15 research outputs found
Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients.
COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images
مطالعهی آزمایشگاهی الگوی آبشستگی ناشی از استقرار پایههای دوتایی همگرا و واگرا و همراستا با جریان در موقعیتهای مختلف قوس ۱۸۰ درجه
One of the issues in river engineering is bridge protection in a way that bridges suffer the minimum damage during a flood. In many cases, due to the limitations, bridges are constructed on a river bend. The purpose of this research is to investigate the effect of constructing convergent and divergent (V-shaped \& A-shaped) coupled bridge piers and their position in parallel to the flow on a steep 180-degree bend bed topography. This research was performed on a laboratory channel at Persian Gulf University of Bushehr, Iran. The channel has a width of 1 meter, and a steep bend with a central radius to channel width ratio of 2 and a 180-degree bend. In the upstream and downstream of the bed, there are direct routes with lengths of 6.5 and 5.1 meters, respectively. For the experiments, PVC piers with 5 cm diameter a 21-degree angle to the vertical axis, materials with an average diameter of 1.5 mm and standard deviation of 1.14 were used. All the experiments were performed with flow velocity to critical velocity ratio of 0.98 (U/Uc=0.98) and constant depth of 18 cm at the beginning of the bend and 70 l/s discharge. The results show that the maximum depth of the scour hole occurs adjacent to the V-shaped coupled piers established at the position of 90 degree. By changing the piers from V-shaped to A-shaped at 60 and 90 degrees positions, the maximum scour hole is transfered from the adjacency of the upstream pier to the adjacency of the downstream pier, and by changing the position of the piers from the bend's upstream towards the downstream, the development of the maximum scour hole decreases. The highest scour depths were measured in the main hole and the second scour hole and also the highest sedimentation were measured as 0.97, 0.93 and 0.58 times the flow depth at the starting point of the bend respectively. In this paper, the results are discussed and analyzed
Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods
Several researchers benefited from the EU supported project Sus-tainable Process Integration Laboratory - SPIL funded as project No. CZ.02.1.01/0.0/0.0/15_003/0000456, by Czech Republic Operational Programme Research and Development, Education, Priority 1: Strengthening capacity for quality research, based on the SPIL project.This work was also partly supported by the Ministerio de Ciencia e Innovacion (Espana) /FEDER under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250 and A-TIC-080-UGR18 projects.The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread
worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the
number of new cases and deaths during this period can be a useful step in predicting the costs and facilities
required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day
ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the
investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such
a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are
examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU.
The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths
in Australia and Iran countries.
This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning
methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate
time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for
prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors
than other models. A several error evaluation metrics are presented to compare all models, and finally, the
superiority of bidirectional methods is determined. This research could be useful for organisations working
against COVID-19 and determining their long-term plans.European Commission CZ.02.1.01/0.0/0.0/15_003/0000456Czech Republic Operational Programme Research and Development, Education, Priority 1: Strengthening capacity for quality research, based on the SPIL projectMinisterio de Ciencia e Innovacion (Espana) /FEDER RTI2018-098913-B100Junta de AndaluciaEuropean Commission CV20-45250
A-TIC-080-UGR1