16, VGG19, and Xception. Our model produced a classification accuracy of 85.64, 84.25, and 82.87 %, respectively, for VGG16, VGG19, and Xception. HIGHLIGHTS Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression using CT images to screen for Corona Virus Disease (COVID-19)       Automatic detection of  COVID-19 in chest CT images Convolutional Neural Network (CNN) based models VGG16, VGG19, and Xception to predict COVID-19 in Computed Tomography image GRAPHICAL ABSTRACT

Improved Linear Factor based Grasshopper Optimization Algorithm with Ensemble Learning for Covid-19 Forecasting

2021 ◽  
Vol 19 (8) ◽  
pp. 169-181
Author(s):  
P. Renukadevi ◽  
Dr.A. Rajiv Kannan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ensemble Learning ◽  
Optimization Algorithm ◽  
Technological Development ◽  
Predictive Performance ◽  
Linear Factor ◽  
Corona Virus ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Recently the COVID’19 is extensively increasing around the world with many challenges for researchers. Rigorous respiratory disease corona virus 2 show aggression to many parts of COVID’19 affected patients, together with brain and lungs. The changeableness of Corona virus with likely to infect Central Nervous System emphasize the necessity for technological development to identify, handle, and take care of brain damages in COVID’19 patients. An exact short-term predicting the quantity of newly infected and cured cases is vital for resource optimization to stop or reduce the growth of infection. The previous system designed a Linear Decreasing Inertia Weight based Cat Swarm Optimization with Half Binomial Distribution based Convolutional Neural Network (LDIWCSO-HBDCNN) approach for COVID-19 forecasting. However, the ensemble learning is required to improve the prediction outcome via integrating many approaches. This approach allows the production of better predictive performance compared to a single model. For solving this problem, the proposed system designed an Improved Linear Factor based Grasshopper Optimization Algorithm with Ensemble Learning (ILFGOA with EL) for covid-19 forecasting. Initially, the COVID-19 forecasting dataset is taken as an input. With the help of min-max approach, data normalization is done. Then the optimal features are selected by using Improved Linear Factor based Grasshopper Optimization Algorithm (ILFGOA) algorithm to improve the prediction accuracy. Based on the selected features, Ensemble Learning (EL) which includes Hyperparameter based Convolutional Neural Network (HCNN) is utilized to identify infected and demise cases across india for a period of time. The outcome of analysis shows that the introduced method attains better execution against previous system with regard to error rate, accuracy, precision, recall and f-measure.

scholarly journals A CNN Classification Model For Diagnosis Covid19

2020 ◽  
Author(s):  
Ahmed Abdullah Farid ◽  
hatem khater ◽  
gamal selim
Keyword(s):  
Neural Network ◽  
Virus Disease ◽  
Real Data ◽  
Classification Model ◽  
X Ray ◽  
Corona Virus ◽  
Chest X Ray ◽  
Neural Network Structure ◽  
Extraction Model ◽  
First Time

The paper demonstrates the analysis of Corona Virus Disease based on a CNN probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed Convolution neural network structure. The Study is validated on 2002 chest X-ray images with 60 confirmed positive covid19 cases and (650 bacterial – 412 viral -880 normal) x-ray images. The proposed CNN compared with traditional classifiers with proposed CHFS feature extraction model. The experimental study has done with real data demonstrates the feasibility and potential of the proposed approach for the said cause. The result of proposed CNN structure has been successfully done to achieve 98.20% accuracy of covid19 potential cases with comparable of traditional classifiers.

scholarly journals Face Biometrics as a Potential Predictor for COVID-19 Susceptibility

2021 ◽  
Author(s):  
Oleg Kit
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Disease Severity ◽  
Virus Disease ◽  
Binary Classification ◽  
Severe Form ◽  
K Nearest Neighbors ◽  
Face Descriptor ◽  
The Individual

Fighting against the COVID-19 pandemic caused by the SARS-CoV-2 virus is one of the most critical challenges facing the global health system today. The possibility to identify the group of persons in the cohort of people under 50 years old, who are sensitive to the COVID-disease by non-invasive methods, is a very perspective approach for estimating the epidemiological state of the human population. The study aimed to identify the features of people's faces with COVID-19 that the most correlate with disease severity could serve as one of these approaches. For this aim, 525 photos of patients' faces with different outcomes of COVID-19 disease were analyzed using the Dlib face recognition convolutional neural network pre-trained for face recognition. Face descriptor vectors were obtained using the convolutional neural network. Facial features were found that predict a person's sensitivity to the SARS-CoV-2 virus (disease severity), and the contribution of each of the features to the risk of developing a severe form of COVID in a person was found. The accuracy of the binary classification of the individual severity of the COVID-19 course using the k-nearest neighbors algorithm on the test dataset was accuracy - 84%, AUC - 0.90.

scholarly journals Towards robotics and artificial intelligence for the prevention of Covid 19 pandemic

2021 ◽  
Vol 229 ◽  
pp. 01035
Author(s):  
Saloua Senhaji ◽  
Sanaa Faquir ◽  
Mohammed Ouazzani Jamil
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Train Station ◽  
Facial Mask ◽  
Corona Virus ◽  
Temperature Detection ◽  
Medical Crisis

In times of medical crisis, robotics and artificial intelligence helps humans manage emergencies and ensure a fast and efficient decontamination process. In this paper, we propose a robot with temperature detection, Corona virus checker using new biosensors, and artificial intelligence facial mask detection based on the deep convolutional neural network. Our robot can sterilize and patrol any type of area. In particular, airports, the train station and transport facilities which are the routes of transmission of the virus from one country to another.

scholarly journals Real Time Face Mask Identification using AI-Deep Learning Neural Network

2021 ◽  
Vol 9 (VI) ◽  
pp. 1424-1432
Author(s):  
Aishwarya. B. K
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Virus Disease ◽  
Face Mask ◽  
Primary Objective ◽  
Working Environment ◽  
Corona Virus ◽  
Learning Techniques ◽  
Deep Learning Neural Network ◽  
Safe Working

The COVID - 19 pandemic is devastating mankind irrespective of caste, creed, gender, and religion. Contribution of each individual to constrain the expansion of the corona- virus. Is a primary objective/Fundamental duties as a responsible individual to Use a face mask can undoubtedly help in managing the spread of the virus. COVID - 19 face mask Detector uses or owns Facemask net, deep learning techniques to successfully test whether a person is with wearing a face mask or not. In this project we are working on “FACE MASK IDENTIFICATION USING AI DEEP LEARNING NEURAL NETWORK”. The end of 2019 witnessed the outbreak of Corona virus Disease 2019 (COVID-19), which has continued to be the cause of plight for millions of lives and businesses even in 2020. As the world recovers from the pandemic and plans to return to a state of normalcy, there is a wave of anxiety among all individuals, especially those who intend to resume in-person activity. Studies have proved that wearing a face mask significantly reduces the risk of viral transmission as well as provides a sense of protection. However, it is not feasible to manually track the implementation of this policy. Technology holds the key here. We are using a Deep Learning based system that can detect instances where face masks are not used properly. Our system consists of a faster region-based Convolution Neural Network (FRCNN) architecture capable of detecting masked and unmasked faces and can be integrated with preinstalled CCTV cameras. This will help track safety violations, promote the use of face masks, and ensure a safe working environment.

scholarly journals A Suggested Diagnostic System of Corona Virus based on the Neutrosophic Systems and Deep Learning

2020 ◽  
pp. 54-59
Author(s):  
admin admin ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Linear Model ◽  
Diagnostic System ◽  
Blood Samples ◽  
Corona Virus

The idea for this paper is based on the use of a computer-connected microscope associated with Deep Learning, using Convolutional Neural Network (CNN). CNN is a mathematical type of Deep Learning used to recognize and diagnose images. After that, we photograph blood samples, as well as samples, were taken from the mouth and nose, as well as it is possible to photograph the throat from the inside of a large number of injured and uninfected people as well as suspected of infection and provide a large number of references for this program for each type of those different samples. It is possible to perform this process in few minutes, save time and money, make analyzes for the largest possible number of people, and provide results in an accurate and documented manner, which is through the Neutrosophic time series. The basis and analysis of dealing with all data, whether specific or not, that can be taken by time series values, then we present the linear model for the neutrosophic time series, and we test the significance of its coefficient based on patients distribution. Finally, from the above, we can provide a patient neutrosophic time series according to the linear model through which we can accurately predict the program will give degrees of verification and degrees of the uncertainty of the data.

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