Pattern recognition of Seismic Activity in Indonesia through Deep Learning

2020 ◽  
Author(s):  
Nishtha Srivastava ◽  
Kai Zhou ◽  
Jan Steinheimer ◽  
Johannes Faber ◽  
Horst Stoecker
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Seismic Activity ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Stress Release ◽  
Stress History ◽  
Deep Learning Algorithm ◽  
History Of ◽  
Seismic Stress

<p class="western" align="justify"><span>Earthquakes have disastrously impacted communities by destructing the buildings and infrastructure and creating substantial setbacks in the socio-economic development of a region in addition to the huge human loss. They are inevitable and considered extremely difficult to predict. Earthquake prediction research is being carried out for more than 100 years with no well acknowledged model achieved till date. However, the analysis of past seismic stress history of an active fault may help in understanding the stress build up and the local breaking points of the faults. Yet, analysing and interpreting the abundant seismological dataset is most time consuming and is a herculean task.</span></p> <p class="western" align="justify"><span>The possibilities to solve big data, complex problems with Deep Learning are undeniable, however, it’s usage in Seismology is still in its early stage. The implementation of Deep Learning algorithms has the potential to decipher the complex patterns and hidden information in past stress history that is nearly impossible for scientists. The careful implementation of various Deep Learning algorithms in the exponentially growing seismic data can significantly improve the Early Warning System. In the present study, we train a time efficient machine/deep learning algorithm to self-learn and decode the intricate stress accumulation and release pattern, to estimate the probability of local breakdowns of the fault.</span></p> <p class="western" align="justify"><a name="_GoBack"></a> <span>The study region for the present research is Indonesia, which under the influence of the Eurasian, Indo-Australian, Philippine and Pacific plates, immensely suffers due to high seismic activity. The principal contributor in the seismicity of the region is Java-Sunda Trench which lies in the Pacific Ring of Fire (PROF). Owing to the high frequency of earthquakes striking every year from different epicentres, the region provides a huge database. The earthquakes triggering in the region from 1970-2018 is downloaded from the International Seismological Centre website (http://www.isc.ac.uk). These earthquake data comprised of ~270,000 events with the information of Latitude, Longitude, Time of the event and focal depth. To respect the bias which is unavoidable due to the change of the quality of the sensors and the data over the decades, the data is divided into subsets. We considered both small and large magnitude earthquakes along the subduction line to generate a localized time series of stress release to understand the seismic history of the region. By using different neural network models such as one dimensional Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), an optimized Deep Learning algorithm is trained to understand the intricate pattern associated with the seismic stress release in region. This specialized model is expected to empower seismologist by providing a time saving, automated process for the identification of the zone of failures.</span></p>

Malaria Detection System Using Convolutional Neural Network Algorithm

2020 ◽  
pp. 219-230
Author(s):  
Kanika Gautam ◽  
Sunil Kumar Jangir ◽  
Manish Kumar ◽  
Jay Sharma
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Detection System ◽  
Learning Algorithms ◽  
Anopheles Mosquito ◽  
Deep Learning Algorithm ◽  
Blood Smears ◽  
Neural Network Algorithm ◽  
Modern Technologies

Malaria is a disease caused when a female Anopheles mosquito bites. There are over 200 million cases recorded per year with more than 400,000 deaths. Current methods of diagnosis are effective; however, they work on technologies that do not produce higher accuracy results. Henceforth, to improve the prediction rate of the disease, modern technologies need to be performed for obtain accurate results. Deep learning algorithms are developed to detect, learn, and determine the containing parasites from the red blood smears. This chapter shows the implementation of a deep learning algorithm to identify the malaria parasites with higher accuracy.

scholarly journals Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 652 ◽  
Author(s):  
Carlo Augusto Mallio ◽  
Andrea Napolitano ◽  
Gennaro Castiello ◽  
Francesco Maria Giordano ◽  
Pasquale D'Alessio ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Learning Algorithm ◽  
Checkpoint Inhibitor ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Algorithm

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. Methods: We enrolled three groups: a pneumonia-free group (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann–Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). Results: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.

Digital object restoration using generalized regression neural network deep learning—Taking Dunhuang mural restoration as an example

2020 ◽  
pp. 002072092092854
Author(s):  
Wenjing She
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Restoration ◽  
Anisotropic Diffusion ◽  
Learning Algorithm ◽  
Diffusion Method ◽  
Generalized Regression Neural Network ◽  
Deep Learning Algorithm ◽  
Training Samples ◽  
Restoration Effect

In this research, Dunhuang murals is taken as the object of restoration, and the role of digital repair combined with deep learning algorithm in mural restoration is explored. First, the image restoration technology is described, as well as its advantages and disadvantages are analyzed. Second, the deep learning algorithm based on artificial neural network is described and analyzed. Finally, the deep learning algorithm is integrated into the digital repair technology, and a mural restoration method based on the generalized regression neural network is proposed. The morphological expansion method and anisotropic diffusion method are used to preprocess the image. The MATLAB software is used for the simulation analysis and evaluation of the image restoration effect. The results show that in the restoration of the original image, the accuracy of the digital image restoration technology is not high. The nontexture restoration technology is not applicable in the repair of large-scale texture areas. The predicted value of the mural restoration effect based on the generalized neural network is closer to the true value. The anisotropic diffusion method has a significant effect on the processing of image noise. In the image similarity rate, the different number of training samples and smoothing parameters are compared and analyzed. It is found that when the value of δ is small, the number of training samples should be increased to improve the accuracy of the prediction value. If the number of training samples is small, a larger value of δ is needed to get a better prediction effect, and the best restoration effect is obtained for the restored image. Through this study, it is found that this study has a good effect on the restoration model of Dunhuang murals. It provides experimental reference for the restoration of later murals.

scholarly journals A64 Viral sequence classification using deep learning algorithms

2019 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
David Nieuwenhuijse ◽  
Bas Oude Munnink ◽  
My Phan ◽  
Marion Koopmans
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
High Sensitivity ◽  
Current Approach ◽  
Metagenomic Sequencing ◽  
Public Health Importance ◽  
Short Reads ◽  
Deep Learning Algorithm ◽  
High Threat

Abstract Sewage samples have a high potential benefit for surveillance of circulating pathogens because they are easy to obtain and reflect population-wide circulation of pathogens. These type of samples typically contain a great diversity of viruses. Therefore, one of the main challenges of metagenomic sequencing of sewage for surveillance is sequence annotation and interpretation. Especially for high-threat viruses, false positive signals can trigger unnecessary alerts, but true positives should not be missed. Annotation thus requires high sensitivity and specificity. To better interpret annotated reads for high-threat viruses, we attempt to determine how classifiable they are in a background of reads of closely related low-threat viruses. As an example, we attempted to distinguish poliovirus reads, a virus of high public health importance, from other enterovirus reads. A sequence-based deep learning algorithm was used to classify reads as either polio or non-polio enterovirus. Short reads were generated from 500 polio and 2,000 non-polio enterovirus genomes as a training set. By training the algorithm on this dataset we try to determine, on a single read level, which short reads can reliably be labeled as poliovirus and which cannot. After training the deep learning algorithm on the generated reads we were able to calculate the probability with which a read can be assigned to a poliovirus genome or a non-poliovirus genome. We show that the algorithm succeeds in classifying the reads with high accuracy. The probability of assigning the read to the correct class was related to the location in the genome to which the read mapped, which conformed with our expectations since some regions of the genome are more conserved than others. Classifying short reads of high-threat viral pathogens seems to be a promising application of sequence-based deep learning algorithms. Also, recent developments in software and hardware have facilitated the development and training of deep learning algorithms. Further plans of this work are to characterize the hard-to-classify regions of the poliovirus genome, build larger training databases, and expand on the current approach to other viruses.

scholarly journals Essentials of a Robust Deep Learning System for Diabetic Retinopathy Screening: A Systematic Literature Review

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Aan Chu ◽  
David Squirrell ◽  
Andelka M. Phillips ◽  
Ehsan Vaghefi
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Validation Dataset ◽  
Cochrane Library ◽  
Clinical Implementation ◽  
Diabetic Retinopathy Screening ◽  
Deep Learning Algorithm ◽  
Retinopathy Screening

This systematic review was performed to identify the specifics of an optimal diabetic retinopathy deep learning algorithm, by identifying the best exemplar research studies of the field, whilst highlighting potential barriers to clinical implementation of such an algorithm. Searching five electronic databases (Embase, MEDLINE, Scopus, PubMed, and the Cochrane Library) returned 747 unique records on 20 December 2019. Predetermined inclusion and exclusion criteria were applied to the search results, resulting in 15 highest-quality publications. A manual search through the reference lists of relevant review articles found from the database search was conducted, yielding no additional records. A validation dataset of the trained deep learning algorithms was used for creating a set of optimal properties for an ideal diabetic retinopathy classification algorithm. Potential limitations to the clinical implementation of such systems were identified as lack of generalizability, limited screening scope, and data sovereignty issues. It is concluded that deep learning algorithms in the context of diabetic retinopathy screening have reported impressive results. Despite this, the potential sources of limitations in such systems must be evaluated carefully. An ideal deep learning algorithm should be clinic-, clinician-, and camera-agnostic; complying with the local regulation for data sovereignty, storage, privacy, and reporting; whilst requiring minimum human input.

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