Topic2Labels: A framework to annotate and classify the social media data through LDA topics and deep learning models for crisis response

The abundant use of social media impacts every aspect of life, including crisis management. Disaster management needs real-time data to be used in machine learning and deep learning models to aid their decision making. Mostly the data that is newly generated from social media is unstructured and unlabeled. Current text classification models based on supervised deep learning models heavily rely on human-labeled data that very small size and imbalanced in the context of disasters, ultimately affecting the generalization of models. In this study, we propose Topic2labels (T2L) framework which provides an automated way of labeling the data through LDA (latent dirichlet allocation) topic modelling approach and utilize Bert (the bidirectional encoder representation from transformer) embeddings for construction of feature vector to be employed to classify the data contextually. Our framework consists of three layers. In the first layer, we adopt LDA to generate the topics from the data, and develop a new algorithm to rank the topics, and map the highest ranked dominant topic into label to annotate the data. In the second layer, we transform the labeled text into feature representation through Bert embeddings and in the third layer we leveraged deep learning models as classifiers to classify the textual data into multiple categories. Experimental results on crisis-related datasets show that our framework performs better in terms of classification performance and yields improvement as compared to other baseline approaches.

Role of High Resolution Computed Tomography chest in the diagnosis and evaluation of COVID -19 patients -A systematic review and meta-analysis.

BACKGROUND: Recent studies reported that CT scan findings could be implicated in the diagnosis and evaluation of COVID-19 patients. OBJECTIVE: To identify the role of High-Resolution Computed Tomography chest and summarize characteristics of chest CT imaging for the diagnosis and evaluation of SARS-CoV-2 patients. METHODOLOGY: Google Scholar, PubMed, Science Direct, Research Gate and Medscape were searched up to 31 January 2020 to find relevant articles which highlighted the importance of thoracic computed tomography in the diagnosis as well as the assessment of SARS-CoV-2 infected patients. HRCT abnormalities of SARS-CoV-2 patients were extracted from the eligible studies for meta-analysis. RESULTS: In this review, 28 studies (total 2655 patients) were included. Classical findings were Ground Glass Opacities (GGO) (71.64 %), GGO with consolidation (35.22 %), vascular enlargement (65.41 %), subpleural bands (52.54 %), interlobular septal thickening (43.28 %), pleural thickening (38.25 %), and air bronchograms sign (35.15 %). The common anatomic distribution of infection was bilateral lung infection (71.55 %), peripheral distribution (54.63 %) and multiple lesions (74.67 %). The incidences were higher in in the left lower lobe (75.68 %) and right lower lobe (73.32 %). A significant percentage of patients had over 2 lobes involvement (68.66 %). CONCLUSION: Chest CT-scan is a helpful modality in the early detection of COVID-19 pneumonia. The GGO in the peripheral areas of lungs with multiple lesions is the characteristic pattern of COVID-19. The correct interpretation of HRCT features makes it easier to detect COVID-19 even in the early phases and the disease progression can also be accessed with the help of the follow-up chest scans.

IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19.