Deep Ensemble Model for COVID-19 Diagnosis and Classification Using Chest CT Images

Coronavirus disease 2019 (COVID-19) has spread worldwide, and medicinal resources have become inadequate in several regions. Computed tomography (CT) scans are capable of achieving precise and rapid COVID-19 diagnosis compared to the RT-PCR test. At the same time, artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), find it useful to design COVID-19 diagnoses using chest CT scans. In this aspect, this study concentrates on the design of an artificial intelligence-based ensemble model for the detection and classification (AIEM-DC) of COVID-19. The AIEM-DC technique aims to accurately detect and classify the COVID-19 using an ensemble of DL models. In addition, Gaussian filtering (GF)-based preprocessing technique is applied for the removal of noise and improve image quality. Moreover, a shark optimization algorithm (SOA) with an ensemble of DL models, namely recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), is employed for feature extraction. Furthermore, an improved bat algorithm with a multiclass support vector machine (IBA-MSVM) model is applied for the classification of CT scans. The design of the ensemble model with optimal parameter tuning of the MSVM model for COVID-19 classification shows the novelty of the work. The effectiveness of the AIEM-DC technique take place on benchmark CT image data set, and the results reported the promising classification performance of the AIEM-DC technique over the recent state-of-the-art approaches.

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Multi-Regional Online Car-Hailing Order Quantity Forecasting Based on the Convolutional Neural Network

Information ◽

10.3390/info10060193 ◽

2019 ◽

Vol 10 (6) ◽

pp. 193 ◽

Cited By ~ 1

Author(s):

Zihao Huang ◽

Gang Huang ◽

Zhijun Chen ◽

Chaozhong Wu ◽

Xiaofeng Ma ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Travel Demand ◽

Short Term Memory ◽

Demand Forecasting ◽

Image Feature ◽

Support Vector ◽

Data Set ◽

Demand Distribution ◽

Demand Forecasting Model

With the development of online cars, the demand for travel prediction is increasing in order to reduce the information asymmetry between passengers and drivers of online car-hailing. This paper proposes a travel demand forecasting model named OC-CNN based on the convolutional neural network to forecast the travel demand. In order to make full use of the spatial characteristics of the travel demand distribution, this paper meshes the prediction area and creates a travel demand data set of the graphical structure to preserve its spatial properties. Taking advantage of the convolutional neural network in image feature extraction, the historical demand data of the first twenty-five minutes of the entire region are used as a model input to predict the travel demand for the next five minutes. In order to verify the performance of the proposed method, one-month data from online car-hailing of the Chengdu Fourth Ring Road are used. The results show that the model successfully extracts the spatiotemporal features of the data, and the prediction accuracies of the proposed method are superior to those of the representative methods, including the Bayesian Ridge Model, Linear Regression, Support Vector Regression, and Long Short-Term Memory networks.

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LSTM-based soft sensor design for oxygen content of flue gas in coal-fired power plant

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220932390 ◽

2020 ◽

pp. 014233122093239

Author(s):

Hongguang Pan ◽

Tao Su ◽

Xiangdong Huang ◽

Zheng Wang

Keyword(s):

Power Plant ◽

Oxygen Content ◽

Flue Gas ◽

Oxygen Sensor ◽

Short Term Memory ◽

Support Vector ◽

Data Sets ◽

Auxiliary Variables ◽

Data Set ◽

Lstm Network

To address problems of high cost, complicated process and low accuracy of oxygen content measurement in flue gas of coal-fired power plant, a method based on long short-term memory (LSTM) network is proposed in this paper to replace oxygen sensor to estimate oxygen content in flue gas of boilers. Specifically, first, the LSTM model was built with the Keras deep learning framework, and the accuracy of the model was further improved by selecting appropriate super-parameters through experiments. Secondly, the flue gas oxygen content, as the leading variable, was combined with the mechanism and boiler process primary auxiliary variables. Based on the actual production data collected from a coal-fired power plant in Yulin, China, the data sets were preprocessed. Moreover, a selection model of auxiliary variables based on grey relational analysis is proposed to construct a new data set and divide the training set and testing set. Finally, this model is compared with the traditional soft-sensing modelling methods (i.e. the methods based on support vector machine and BP neural network). The RMSE of LSTM model is 4.51% lower than that of GA-SVM model and 3.55% lower than that of PSO-BP model. The conclusion shows that the oxygen content model based on LSTM has better generalization and has certain industrial value.

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Employment of Artificial Intelligence Based on Routine Laboratory Results for the Early Diagnosis of Multiple Myeloma

Frontiers in Oncology ◽

10.3389/fonc.2021.608191 ◽

2021 ◽

Vol 11 ◽

Author(s):

Wei Yan ◽

Hua Shi ◽

Tao He ◽

Jian Chen ◽

Chen Wang ◽

...

Keyword(s):

Artificial Intelligence ◽

Multiple Myeloma ◽

Early Diagnosis ◽

Machine Learning Algorithms ◽

Support Vector ◽

Diagnostic Model ◽

Routine Laboratory ◽

Data Set ◽

Hepatic Diseases ◽

Laboratory Results

ObjectiveIn order to enhance the detection rate of multiple myeloma and execute an early and more precise disease management, an artificial intelligence assistant diagnosis system is developed.Methods4,187 routine blood and biochemical examination records were collected from Shengjing Hospital affiliated to China Medical University from January 2010 to January 2020, which include 1,741 records of multiple myeloma (MM) and 2,446 records of non-myeloma (infectious diseases, rheumatic immune system diseases, hepatic diseases and renal diseases). The data set was split into training and test subsets with the ratio of 4:1 while connecting hemoglobin, serum creatinine, serum calcium, immunoglobulin (A, G and M), albumin, total protein, and the ratio of albumin to globulin data. An early assistant diagnostic model of MM was established by Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Deep Neural Networks (DNN), and Random Forest (RF). Out team calculated the precision and recall of the system. The performance of the diagnostic model was evaluated by using the receiver operating characteristic (ROC) curve.ResultsBy designing the features properly, the typical machine learning algorithms SVM, DNN, RF and GBDT all performed well. GBDT had the highest precision (92.9%), recall (90.0%) and F1 score (0.915) for the myeloma group. The maximized area under the ROC (AUROC) was calculated, and the results of GBDT (AUC: 0.975; 95% confidence interval (CI): 0.963–0.986) outperformed that of SVM, DNN and RF.ConclusionThe model established by artificial intelligence derived from routine laboratory results can accurately diagnose MM, which can boost the rate of early diagnosis.

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Deep-learning GIS hybrid approach in precipitation modeling based on spatio-temporal variables in the coastal zone of Turkey

Climate Research ◽

10.3354/cr01612 ◽

2020 ◽

Vol 81 ◽

pp. 149-165

Author(s):

H Apaydin ◽

MT Sattari

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Water Resource Management ◽

Short Term Memory ◽

Hybrid Approach ◽

Support Vector ◽

Monthly Precipitation ◽

Artificial Intelligence Methods ◽

Temporal Variables ◽

Spatio Temporal

It is clearly known that precipitation is essential for fauna and flora. Studies have shown that location and temporal factors have an effect on precipitation. Accurate prediction of precipitation is very important for water resource management, and artificial intelligence methods are frequently used to make such predictions. In this study, the deep-learning and geographic information system (GIS) hybrid approach based on spatio-temporal variables was applied in order to model the amount of precipitation on Turkey’s coastline. Information about latitude, longitude, altitude, distance to the sea, and aspect was taken from meteorological stations, and these factors were utilized as spatial variables. The change in monthly precipitation was taken into account as a temporal variable. Artificial intelligence methods such as Gaussian process regression, support vector regression, the Broyden-Fletcher-Goldfarb-Shanno artificial neural network, M5, random forest, and long short-term memory (LSTM) were used. According to the results of the study, in which different input variable alternatives were also evaluated, LSTM was the most successful method for predicting precipitation with a value of 0.93 R. The study shows that the amount of precipitation can be estimated and a distribution map can be drawn by using spatio-temporal data and the deep-learning and GIS hybrid method at points where the measurement is not performed.

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Stock Prediction using Long Short-Term Memory, Support Vector Regression and Linear Regression Algorithms

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37183 ◽

2021 ◽

Vol 9 (VII) ◽

pp. 3632-3638

Author(s):

Mr. V. Manoj Kumar

Keyword(s):

Linear Regression ◽

Support Vector Regression ◽

Short Term Memory ◽

Support Vector ◽

Stock Prediction ◽

Short Term ◽

Data Set ◽

Term Memory ◽

Long Short Term Memory ◽

Memory Support

Prediction is most important for stock market not only for traders but also for computer engineers who analyses stock data. We can perform this prediction by two ways one is using historical stock data and other by analyzing by information gathered from social media. It is based on model/pattern used to predict stock dataset, there are so many models are available for predicting stocks, simply model is algorithm that’s from machine learning and deep learning. In the data set the two main parameters open and close value are used for stock prediction mostly but we can also predict by its volume too. So that data is preprocessed before it is used for prediction. In this paper we used various algorithm like linear regression, support vector regression and long short-term memory for better accuracy and to compare how it different from other algorithm and for predicting future stock.

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One Shot Model For COVID-19 Classification and Lesions Segmentation In Chest CT Scans Using LSTM With Attention Mechanism

10.21203/rs.3.rs-257910/v1 ◽

2021 ◽

Author(s):

Aram Ter-Sarkisov

Keyword(s):

Short Term Memory ◽

Relevant Information ◽

Chest Ct ◽

Attention Mechanism ◽

Ct Scans ◽

Short Term ◽

Term Memory ◽

Memory Network ◽

Long Short Term Memory ◽

Chest Ct Scan

Abstract We present a model that fuses instance segmentation, Long Short-Term Memory Network and Attention mechanism to predict COVID-19 and segment chest CT scans. The model works by extracting a sequence of Regions of Interest that contain class-relevant information, and applies two Long Short-Term Memory networks with attention to this sequence to extract class-relevant features. The model is trained in one shot: both segmentation and classification branches, using two different sets of data. We achieve a 95.74% COVID-19 sensitivity, 98.13% Common Pneumonia sensitivity, 99.27% Control sensitivity and 98.15% class-adjusted F1 score on the main dataset of 21191 chest CT scan slices, and also run a number of ablation studies in which we achieve 97.73% COVID-19 sensitivity and 98.41% F1 score. All source code and models are available on https://github.com/AlexTS1980/COVID-LSTM-Attention.

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Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study

Journal of Medical Internet Research ◽

10.2196/17478 ◽

2020 ◽

Vol 22 (8) ◽

pp. e17478 ◽

Cited By ~ 1

Author(s):

Shyam Visweswaran ◽

Jason B Colditz ◽

Patrick O’Halloran ◽

Na-Rae Han ◽

Sanya B Taneja ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Surveillance System ◽

Short Term Memory ◽

Characteristic Curve ◽

Superior Performance ◽

Support Vector ◽

Data Set ◽

Machine Learning Classifiers ◽

Learning Classifiers

Background Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available unannotated tweets. Objective This study aims to derive and evaluate traditional and deep learning classifiers that can identify tweets relevant to vaping, tweets of a commercial nature, and tweets with provape sentiments. Methods We continuously collected tweets that matched vaping-related keywords over 2 months from August 2018 to October 2018. From this data set of tweets, a set of 4000 tweets was selected, and each tweet was manually annotated for relevance (vape relevant or not), commercial nature (commercial or not), and sentiment (provape or not). Using the annotated data, we derived traditional classifiers that included logistic regression, random forest, linear support vector machine, and multinomial naive Bayes. In addition, using the annotated data set and a larger unannotated data set of tweets, we derived deep learning classifiers that included a convolutional neural network (CNN), long short-term memory (LSTM) network, LSTM-CNN network, and bidirectional LSTM (BiLSTM) network. The unannotated tweet data were used to derive word vectors that deep learning classifiers can leverage to improve performance. Results LSTM-CNN performed the best with the highest area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI 0.93-0.98) for relevance, all deep learning classifiers including LSTM-CNN performed better than the traditional classifiers with an AUC of 0.99 (95% CI 0.98-0.99) for distinguishing commercial from noncommercial tweets, and BiLSTM performed the best with an AUC of 0.83 (95% CI 0.78-0.89) for provape sentiment. Overall, LSTM-CNN performed the best across all 3 classification tasks. Conclusions We derived and evaluated traditional machine learning and deep learning classifiers to identify vaping-related relevant, commercial, and provape tweets. Overall, deep learning classifiers such as LSTM-CNN had superior performance and had the added advantage of requiring no preprocessing. The performance of these classifiers supports the development of a vaping surveillance system.

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Weighted Random Forests to Improve Arrhythmia Classification

Electronics ◽

10.3390/electronics9010099 ◽

2020 ◽

Vol 9 (1) ◽

pp. 99 ◽

Cited By ~ 2

Author(s):

Krzysztof Gajowniczek ◽

Iga Grzegorczyk ◽

Tomasz Ząbkowski ◽

Chandrajit Bajaj

Keyword(s):

Random Forest ◽

Optimal Parameter ◽

Parameter Tuning ◽

Classification Problem ◽

Random Forest Model ◽

Data Set ◽

Forest Model ◽

Intensive Care Patients ◽

Comprehensive Examination ◽

Straightforward Approach

Construction of an ensemble model is a process of combining many diverse base predictive learners. It arises questions of how to weight each model and how to tune the parameters of the weighting process. The most straightforward approach is simply to average the base models. However, numerous studies have shown that a weighted ensemble can provide superior prediction results to a simple average of models. The main goals of this article are to propose a new weighting algorithm applicable for each tree in the Random Forest model and the comprehensive examination of the optimal parameter tuning. Importantly, the approach is motivated by its flexibility, good performance, stability, and resistance to overfitting. The proposed scheme is examined and evaluated on the Physionet/Computing in Cardiology Challenge 2015 data set. It consists of signals (electrocardiograms and pulsatory waveforms) from intensive care patients which triggered an alarm for five cardiac arrhythmia types (Asystole, Bradycardia, Tachycardia, Ventricular Tachycardia, and Ventricular Fultter/Fibrillation). The classification problem regards whether the alarm should or should not have been generated. It was proved that the proposed weighting approach improved classification accuracy for the three most challenging out of the five investigated arrhythmias comparing to the standard Random Forest model.

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COVID-19 Diagnosis from Chest CT Scans: A Weakly Supervised CNN-LSTM Approach

AI ◽

10.3390/ai2030020 ◽

2021 ◽

Vol 2 (3) ◽

pp. 330-341

Author(s):

Mustafa Kara ◽

Zeynep Öztürk ◽

Sergin Akpek ◽

Ayşegül Turupcu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Short Term Memory ◽

High Sensitivity ◽

High Specificity ◽

Chest Ct ◽

Ct Scans ◽

Imaging Data ◽

Weakly Supervised

Advancements in deep learning and availability of medical imaging data have led to the use of CNN-based architectures in disease diagnostic assisted systems. In spite of the abundant use of reverse transcription-polymerase chain reaction-based tests in COVID-19 diagnosis, CT images offer an applicable supplement with their high sensitivity rates. Here, we study the classification of COVID-19 pneumonia and non-COVID-19 pneumonia in chest CT scans using efficient deep learning methods to be readily implemented by any hospital. We report our deep network framework design that encompasses Convolutional Neural Networks and bidirectional Long Short Term Memory architectures. Our study achieved high specificity (COVID-19 pneumonia: 98.3%, non-COVID-19 pneumonia: 96.2% Healthy: 89.3%) and high sensitivity (COVID-19 pneumonia: 84.0%, non-COVID-19 pneumonia: 93.9% Healthy: 94.9%) in classifying COVID-19 pneumonia, non-COVID-19 pneumonia and healthy patients. Next, we provide visual explanations for the Convolutional Neural Network predictions with gradient-weighted class activation mapping (Grad-CAM). The results provided a model explainability by showing that Ground Glass Opacities, indicators of COVID-19 pneumonia disease, were captured by our convolutional neural network. Finally, we have implemented our approach in three hospitals proving its compatibility and efficiency.

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Streamflow forecasting based on the hybrid decomposition-ensemble model

10.21203/rs.3.rs-800297/v1 ◽

2021 ◽

Author(s):

Xiaomei Sun ◽

Haiou Zhang ◽

Jian Wang ◽

Chendi Shi ◽

Dongwen Hua ◽

...

Keyword(s):

Short Term Memory ◽

Gradient Boosting ◽

Support Vector ◽

Ensemble Model ◽

Streamflow Forecasting ◽

Historical Changes ◽

Daily Streamflow ◽

Mode Decomposition ◽

Ensemble Strategy ◽

Hybrid Decomposition

Abstract Reliable and accurate streamflow forecasting plays a vital role in the optimal management of water resources. To improve the stability and accuracy of streamflow forecasting, a hybrid decomposition-ensemble model named VMD-LSTM-GBRT, which is sensitive to sampling, noise and long historical changes of streamflow, was established. The variational mode decomposition (VMD) algorithm was first applied to extract features, which were then learned by several long short-term memory (LSTM) networks. Simultaneously, an ensemble tree, a gradient boosting tree for regression (GBRT), was trained to model the relationships between the extracted features and the original streamflow. The outputs of these LSTMs were finally reconstructed by the GBRT model to obtain the forecasting streamflow results. A historical daily streamflow series (from 1/1/1997 to 31/12/2014) for Yangxian station, Han River, China, was investigated by the proposed model. VMD-LSTM-GBRT was compared with respect to three aspects: (1) Feature extraction algorithm; ensemble empirical mode decomposition (EEMD) was used. (2) Feature learning techniques; deep neural networks (DNNs) and support vector machines for regression (SVRs) were exploited. (3) Ensemble strategy; the summation strategy was used. The results indicate that the VMD-LSTM-GBRT model overwhelms all other peer models in terms of the root mean square error (RMSE=36.3692), determination coefficient (R 2 =0.9890), mean absolute error (MAE=9.5246) and peak percentage threshold statistics (PPTS(5)=0.0391%). The addressed approach based on the memory of long historical changes with deep feature representations had good stability and high prediction precision.

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