Measuring emotions in the COVID-19 real world worry dataset

The COVID-19 pandemic is having a dramatic impact on societies and economies around the world. With various measures of lockdowns and social distancing in place, it becomes important to understand emotional responses on a large scale. In this paper, we present the first ground truth dataset of emotional responses to COVID-19. We asked participants to indicate their emotions and express these in text. This resulted in the Real World Worry Dataset of 5,000 texts (2,500 short + 2,500 long texts). Our analyses suggest that emotional responses correlated with linguistic measures. Topic modeling further revealed that people in the UK worry about their family and the economic situation. Tweet-sized texts functioned as a call for solidarity, while longer texts shed light on worries and concerns. Using predictive modeling approaches, we were able to approximate the emotional responses of participants from text within 14% of their actual value. We encourage others to use the dataset and improve how we can use automated methods to learn about emotional responses and worries about an urgent problem. © ACL 2020.All right reserved.

An Overview of Latest Methods in Deep Learning for Automated Identification of COVID-19 Infection from Chest X-rays and CT-Scan Images

COVID-19 is a widely spread pandemic that crippled almost all countries and created a critical impact on the quality of human life. The outbreak was started in 2019, and during past years, the coronavirus has undergone various mutations and generated dangerous variants. The best path to control the spread of the coronavirus is with rapid testing and isolation. Among various tests, X-rays and CT-scan images captured from the chest and lungs attracted clinicians and researchers with high sensitivity. Many researchers have developed different automated methods for the identification of COVID-19. Among various automated methods, deep learning-based techniques obtained favourable results in the automation of COVID-19 detection. This paper proposes a detailed review of the latest deep learning methods that are utilized for COVID-19 identification from X-rays and CT scans. The recent publications are carefully choosed from the literature, and a detailed review is conducted. The literature search was conducted in various databases such as Google Scholar, PubMed, and Scopus. The proposed review will be helpful for future researchers in the area of COVID-19 research. © 2022 IEEE.

Benchmarking Crowd-Counting Techniques across Image Characteristics

Crowd-counting is a longstanding computer vision used in estimating the crowd sizes for security purposes at public protests in streets, public gatherings, for collecting crowd statistics at airports, malls, concerts, conferences, and other similar venues, and for monitoring people and crowds during public health crises (such as the one caused by COVID-19). Recently, the performance of automated methods for crowd-counting from single images has improved particularly due to the introduction of deep learning techniques and large labelled training datasets. However, the robustness of these methods to varying imaging conditions, such as weather, image perspective, and large variations in the crowd size has not been studied in-depth in the open literature. To address this gap, a systematic study on the robustness of four recently developed crowd-counting methods is performed in this paper to evaluate their performance with respect to variable (real-life) imaging scenarios that include different event types, weather conditions, image sources and crowd sizes. It is shown that the performance of the tested techniques is degraded in unclear weather conditions (i.e., fog, rain, snow) and also on images taken from large distances by drones. On the opposite, clear weather conditions, crowd-counting methods can provide accurate and usable results. © 2021 Electrotechnical Society of Slovenia. All rights reserved.

Covid-19 Detection Based on the Fine-Tuned MobileNetv2 Through Lung X-rays

Covid-19 is has become an epidemic, which is affecting millions of people around the world. The common symptoms of Covid-19 are cough and fever, which are very similar to the normal Flu. Covid-19 spreads fast and is devastating for people of all ages especially elderly and people having weak immune system. The standard technique used for Covid-19 detection is real-time polymerase chain reaction (RT-PCR) test. However, RT-PCR is unreliable for Covid-19 detection as it takes long time to detect the disease and it produces considerable number of false positive cases. Therefore, we need to propose an automated and reliable method for Covid-19 detection. Radiographic images are widely used for the detection of various pulmonary diseases such as lung cancer, asthma, pneumonia, etc. We also used chest x-rays for the diagnosis of Covid-19. In this paper, we employed two deep learning models such as SqueezeNet and MobileNetv2 and fine-tuned to check the classification performance. Moreover, we performed data augmentation technique to increase the amount of data and avoid the overfitting of model. We evaluated the performance of the proposed system on standard dataset Covid-19 Radiography dataset that is publicly available. More specifically, we achieved remarkable accuracy of 97%, precision of 95.19%, recall of 100%, specificity of 95%, area under the curve of 98.93%, and F1-score of 97.06% on MobileNetv2. Experimental results and comparative analysis with other existing methods demonstrate that our method is reliable than PT-PCR and other existing state-of-the-art methods for Covid-19 detection. © 2021 IEEE.