scholarly journals Research on Mount Wilson Magnetic Classification Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yuanbo He ◽  
Yunfei Yang ◽  
Xianyong Bai ◽  
Song Feng ◽  
Bo Liang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Test Results ◽  
Object Locations ◽  
Key Points ◽  
Different Types ◽  
Three Stages ◽  
Formula Type ◽  
The Continuum ◽  
Sunspot Groups

The Mount Wilson magnetic classification of sunspot groups is thought to be meaningful to forecast flares’ eruptions. In this paper, we adopt a deep learning method, CornerNet-Saccade, to perform the Mount Wilson magnetic classification of sunspot groups. It includes three stages, generating object locations, detecting objects, and merging detections. The key technologies consist of the backbone as Hourglass-54, the attention mechanism, and the key points’ mechanism including the top-left corners and the bottom-right corners of the object by corner pooling layers. These technologies improve the efficiency of detecting the objects without sacrificing accuracy. A dataset is built by a total of 2486 composited images which are composited with the continuum images and the corresponding magnetograms from HMI and MDI. After training the network, the sunspot groups in a composited solar full image are detected and classified in 3 seconds on average. The test results show that this method has a good performance, with the accuracy, precision, recall, and mAP as 0.94, 0.93, 0.94, and 0.90, respectively. Moreover, the flare productivities of different types of sunspot groups from 2011 to 2020 are calculated. As I tot   ≥  1, the flare productivities of α , β , β γ , β δ , and β γ δ sunspot groups are 0.14, 0.28, 0.61, 0.71, and 0.87, respectively. As I tot   ≥  10, the flare productivities are 0.02, 0.07, 0.27, 0.45, and 0.65, respectively. It means that the β γ , β δ , and β γ δ types are indeed very closely related to the eruption of solar flares, especially the β γ δ type. Based on the reliability of this method, the sunspot groups of the HMI solar full images from 2011 to 2020 are detected and classified, and the detailed data are shared on the website (https://61.166.157.71/MWMCSG.html).

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

A novel two-stage method of plant seedlings classification based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Tianhong Dai ◽  
Shijie Cong ◽  
Jianping Huang ◽  
Yanwen Zhang ◽  
Xinwang Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Two Stage ◽  
Second Stage ◽  
Stage Classification ◽  
Different Types ◽  
Two Stages ◽  
Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

Fans—Their Types, Characteristics, and Application

1942 ◽  
Vol 148 (1) ◽  
pp. 1-25
Author(s):  
C. G. Ferguson
Keyword(s):  
Output Power ◽  
Axial Flow ◽  
Test Results ◽  
Dust Extraction ◽  
Extraction Plant ◽  
Different Types ◽  
Centrifugal Fans ◽  
Fan Speed

A fan is defined by the British Standards Institution as “a machine which propels air continuously, the total fan head never exceeding 1 lb. per sq. in.” When the pressure is above this limit the field of blowers is entered. The paper deals descriptively with modern fans and their applications under the following three main types: ( a) propeller fans, ( b) centrifugal fans, ( c) axial flow fans. The differences in their characteristics and construction appear to justify the classification of ( a) and ( c) as different types. After recapitulation of the fundamental formulae used in assessing the output, power requirements, and efficiencies of centrifugal fans, the author discusses various test results on actual installations, with some of which he was personally concerned. Axial-flow fans are considered both from manufacturing and from aerodynamical viewpoints, and test results are given, together with curves showing the power absorbed and the efficiency achieved. In the latter part of the paper the author deals with various applications of fans: to the ventilation of buildings, ships, and mines; as a method of supplying draught to boiler installations by mechanical means; and in the operation of dust extraction plant. The control of fan speed and output by hydraulic couplings is compared with methods of adjusting the inlet vanes, in order to achieve the same result.

Arrhythmia Diagnosis through ECG Signal Classification Using A Hybrid CNN-LSTM Model: Comparison with current Deep Learning Approaches (Preprint)

2020 ◽  
Author(s):  
Nicos Maglaveras ◽  
Georgios Petmezas ◽  
Vassilis Kilintzis ◽  
Leandros Stefanopoulos ◽  
Andreas Tzavelis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Model Comparison ◽  
Temporal Dynamics ◽  
Experimental Studies ◽  
Signal Classification ◽  
Ecg Signal ◽  
Learning Approaches ◽  
Different Types

BACKGROUND Electrocardiogram (ECG) recording and interpretation is the most common method used for the diagnosis of cardiac arrhythmias, nonetheless this process requires significant expertise and effort from the doctors’ perspective. Automated ECG signal classification could be a useful technique for the accurate detection and classification of several types of arrhythmias within a short timeframe. OBJECTIVE To review current approaches using state-of-the-art CNNs and deep learning methodologies in arrhythmia detection via ECG feature classification techniques and propose an optimised architecture capable of different types of arrhythmia diagnosis using publicly existing annotated arrhythmia databases from the MIT-BIH databases available at PHYSIONET (physionet.org) . METHODS A hybrid CNN-LSTM deep learning model is proposed to classify beats derived from two large ECG databases. The approach is proposed after a systematic review of current AI/DL methods applied in different types of arrhythmia diagnosis using the same public MIT-BIH databases. In the proposed architecture the CNN part carries out feature extraction and dimensionality reduction, and the LSTM part performs classification of the encoded ECG beat signals. RESULTS In experimental studies conducted with the MIT-BIH Arrhythmia and the MIT-BIH Atrial Fibrillation Databases average accuracies of 96.82% and 96.65% were noted respectively. CONCLUSIONS The proposed system can be used for arrhythmia diagnosis in clinical and mHealth applications managing a number of prevalent arrhythmias such as VT, AFIB, LBBB etc. The capability of CNNs to reduce the ECG beat signal’s size and extract its main features can be effectively combined with the LSTMs’ capability to learn the temporal dynamics of the input data for the accurate and automatic recognition of several types of cardiac arrhythmias. CLINICALTRIAL Not applicable.

Utilizing different types of deep learning models for classification of series arc in photovoltaics systems

2021 ◽  
Vol 96 ◽  
pp. 107478
Author(s):  
Alaa Hamza Omran ◽  
Dalila Mat Said ◽  
Siti Maherah Hussin ◽  
Sadiq H. Abdulhussain ◽  
Haider Samet
Keyword(s):  
Deep Learning ◽  
Learning Models ◽  
Different Types

scholarly journals Deep Learning Application for Effective Classification of Different Types of Psoriasis

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Syeda Fatima Aijaz ◽  
Saad Jawaid Khan ◽  
Fahad Azim ◽  
Choudhary Sobhan Shakeel ◽  
Umer Hassan
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Skin Diseases ◽  
Normal Skin ◽  
Learning Algorithms ◽  
Learning Techniques ◽  
Different Types ◽  
Input Sample

Psoriasis is a chronic inflammatory skin disorder mediated by the immune response that affects a large number of people. According to latest worldwide statistics, 125 million individuals are suffering from psoriasis. Deep learning techniques have demonstrated success in the prediction of skin diseases and can also lead to the classification of different types of psoriasis. Hence, we propose a deep learning-based application for effective classification of five types of psoriasis namely, plaque, guttate, inverse, pustular, and erythrodermic as well as the prediction of normal skin. We used 172 images of normal skin from the BFL NTU dataset and 301 images of psoriasis from the Dermnet dataset. The input sample images underwent image preprocessing including data augmentation, enhancement, and segmentation which was followed by color, texture, and shape feature extraction. Two deep learning algorithms of convolutional neural network (CNN) and long short-term memory (LSTM) were applied with the classification models being trained with 80% of the images. The reported accuracies of CNN and LSTM are 84.2% and 72.3%, respectively. A paired sample T-test exhibited significant differences between the accuracies generated by the two deep learning algorithms with a p < 0.001 . The accuracies reported from this study demonstrate potential of this deep learning application to be applied to other areas of dermatology for better prediction.

scholarly journals Development of machine learning model for diagnostic disease prediction based on laboratory tests

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dong Jin Park ◽  
Min Woo Park ◽  
Homin Lee ◽  
Young-Jin Kim ◽  
Yeongsic Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Laboratory Tests ◽  
Gradient Boosting ◽  
Disease Prediction ◽  
Ensemble Model ◽  
Test Results ◽  
Laboratory Test Results ◽  
Classification Of Diseases

AbstractThe use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by blending a DNN (deep neural network) model with two ML models for disease prediction using laboratory test results. 86 attributes (laboratory tests) were selected from datasets based on value counts, clinical importance-related features, and missing values. We collected sample datasets on 5145 cases, including 326,686 laboratory test results. We investigated a total of 39 specific diseases based on the International Classification of Diseases, 10th revision (ICD-10) codes. These datasets were used to construct light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) ML models and a DNN model using TensorFlow. The optimized ensemble model achieved an F1-score of 81% and prediction accuracy of 92% for the five most common diseases. The deep learning and ML models showed differences in predictive power and disease classification patterns. We used a confusion matrix and analyzed feature importance using the SHAP value method. Our new ML model achieved high efficiency of disease prediction through classification of diseases. This study will be useful in the prediction and diagnosis of diseases.

scholarly journals ChartMaster: An End-to-End Method to Recognize, Redraw, and Redesign Chart

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Lingmei Zhang ◽  
Guangxia Wang ◽  
Lingyu Chen
Keyword(s):  
Easy Task ◽  
Geometric Methods ◽  
Key Points ◽  
Different Types ◽  
Original Table ◽  
End To End

Chart is one kind of ubiquitous information, which is widely utilized and easy for people to understand. Due to there are so many different kinds and different styles of charts, it is not an easy task for a computer to recognize a chart, as well as to redraw the chart or redesign it. This study proposes a three-stage method to chart recognition: analyze the classification of charts, analyze the structure of charts, and analyze the content of charts. When classifying charts, we choose ResNet-50. When recognizing the structure and content of charts, we use different deep frameworks to extract key points based on different types of charts. Besides, we also introduce two datasets, UCCD and UCID, to train deep models to classify and recognize charts. Finally, we utilize some traditional geometric methods to obtain an original table of a chart, so we can redraw it.

scholarly journals Cyber-Physical System for Environmental Monitoring Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3655
Author(s):  
Íñigo Monedero ◽  
Julio Barbancho ◽  
Rafael Márquez ◽  
Juan F. Beltrán
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Research Area ◽  
Cyber Physical System ◽  
Dynamic Configuration ◽  
Sound System ◽  
Physical Systems ◽  
Different Types ◽  
The Internet Of Things

Cyber-physical systems (CPS) constitute a promising paradigm that could fit various applications. Monitoring based on the Internet of Things (IoT) has become a research area with new challenges in which to extract valuable information. This paper proposes a deep learning classification sound system for execution over CPS. This system is based on convolutional neural networks (CNNs) and is focused on the different types of vocalization of two species of anurans. CNNs, in conjunction with the use of mel-spectrograms for sounds, are shown to be an adequate tool for the classification of environmental sounds. The classification results obtained are excellent (97.53% overall accuracy) and can be considered a very promising use of the system for classifying other biological acoustic targets as well as analyzing biodiversity indices in the natural environment. The paper concludes by observing that the execution of this type of CNN, involving low-cost and reduced computing resources, are feasible for monitoring extensive natural areas. The use of CPS enables flexible and dynamic configuration and deployment of new CNN updates over remote IoT nodes.

Dual stain kit for the microscopic gram classification of ocular infection bacteria

1993 ◽  
Vol 51 ◽  
pp. 248-249
Author(s):  
Jacob S. Hanker ◽  
Dale N. Holdren ◽  
Kenneth L. Cohen ◽  
Beverly L. Giammara
Keyword(s):  
Contact Lenses ◽  
Ocular Infection ◽  
Gram Negative Bacteria ◽  
Gram Negative ◽  
Gram Staining ◽  
Ocular Infections ◽  
Different Types ◽  
Culture Positive ◽  
Increased Susceptibility

Keratitis and conjunctivitis (infections of the cornea or conjunctiva) are ocular infections caused by various bacteria, fungi, viruses or parasites; bacteria, however, are usually prominent. Systemic conditions such as alcoholism, diabetes, debilitating disease, AIDS and immunosuppressive therapy can lead to increased susceptibility but trauma and contact lens use are very important factors. Gram-negative bacteria are most frequently cultured in these situations and Pseudomonas aeruginosa is most usually isolated from culture-positive ulcers of patients using contact lenses. Smears for staining can be obtained with a special swab or spatula and Gram staining frequently guides choice of a therapeutic rinse prior to the report of the culture results upon which specific antibiotic therapy is based. In some cases staining of the direct smear may be diagnostic in situations where the culture will not grow. In these cases different types of stains occasionally assist in guiding therapy.

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