Search

A global pandemic on March 11th of 2020, which was initially renowned by the World Health Organization (WHO) revealed the coronavirus (the COVID-19 epidemic). Coronavirus was flown in -December 2019 in Wuhan, Hubei region in China. Currently, the situation is enlarged by the infection in more than 200 countries all over the world. In this situation it was rising into huge forms in Bangladesh too. Modulated with a public dataset delivered by the IEDCR health authority, we have produced a sustainable prognostic method of COVID-19 outbreak in Bangladesh using a deep learning model. Throughout the research, we forecasted up to 30 days in which per day actual prediction was confirmed, death and recoveries number of people. Furthermore, we illustrated that long short-term memory (LSTM) demands the actual output trends among time series data analysis with a controversial study that exceeds random forest (RF) regression and support vector regression (SVR), which both are machine learning (ML) models. The current COVID-19 outbreak in Bangladesh has been considered in this paper. Here, a well-known recurrent neural network (RNN) model in order to referred by the LSTM network that has forecasted COVID-19 cases on per day infected scenario of Bangladesh from May 15th of 2020 till June 15th of 2020. Added with a comparative study that drives into the LSTM, SVR, RF regression which is processed by the RMSE transmission rate. In all respects, in Bangladesh the gravity of COVID-19 has become excessive nowadays so that depending on this situation public health sectors and common people need to be aware of this situation and also be able to get knowledge of how long self-lockdown will be maintained. So far, to the best of our knowledge LSTM based time series analysis forecasting infectious diseases is a well-done formula [ABSTRACT FROM AUTHOR]

The Coronavirus epidemic first appeared in Wuhan-China on December, 31st, 2019. This has put the world's hospitals, clinics, testing laboratories and health administrations under pressure. As of April, 04th, 2020, the World Health Organization reported more than 167515 confirmed cases in more than 100 countries worldwide. The diagnosis of the epidemic will increase the burden on overburdened testing laboratories. Several screening methods have been proposed in parallel in order to facilitate and, above all, to make rapid diagnosis easier. At this level, X-Ray images seem to be a good accompanying solution for emerging countries to help rapid screening. The solution proposed in this paper consists on a collaborative and smart platform based on the Convolutional Neural Network for Classification and Detection namely VGG16. The platform ensures the fast download of the X-Ray image, with the entry of the patient's personal information followed by the launch of a 5 seconds test. The platform generates, as a result, a PDF file containing all patient information. [ABSTRACT FROM AUTHOR]

• A deep learning COVID-19 diagnostic system is developed using state-of-the-art deep learning architectures. • A detailed analysis of different methods used for detecting COVID-19 is presented in this paper. • Chest X-rays from different sources are collected to build a robust classification model. • The developed diagnostic model provided efficient results, even with a limited number of input images. • The results produced in this work are validated by an expert radiologist. In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients' X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible. [ABSTRACT FROM AUTHOR]

COVID-19 infection was reported in December 2019 at Wuhan, China. This virus critically affects several countries such as the USA, Brazil, India and Italy. Numerous research units are working at their higher level of effort to develop novel methods to prevent and control this pandemic scenario. The main objective of this paper is to propose a medical decision support system using the implementation of a convolutional neural network (CNN). This CNN has been developed using EfficientNet architecture. To the best of the authors' knowledge, there is no similar study that proposes an automated method for COVID-19 diagnosis using EfficientNet. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet and 10-fold stratified cross-validation. This paper presents two main experiments. First, the binary classification results using images from COVID-19 patients and normal patients are shown. Second, the multi-class results using images from COVID-19, pneumonia and normal patients are discussed. The results show average accuracy values for binary and multi-class of 99.62% and 96.70%, respectively. On the one hand, the proposed CNN model using EfficientNet presents an average recall value of 99.63% and 96.69% concerning binary and multi-class, respectively. On the other hand, 99.64% is the average precision value reported by binary classification, and 97.54% is presented in multi-class. Finally, the average F1-score for multi-class is 97.11%, and 99.62% is presented for binary classification. In conclusion, the proposed architecture can provide an automated medical diagnostics system to support healthcare specialists for enhanced decision making during this pandemic scenario. • An automated system to support the diagnosis of COVID-19 patients using EfficientNet. • The proposed method has been validated using 10-fold cross-validation. • An external dataset also has been used for validation. • An accuracy of 99.62% and 96.70% is reported for binary and multi-class classification. [ABSTRACT FROM AUTHOR]

• Evaluation of proposed COVDC-Net method for both 3-class and 4-class classification. • A novel hybrid fusion-based Deep Convolutional Neural Network model is presented. • The proposed model helps radiologists to distinguish between types of pneumonia and COVID infections. • The performance of COVDC-Net method is better than several other methods proposed for detection of COVID-19. Coronavirus disease is a viral infection caused by a novel coronavirus (CoV) which was first identified in the city of Wuhan, China somewhere in the early December 2019. It affects the human respiratory system by causing respiratory infections with symptoms (mild to severe) like fever, cough, and weakness but can further lead to other serious diseases and has resulted in millions of deaths until now. Therefore, an accurate diagnosis for such types of diseases is highly needful for the current healthcare system. In this paper, a state of the art deep learning method is described. We propose COVDC-Net, a Deep Convolutional Network-based classification method which is capable of identifying SARS-CoV-2 infected amongst healthy and/or pneumonia patients from their chest X-ray images. The proposed method uses two modified pre-trained models (on ImageNet) namely MobileNetV2 and VGG16 without their classifier layers and fuses the two models using the Confidence fusion method to achieve better classification accuracy on the two currently publicly available datasets. It is observed through exhaustive experiments that the proposed method achieved an overall classification accuracy of 96.48% for 3-class (COVID-19, Normal and Pneumonia) classification tasks. For 4-class classification (COVID-19, Normal, Pneumonia Viral, and Pneumonia Bacterial) COVDC-Net method delivered 90.22% accuracy. The experimental results demonstrate that the proposed COVDC-Net method has shown better overall classification accuracy as compared to the existing deep learning methods proposed for the same task in the current COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

For intelligent risk prediction and optimization in the shield tunnel excavation under uncertainty, this paper proposes a data-driven decision support framework based on the integration of deep neural network and gradient descent technique (DNN-GDO). More specifically, the DNN architecture for modeling non-linear relationships between influential factors and potential risk is superior in concurrently extracting and learning features from multi-variable inputs, while the GDO technique as a solution for the multi-objective optimization problem can be directly applied in the established DNN model to provide suggestions for reducing or preventing potential risks in the tunneling environment. To reveal the effectiveness of the developed approach, it is performed in a practical tunnel construction case in the Wuhan metro system, China. Sixteen decision variables belong to four categories of tunnel design, geology, the tunnel boring machine (TBM) operation, and surrounding building are taken into account, which are closely relevant to two significant risks named the accumulative ground settlement and building tilt rate. It has been found that the end-to-end learning pipeline based on DNN can yield high accuracy in the multi-output regression task, resulting in the mean-square error (MSE) of 0.737 and 0.002 for the two target risks, respectively. GDO is proven useful in not only finding the optimal solutions for effective risk control but also uncovering the key features for a better explanation of the optimization process. It has been proved that optimal solutions derived from GDO can flexibly guide to adjust input variables of interest to achieve the minimal risk degree. Besides, cutter rotating speed and ground volume of TBM along with the relative horizontal distance of buildings turn out to be the top 3 critical features contributing more to the tunnel-induced risk mitigation, which deserve more attention. In brief, the novel DNN-GDO decision tool supports to formulate meaningful strategies objectively and efficiently for tunneling safety assurance, which no longer largely relies on time-consuming and laborious manual assessment. • A data-driven approach based on deep neural network and gradient descent technique is proposed. • Gradient descent technique aims to provide suggestions for preventing potential risks in tunnels. • A practical tunnel construction case in China is applied for demonstration purposes. • Deep learning model can yield a high accuracy in the multi-output regression prediction task. • The derived optimal solutions can flexibly guide to adjust input variables to achieve minimal risk. [ABSTRACT FROM AUTHOR]

Predicting unsafe behaviour in advance can enable remedial measures to be put in place to mitigate likely accidents on construction sites. Prevailing safety studies in construction tend to be retrospective and focus on examining the conditions that contribute to unsafe behaviour from a psychological perspective. While such studies are warranted, they can also not visually comprehend the dynamic and complex conditions that influence unsafe behaviour. In this paper, we aim to contribute to filling this void and, in doing so, combine computer vision with Long-Short Term Memory (LSTM) to predict unsafe behaviours from videos automatically. Our proposed approach for predicting unsafe behaviour is based on: (1) tracking people using a SiamMask; (2) predicting the trajectory of people using an improved Social-LSTM; and (3) predicting unsafe behaviour using Franklin's point inclusion polygon (PNPoly) algorithm. We use the Wuhan metro project as a case to evaluate our approach's feasibility and effectiveness. Our adopted SiamMask method outperforms current techniques used for tracking people. Additionally, our improved Social-LSTM can achieve higher accuracy on trajectory prediction than other methods (e.g., Social-GAN). The research findings demonstrate that our developed computer vision approach can be used to accurately predict unsafe behaviour on construction sites. [ABSTRACT FROM AUTHOR]

SARS-CoV-2, a novel strain of coronavirus' also called coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral infection emerged in December 2019 in Wuhan, a city in China's Hubei province without an obvious cause. Very rapidly it spread across the globe (over 200 countries and territories) and finally on 11 March 2020 World Health Organisation characterized it as a "pandemic". Although it has low mortality of around 3% as of 18 May 2021 it has already infected 164,316,270 humans with 3,406,027 unfortunate deaths. Undoubtedly the world was rocked by the COVID-19 pandemic, but researchers rose to all manner of challenges to tackle this pandemic by adopting the shreds of evidence of ML and AI in previous epidemics to develop novel models, methods, and strategies. We aim to provide a deeper insight into the convolutional neural network which is the most notable and extensively adopted technique on radiographic visual imagery to help expert medical practitioners and researchers to design and finetune their state-of-the-art models for their applicability in the arena of COVID-19. In this study, a deep convolutional neural network, its layers, activation and loss functions, regularization techniques, tools, methods, variants, and recent developments were explored to find its applications for COVID-19 prognosis. The pipeline of a general architecture for COVID-19 prognosis has also been proposed. This paper highlights recent studies of deep CNN and its applications for better prognosis, detection, classification, and screening of COVID-19 to help researchers and expert medical community in multiple directions. It also addresses a few challenges, limitations, and outlooks while using such methods for COVID-19 prognosis. The recent and ongoing developments in AI, MI, and deep learning (Deep CNN) has shown promising results and significantly improved performance metrics for screening, prediction, detection, classification, forecasting, medication, treatment, contact tracing, etc. to curtail the manual intervention in medical practice. However, the research community of medical experts is yet to recognize and label the benchmark of the deep learning framework for effective detection of COVID-19 positive cases from radiology imagery. [ABSTRACT FROM AUTHOR]

Virus diseases are a continued threat to human health in both community and healthcare settings. The current virus disease COVID-19 outbreak raises an unparalleled public health issue for the world at large. Wuhan is the city in China from where this virus came first and, after some time the whole world was affected by this severe disease. It is a challenge for every country's people and higher authorities to fight with this battle due to the insufficient number of resources. On-going assessment of the epidemiological features and future impacts of the COVID-19 disease is required to stay up-to-date of any changes to its spread dynamics and foresee needed resources and consequences in different aspects as social or economic ones. This paper proposes a prediction model of confirmed and death cases of COVID-19. The model is based on a deep learning algorithm with two long short-term memory (LSTM) layers. We consider the available infection cases of COVID-19 in India from January 22, 2020, till October 9, 2020, and parameterize the model. The proposed model is an inference to obtain predicted coronavirus cases and deaths for the next 30 days, taking the data of the previous 260 days of duration of the pandemic. The proposed deep learning model has been compared with other popular prediction methods (Support Vector Machine, Decision Tree and Random Forest) showing a lower normalized RMSE. This work also compares COVID-19 with other previous diseases (SARS, MERS, h1n1, Ebola, and 2019-nCoV). Based on the mortality rate and virus spread, this study concludes that the novel coronavirus (COVID-19) is more dangerous than other diseases. • A predictive model of confirmed and death cases of COVID-19 is proposed. • The predictive model is a deep learning model based on LSTM networks. • COVID-19 cases and deaths for the next 30 days are predicted. • The spread and mortality rates of five different virus diseases are compared. • It is concluded that COVID-19 is more dangerous than SARS, MERS, h1n1, and Ebola. [ABSTRACT FROM AUTHOR]

The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%–40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a series of challenges (e.g. data discrepancy, testing time-effectiveness of model, data security, etc.) in this particular situation and deploy the system in four weeks. In addition, since the proposed AI system provides the priority of each CT image with probability of infection, the physicians can confirm and segregate the infected patients in time. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we are able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases. • We build a system for detecting COVID-19 infection regions. • We build a dataset of COVID-19. • It is easy to distinguish pneumonia from healthy cases. [ABSTRACT FROM AUTHOR]

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. In computer science, this represents a typical problem of machine learning over incomplete or limited data in early epidemic Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal–spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min–max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic. • Composite Monte-Carlo (CMC) simulation is a forecasting method. • A case study of using CMC through deep learning network is developed. • Decision makers are benefited from a better fitted Monte Carlo outputs. • Novel Coronavirus Epidemic is studied. [ABSTRACT FROM AUTHOR]

Quality management in shield tunneling projects is a challenging problem. To improve the quality of segment erection, the shield driver must constantly adjust the shield's attitude and position to reduce a snakelike motion for fitting the design tunnel axis based on the driver's experience, which is untimely and less reliable during excavation. Considering the disadvantage of this control method, this paper presents a predictive framework for the attitude and position in shield tunneling by applying a hybrid deep learning model. This framework contains a wavelet transform noise filter, convolutional neural network feature extractor, and long short-term memory predictor for determining the attitude and position of the shield machine in the future. The prediction framework is tested with the collected data of Mixshield operated in the river-crossing tunnel project of Yangtze Sanyang Road, Wuhan, China. Six variables characterizing the shield attitude and position are selected to validate the feasibility and performance of our method. Results reveal that the proposed model outperforms the other three similar models in predictive accuracy and provides decision support for adjusting the attitude and position in shield tunneling. • A new method for predicting the attitude and position of shield machine was proposed. • A hybrid deep learning model consisting of wavelet transform, CNN, and LSTM was constructed for the prediction task. • The on-site data collected from the largest river-crossing tunnel in China has been tested. • The prediction results comparing with other three models and the potential practice value were discussed. [ABSTRACT FROM AUTHOR]