scholarly journals Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 517 ◽  
Author(s):  
Ali M. Hasan ◽  
Mohammed M. AL-Jawad ◽  
Hamid A. Jalab ◽  
Hadil Shaiba ◽  
Rabha W. Ibrahim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Accurate Method ◽  
Short Term ◽  
Lung Scan ◽  
Deep Learning Features ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Ct Slices

Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists’ efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.

scholarly journals Including Image-Based Perception in Disturbance Observer for Warehouse Drones

2020 ◽  
Author(s):  
Zhu Chen ◽  
Xiao Liang ◽  
Minghui Zheng
Keyword(s):  
Deep Learning ◽  
Disturbance Observer ◽  
Feedback Loop ◽  
Short Term Memory ◽  
External Disturbance ◽  
Short Term ◽  
Object A ◽  
Numerical Studies ◽  
Long Short Term Memory ◽  
Lstm Network

Abstract Grasping and releasing objects would cause oscillations to delivery drones in the warehouse. To reduce such undesired oscillations, this paper treats the to-be-delivered object as an unknown external disturbance and presents an image-based disturbance observer (DOB) to estimate and reject such disturbance. Different from the existing DOB technique that can only compensate for the disturbance after the oscillations happen, the proposed image-based one incorporates image-based disturbance prediction into the control loop to further improve the performance of the DOB. The proposed image-based DOB consists of two parts. The first one is deep-learning-based disturbance prediction. By taking an image of the to-be-delivered object, a sequential disturbance signal is predicted in advance using a connected pre-trained convolutional neural network (CNN) and a long short-term memory (LSTM) network. The second part is a conventional DOB in the feedback loop with a feedforward correction, which utilizes the deep learning prediction to generate a learning signal. Numerical studies are performed to validate the proposed image-based DOB regarding oscillation reduction for delivery drones during the grasping and releasing periods of the objects.

scholarly journals LSTM Network and OCR Performance for Classification of Decimal Dewey Classification Code

2020 ◽  
Vol 6 (1) ◽  
pp. 45
Author(s):  
Yesy Diah Rosita ◽  
Yanuarini Nur Sukmaningtyas
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Short Term Memory ◽  
Experimental Result ◽  
Short Term ◽  
Classification Code ◽  
Accuracy Difference ◽  
Long Short Term Memory ◽  
Lstm Network

Background of the study: Giving book code by a librarian in accordance with the Decimal Dewey Classification system aims to facilitate the search for books on the shelf precisely and quickly. Purpose: The first step in giving code to determine the class of books is the principal division which has 10 classes.Method: This study proposed Optical Character Recognition to read the title text on the book cover, preprocessing the text, and classifying it by Long Short-Term Memory Neural Network. Findings: In general, a librarian labeled a book by reading the book title on the book cover and doing book class matching with the book guide of DDC. Automatically, the task requires time increasingly. We tried to classify the text without OCR and utilize OCR which functions to convert the text in images into text that is editable. BY the experimental result, the level of classification accuracy without utilizing OCR is higher than using OCR. Conclusion: The magnitude of the accuracy is 88.57% and 74.28% respectively. However, the participation of OCR in this classification is quite efficient enough to assist a beginner librarian to overcome this problem because the accuracy difference is less than 15%.

scholarly journals Deepfake tweets classification using stacked Bi-LSTM and words embedding

2021 ◽  
Vol 7 ◽  
pp. e745
Author(s):  
Vaibhav Rupapara ◽  
Furqan Rustam ◽  
Aashir Amaar ◽  
Patrick Bernard Washington ◽  
Ernesto Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Learning Models ◽  
Short Term ◽  
Term Memory ◽  
Tree Classifier ◽  
Long Short Term Memory ◽  
Digital Manipulation ◽  
Lstm Network

The spread of altered media in the form of fake videos, audios, and images, has been largely increased over the past few years. Advanced digital manipulation tools and techniques make it easier to generate fake content and post it on social media. In addition, tweets with deep fake content make their way to social platforms. The polarity of such tweets is significant to determine the sentiment of people about deep fakes. This paper presents a deep learning model to predict the polarity of deep fake tweets. For this purpose, a stacked bi-directional long short-term memory (SBi-LSTM) network is proposed to classify the sentiment of deep fake tweets. Several well-known machine learning classifiers are investigated as well such as support vector machine, logistic regression, Gaussian Naive Bayes, extra tree classifier, and AdaBoost classifier. These classifiers are utilized with term frequency-inverse document frequency and a bag of words feature extraction approaches. Besides, the performance of deep learning models is analyzed including long short-term memory network, gated recurrent unit, bi-direction LSTM, and convolutional neural network+LSTM. Experimental results indicate that the proposed SBi-LSTM outperforms both machine and deep learning models and achieves an accuracy of 0.92.

scholarly journals Prediction of Sorption Processes Using the Deep Learning Methods (Long Short-Term Memory)

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6601
Author(s):  
Dorian Skrobek ◽  
Jaroslaw Krzywanski ◽  
Marcin Sosnowski ◽  
Anna Kulakowska ◽  
Anna Zylka ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Experimental Tests ◽  
Relative Pressure ◽  
Short Term ◽  
Term Memory ◽  
Numerical Research ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Good Agreement

The paper introduces the artificial intelligence (AI) approach for modeling fluidized adsorption beds. The idea of fluidized bed application allows a significantly increased heat transfer coefficient between adsorption bed and the surface of a heat exchanger, improving the performance of adsorption cooling and desalination systems. The Long Short-Term Memory (LSTM) network algorithm was used, classified as a deep learning method, to predict the vapor mass quantity in the adsorption bed. The research used an LSTM network with two hidden layers. The network used in the study is composed of seven inputs (absolute pressures in the adsorption chamber and evaporator, the temperatures in adsorption chamber and evaporator, relative pressure, the temperatures in the center of adsorption bed and 25 mm from the bed center, the kind of the solids mixture, the percentage value of the addition) and one output (mass of the sorption bed). The paper presents numerical research concerning mass prediction with the algorithm mentioned above for three sorbents in fixed ad fluidized beds. The results obtained by the developed algorithm of the LSTM network and the experimental tests are in good agreement of the matching the results above 0.95.

scholarly journals Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 924
Author(s):  
Moslem Imani ◽  
Hoda Fakour ◽  
Wen-Hau Lan ◽  
Huan-Chin Kao ◽  
Chi Ming Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Short Term Memory ◽  
Learning Model ◽  
Short Term ◽  
Long Short Term Memory ◽  
Deep Learning Model

Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.

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