scholarly journals Covhindia: Deep Learning Framework for Sentiment Polarity Detection of Covid-19 Tweets in Hindi

2020 ◽  
Vol 9 (5) ◽  
pp. 23-34
Author(s):  
Purva Singh
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Virus Disease ◽  
Performance Comparison ◽  
Sentiment Classification ◽  
World Health ◽  
Detection Accuracy ◽  
Learning Framework ◽  
The World ◽  
Hindi Language

On 11th March 2020, the World Health Organization (WHO) declared Corona Virus Disease of 2019 (COVID-19) as a pandemic. Over time, the exponential growth of this disease has highlighted a mixture of sentiments expressed by the general population from various parts of the world speaking varied languages. It is, therefore, essential to analyze the public sentiment during this wave of the pandemic. While much work prevails to determine the sentiment polarity for tweets related to COVID-19, expressed in the English language, we still need to work on public sentiments expressed in languages other than English. This paper proposes a framework, Covhindia, a deep-learning framework that performs sentiment polarity detection of tweets related to COVID-19 posted in the Hindi language on the Twitter platform. The proposed framework leverages machine translation on Hindi tweets and passes the translated data as input to a deep learning model which is trained on an English corpus of COVID-19 tweets posted from India [18]. The paper compares nine deep learning models' performances in classifying the sentiment polarity on an English dataset. Performance comparison of these architectures reveals that the BERT model had the best polarity detection accuracy on the English corpus. As part of testing the Covhindia’s accuracy in performing sentiment classification on Hindi tweets, the paper employs a separate dataset developed using a python library called Tweepy to extract Hindi tweets related to COVID-19. Experimental results reveal that Covhindia achieved state-of-the-art accuracy in classifying COVID-19 tweets posted in the Hindi language. The use of open-source machine translation tools paved the way for leveraging Covhindia for performing multilingual sentiment classification on COVID-19 tweets. For the benefit of the research community, the code and Jupyter Notebooks related to this paper are available on Github

scholarly journals Bare Face Person Recognition System using Deep Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 329-337
Author(s):  
Prachi Satpute
Keyword(s):  
Deep Learning ◽  
Public Space ◽  
Service Providers ◽  
Virus Disease ◽  
Face Mask ◽  
Control Measures ◽  
World Health ◽  
Camera System ◽  
Simplified Approach ◽  
The World

Nowadays, maintaining a good hygiene is very important to prevent many diseases like Corona Virus Disease (COVID-19). It has been rapidly affected our day-today life by disrupting the world trade and movements. The World Health Organization (WHO) recommend to the world that all people must wear a mask to prevent COVID-19. The use of masks is part of a comprehensive package of prevention and control measures that can limit the spread of certain respiratory viral diseases. Wearing a protective mask has become a new normal and beneficial for human being to avoid certain diseases. In the near future, many public service providers will ask the customers to wear the masks to provide their services. Therefore, face mask detection has become an important task to help global society. This paper introduce a simplified approach for face mask detection by using Deep learning and python as the programming language. We are also using Open-CV, to search for faces within a picture and then identifies if it has a mask on it or not. By using this system, the surveillance camera system present at some public Space will automatically detect whether the persons are wearing a mask or not.

COVID 19: REALS AND CHALLENGES AGAINST DOCTORS

2020 ◽  
Vol 2 (1) ◽  
pp. 65-87
Author(s):  
Zen Ahmad
Keyword(s):  
Virus Disease ◽  
Clinical Manifestations ◽  
World Health ◽  
Unknown Etiology ◽  
Wuhan City ◽  
Traditional Markets ◽  
Pneumonia Case ◽  
The World ◽  
New Type ◽  
Health Organization

Corona Virus Disease (Covid-19) is a contagious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) which was discovered in December 2019 in China. This disease can cause clinical manifestations in the airway, lung and systemic. The World Health Organization (WHO) representative of China reported a pneumonia case with unknown etiology in Wuhan City, Hubei Province, China on December 31, 2019. The cause was identified as a new type of coronavirus on January 7, 2020 with an estimated source of the virus from traditional markets (seafood market). ) Wuhan city

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

scholarly journals Application of Color Featuring and Deep Learning in Maize Plant Detection

2020 ◽  
Vol 12 (14) ◽  
pp. 2229
Author(s):  
Haojie Liu ◽  
Hong Sun ◽  
Minzan Li ◽  
Michihisa Iida
Keyword(s):  
Deep Learning ◽  
False Negative ◽  
Color Space ◽  
Performance Comparison ◽  
Maize Plant ◽  
Detection Accuracy ◽  
Learning Technology ◽  
Practical Applications ◽  
Feature Based ◽  
Detection Speed

Maize plant detection was conducted in this study with the goals of target fertilization and reduction of fertilization waste in weed spots and gaps between maize plants. The methods used included two types of color featuring and deep learning (DL). The four color indices used were excess green (ExG), excess red (ExR), ExG minus ExR, and the hue value from the HSV (hue, saturation, and value) color space, while the DL methods used were YOLOv3 and YOLOv3_tiny. For practical application, this study focused on performance comparison in detection accuracy, robustness to complex field conditions, and detection speed. Detection accuracy was evaluated by the resulting images, which were divided into three categories: true positive, false positive, and false negative. The robustness evaluation was performed by comparing the average intersection over union of each detection method across different sub–datasets—namely original subset, blur processing subset, increased brightness subset, and reduced brightness subset. The detection speed was evaluated by the indicator of frames per second. Results demonstrated that the DL methods outperformed the color index–based methods in detection accuracy and robustness to complex conditions, while they were inferior to color feature–based methods in detection speed. This research shows the application potential of deep learning technology in maize plant detection. Future efforts are needed to improve the detection speed for practical applications.

scholarly journals An Explainable approach to Deep Learning from CT-scans for Covid Identification

2021 ◽  
Author(s):  
Eduardo Soares ◽  
Plamen Angelov ◽  
Ziyang Zhang
Keyword(s):  
Deep Learning ◽  
World Health ◽  
Learning Approaches ◽  
Heat Map ◽  
Algorithm Performance ◽  
Decision Mechanism ◽  
The World ◽  
Public Health Officials ◽  
Decision Structure ◽  
Health Organization

The Covid-19 disease has spread widely over the whole world since the beginning of 2020. Following the epidemic which started in Wuhan, China on January 30, 2020 the World Health Organization (WHO) declared a global health emergency and a pandemic. Researchers of different disciplines work along with public health officials to understand the SARS-CoV-2 pathogenesis and jointly with the policymakers urgently develop strategies to control the spread of this new disease. Recent findings have observed specific image patterns from computed tomography (CT) for patients infected by SARS-CoV-2 which are distinct from the other pulmonary diseases. In this paper, we propose an explainable-by-design that has an integrated image segmentation mechanism based on SLIC that improves the algorithm performance and the interpretability of the resulting model. In order to evaluate the proposed approach, we used the SARS-CoV-2 CT scan dataset that we published recently and has been widely used in the literature. The proposed Super-xDNN could obtain statistically better results than traditional deep learning approaches as DenseNet-201 and Resnet-152. Furthermore, it also improved the explainability and interpretability of its decision mechanism when compared with the xDNN basis approach that uses the whole image as prototype. The segmentation mechanism of Super-xDNN favored a decision structure that is more close to the human logic. Moreover, it also allowed the provision of new insights as a heat-map which highlights the areas with highest similarities with Covid-19 prototypes, and an estimation of the area affected by the disease.

scholarly journals PKM Kemasan Produksi Cairan Desinfektan yang Ramah Lingkungan dan Higienis Sesuai Standar WHO

2020 ◽  
Vol 1 (1) ◽  
pp. 59-66
Author(s):  
Marjan Miharja ◽  
Wiend Sakti Myharto ◽  
Hendrikus Lermatin ◽  
Paternus Ndruru ◽  
Veni Florence Lakie ◽  
...  
Keyword(s):  
World Health Organization ◽  
Virus Disease ◽  
Public Awareness ◽  
Environmentally Friendly ◽  
World Health ◽  
New Normal ◽  
Corona Virus ◽  
The World ◽  
Service Activity ◽  
Health Organization

The spread of Covid-19 has become one of the people's concerns, starting in the city of Wuhan, China at the end of 2019 when this virus was discovered, the spread of the virus that the antidote has not yet been found is now out of control. More than 200 countries in the world have reported that their people have contracted the Covid-19 virus. Corona Virus Disease 19 has been declared a Global Public Health Emergency by the World Health Organization (WHO) on January 30, 2020. Conditions in Indonesia until Thursday, November 30, 2020, the number of people who tested positive for Covid-19 reached 538,883 cases, 450,518 people recovered and 16,945 of them died. This figure will continue to increase in line with the opinion of some epidemiologists and statistics that a pandemic outbreak will not end quickly. The purpose of this community service activity is to realize one of the contents of Presidential Instruction Number 4 of 2020, namely "Rrefocussing activities, reallocation of budgets and procurement of goods and services in order to accelerate the handling of Corona Virus Disease 2019 (COVID-19)", namely by making and distributing fluids. Disinfectant that is safe and environmentally friendly and recommended by the BPOM and the World Health Organization (WHO) to help people face the New Normal era. The result of this service activity is a disinfectant liquid that is safe and environmentally friendly and is able to anticipate the spread of covid-19 and increase public awareness of the Covid-19 Virus in the face of the New Normal era.

scholarly journals Deep learning for classification of pediatric chest radiographs by WHO’s standardized methodology

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253239
Author(s):  
Yiyun Chen ◽  
Craig S. Roberts ◽  
Wanmei Ou ◽  
Tanaz Petigara ◽  
Gregory V. Goldmacher ◽  
...  
Keyword(s):  
Deep Learning ◽  
World Health Organization ◽  
Characteristic Curve ◽  
Learning Model ◽  
Observer Agreement ◽  
World Health ◽  
Widespread Application ◽  
The World ◽  
Health Organization ◽  
Deep Learning Model

Background The World Health Organization (WHO)-defined radiological pneumonia is a preferred endpoint in pneumococcal vaccine efficacy and effectiveness studies in children. Automating the WHO methodology may support more widespread application of this endpoint. Methods We trained a deep learning model to classify pneumonia CXRs in children using the World Health Organization (WHO)’s standardized methodology. The model was pretrained on CheXpert, a dataset containing 224,316 adult CXRs, and fine-tuned on PERCH, a pediatric dataset containing 4,172 CXRs. The model was then tested on two pediatric CXR datasets released by WHO. We also compared the model’s performance to that of radiologists and pediatricians. Results The average area under the receiver operating characteristic curve (AUC) for primary endpoint pneumonia (PEP) across 10-fold validation of PERCH images was 0.928; average AUC after testing on WHO images was 0.977. The model’s classification performance was better on test images with high inter-observer agreement; however, the model still outperformed human assessments in AUC and precision-recall spaces on low agreement images. Conclusion A deep learning model can classify pneumonia CXR images in children at a performance comparable to human readers. Our method lays a strong foundation for the potential inclusion of computer-aided readings of pediatric CXRs in vaccine trials and epidemiology studies.

scholarly journals Medicinal Plants for Prevention and Treatment of Coronavirus Disease

2021 ◽  
pp. 74-85
Author(s):  
Onder Otlu ◽  
Ceyhun Bereketoglu ◽  
Tugba Raika Kiran ◽  
Aysun Bay Karabulut
Keyword(s):  
Medicinal Plants ◽  
Virus Disease ◽  
White Blood Cells ◽  
Antioxidant Properties ◽  
Antiviral Agent ◽  
World Health ◽  
Chronic Infections ◽  
Corona Virus ◽  
The World ◽  
Health Organization

The 2019-nCoV (new Corona Virus) outbreak was announced by the World Health Organization (WHO), the International Emergency Public Health Organization, on January 30, 2020, and WHO reported the 2019-nCoV pathogen to SARSCoV-2 and Corona virus Disease 2019 on 12 February. (COVID2019). COVID-19 was officially recognized as a Pandemic by WHO on March 11, 2020. Plants have been used all over the world as traditional medicine for centuries to treat many chronic infections, including viral diseases. In recent years, scientists have been trying to verify the potential of functional compounds to protect human health and cure diseases with their research on functional and nutraceutical foods. Traditional medicinal plants have a long history supported by many researches such as maintaining a healthy life, toxins taken in daily life, fighting and preventing diseases, and longevity. Studies on the antiviral, antiinflammatory and antioxidant properties of ethnomedical plants and natural phytochemicals can be considered as a great potential drug source against various ailments as well as Covid-19 treatment. Based on this study, plant extracts increase immunity with the increase in the number of white blood cells and lymphocytes in viral infections such as Covid-19, which can lead to fatal consequences, regulating the production and release of proinflammatory cytokines, showing an anti-inflammatory effect with a decrease in the C-reactive protein and erythrocyte sedimentation rate, It appears to have a positive effect such as interfering with the development and potential antiviral agent activity. In this study, phytochemicals and effects associated with COVID-19 infection were reviewed.

scholarly journals Designing for deep learning in the context of digital and social media

Comunicar ◽  
2019 ◽  
Vol 27 (58) ◽  
pp. 9-18 ◽  
Author(s):  
James-Paul Gee ◽  
Moisés Esteban-Guitart
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
World Health ◽  
Human Beings ◽  
Redes Sociales ◽  
Living Things ◽  
The World ◽  
Mind And Body ◽  
Health Organization

There is today a great deal of controversy over digital and social media. Even leaders in the tech industry are beginning to decry the time young people spend on smartphones and social networks. Recently, the World Health Organization proposed adding “gaming disorder” to its official list of diseases, defining it as a pattern of gaming behavior so severe that it takes “precedence over other life interests”. At the same time, many others have celebrated the positive properties of video games, social media, and social networks. This paper argues that a deeper understanding of human beings is needed to design for deep learning. For the purposes of this study “design for deep learning” means helping people matter and find meaning in ways that make them and others healthy in mind and body, while improving the state of the world for all living things, with due respect for truth, sensation, happiness, imagination, individuality, diversity, and the future. In particular, fifteen features related to human nature are suggested based on recent scientific developments to answer the question: What is a human being? Consequently, proposals that are linked to learning and transformation, as well as social improvement, should fit with the ways in which humans, as specific sorts of biological and social creatures, learn best (or can learn at all) and can change for the better. En la actualidad existe una nutrida controversia en relación a los medios de comunicación sociales y digitales que ha llevado, incluso, a censurar la utilización de las redes sociales y los móviles por parte de líderes en la industria tecnológica. En este sentido, la Organización Mundial para la Salud ha propuesto añadir el «desorden del juego» a su listado de enfermedades, definiéndolo como un modelo de comportamiento de juego tan severo que se impone como «preferencia sobre otros intereses». Al mismo tiempo, distintos académicos han enfatizado los aspectos positivos derivados de las redes sociales y los videojuegos. En este artículo se argumenta que es necesaria una mejor comprensión del ser humano para poder implementar lo que aquí se define como diseño para el aprendizaje profundo. El «diseño para el aprendizaje profundo» está encaminado al reconocimiento de las personas y el desarrollo de sentidos saludables, individual y colectivamente, así como la mejora, en general, del estado del mundo para todos los seres vivos, según principios de verdad, felicidad, imaginación, individualidad, diversidad y futuro. En particular, se sugieren quince características basadas en desarrollos científicos que responden a la pregunta: ¿Qué es un ser humano? Consecuentemente, propuestas vinculadas al aprendizaje y la transformación y mejora social deben ser coherentes con dichas características que permiten definir cómo las personas, en tanto que organismos biológicos y sociales, aprenden o pueden aprender óptimamente, así como cambiar para mejorar.

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