scholarly journals Applicability of the Deep Learning Flood Forecast Model Against the Inexperienced Magnitude of Flood

10.29007/fdp5 ◽  
2018 ◽  
Author(s):  
Masayuki Hitokoto ◽  
Masaaki Sakuraba
Keyword(s):  
Deep Learning ◽  
First Grade ◽  
Training Data ◽  
Observation Data ◽  
Validation Data ◽  
River Catchment ◽  
River Stage ◽  
Hourly Rainfall ◽  
Feed Forward Network ◽  
Ann Models

Although artificial neural networks (ANN) is widely used for real-time flood prediction model, it is pointed out that the weak point of the model is poor applicability for the inexperienced magnitude of flood. In this study, the ANN models were applied to first-grade rivers in Japan, Tokoro River catchment and Abashiri River catchment. The training data of the ANN models were all the rainfall-runoff event which exceeded the Flood Watch Water Level during the period of 1998-2015. Types of observation data were river-stage and rainfall at 1-hour pitch. The validation data was the largest flood since the river-stage observation had started. The main component of the model was the four-layer feed-forward network. As a network training method, the deep learning based on the denoising autoencoder was applied. The output of the neural network was change in river-stage in T hours at the prediction point. The input data was the upstream river-stage, hourly change in river-stage and hourly rainfall. The river- stage prediction up to 6 hours showed very good accuracy, and It was proved that it can be nicely predicted even for the past largest flood.

Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms

Heart ◽  
2018 ◽  
Vol 104 (23) ◽  
pp. 1921-1928 ◽  
Author(s):  
Ming-Zher Poh ◽  
Yukkee Cheung Poh ◽  
Pak-Hei Chan ◽  
Chun-Ka Wong ◽  
Louise Pun ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Test Data ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Performance Comparison ◽  
Learning System ◽  
Training Data ◽  
Validation Data ◽  
Data Set

ObjectiveTo evaluate the diagnostic performance of a deep learning system for automated detection of atrial fibrillation (AF) in photoplethysmographic (PPG) pulse waveforms.MethodsWe trained a deep convolutional neural network (DCNN) to detect AF in 17 s PPG waveforms using a training data set of 149 048 PPG waveforms constructed from several publicly available PPG databases. The DCNN was validated using an independent test data set of 3039 smartphone-acquired PPG waveforms from adults at high risk of AF at a general outpatient clinic against ECG tracings reviewed by two cardiologists. Six established AF detectors based on handcrafted features were evaluated on the same test data set for performance comparison.ResultsIn the validation data set (3039 PPG waveforms) consisting of three sequential PPG waveforms from 1013 participants (mean (SD) age, 68.4 (12.2) years; 46.8% men), the prevalence of AF was 2.8%. The area under the receiver operating characteristic curve (AUC) of the DCNN for AF detection was 0.997 (95% CI 0.996 to 0.999) and was significantly higher than all the other AF detectors (AUC range: 0.924–0.985). The sensitivity of the DCNN was 95.2% (95% CI 88.3% to 98.7%), specificity was 99.0% (95% CI 98.6% to 99.3%), positive predictive value (PPV) was 72.7% (95% CI 65.1% to 79.3%) and negative predictive value (NPV) was 99.9% (95% CI 99.7% to 100%) using a single 17 s PPG waveform. Using the three sequential PPG waveforms in combination (<1 min in total), the sensitivity was 100.0% (95% CI 87.7% to 100%), specificity was 99.6% (95% CI 99.0% to 99.9%), PPV was 87.5% (95% CI 72.5% to 94.9%) and NPV was 100% (95% CI 99.4% to 100%).ConclusionsIn this evaluation of PPG waveforms from adults screened for AF in a real-world primary care setting, the DCNN had high sensitivity, specificity, PPV and NPV for detecting AF, outperforming other state-of-the-art methods based on handcrafted features.

scholarly journals APPLICABILITY OF VARIOUS INTERPOLATION APPROACHES FOR HIGH RESOLUTION SPATIAL MAPPING OF CLIMATE DATA IN KOREA

2018 ◽  
Vol XLII-3 ◽  
pp. 703-710 ◽  
Author(s):  
A. Jo ◽  
J. Ryu ◽  
H. Chung ◽  
Y. Choi ◽  
S. Jeon
Keyword(s):  
Interpolation Method ◽  
Training Data ◽  
Spatial Mapping ◽  
Observation Data ◽  
Climate Data ◽  
Weather System ◽  
Validation Data ◽  
Instantaneous Temperature ◽  
Elevation Model ◽  
Total Point

The purpose of this study is to create a new dataset of spatially interpolated monthly climate data for South Korea at high spatial resolution (approximately 30m) by performing various spatio-statistical interpolation and comparing with forecast LDAPS gridded climate data provided from Korea Meterological Administration (KMA). Automatic Weather System (AWS) and Automated Synoptic Observing System (ASOS) data in 2017 obtained from KMA were included for the spatial mapping of temperature and rainfall; instantaneous temperature and 1-hour accumulated precipitation at 09:00 am on 31th March, 21th June, 23th September, and 24th December. Among observation data, 80 percent of the total point (478) and remaining 120 points were used for interpolations and for quantification, respectively. With the training data and digital elevation model (DEM) with 30&amp;thinsp;m resolution, inverse distance weighting (IDW), co-kriging, and kriging were performed by using ArcGIS10.3.1 software and Python 3.6.4. Bias and root mean square were computed to compare prediction performance quantitatively. When statistical analysis was performed for each cluster using 20&amp;thinsp;% validation data, co kriging was more suitable for spatialization of instantaneous temperature than other interpolation method. On the other hand, IDW technique was appropriate for spatialization of precipitation.

scholarly journals Prediction of groundwater drawdown using artificial neural networks

2021 ◽  
Author(s):  
Vahid Gholami ◽  
Hossein Sahour
Keyword(s):  
Neural Network ◽  
Field Experiments ◽  
Accuracy Assessment ◽  
Field Measurements ◽  
Training Data ◽  
Validation Data ◽  
The North ◽  
Groundwater Drawdown ◽  
Ann Models ◽  
Artificial Neural

Abstract Groundwater drawdown is typically measured using pumping tests and field experiments; however, the traditional methods are time-consuming and costly when applied to extensive areas. In this research, a methodology is introduced based on artificial neural network (ANN)s and field measurements in an alluvial aquifer in the north of Iran. First, the annual drawdown as the output of the ANN models in 250 piezometric wells was measured, and the data were divided into three categories of training data, cross-validation data, and test data. Then, the effective factors in groundwater drawdown including groundwater depth, annual precipitation, annual evaporation, the transmissivity of the aquifer formation, elevation, distance from the sea, distance from water sources (recharge), population density, and groundwater extraction in the influence radius of each well (1000 m) were identified and used as the inputs of the ANN models. Several ANN methods were evaluated, and the predictions were compared with the observations. Results show that, the modular neural network (MNN) showed the highest performance in modeling groundwater drawdown ​​(Training R-sqr = 0.96, test R-sqr = 0.81). The optimum network was fitted to available input data to map the annual drawdown ​​across the entire aquifer. The accuracy assessment of the final map yielded favorable results (R-sqr = 0.8). The adopted methodology can be applied for the prediction of groundwater drawdown in the study site and similar settings elsewhere.

Automated detection of diabetic retinopathy using custom convolutional neural network

2022 ◽  
pp. 1-17
Author(s):  
Saleh Albahli ◽  
Ghulam Nabi Ahmad Hassan Yar
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Macular Edema ◽  
Training Data ◽  
Validation Dataset ◽  
Learning Models ◽  
Validation Data ◽  
Study Results ◽  
Image Dataset ◽  
Different Types

Diabetic retinopathy is an eye deficiency that affects retina as a result of the patient having diabetes mellitus caused by high sugar levels, which may eventually lead to macular edema. The objective of this study is to design and compare several deep learning models that detect severity of diabetic retinopathy, determine risk of leading to macular edema, and segment different types of disease patterns using retina images. Indian Diabetic Retinopathy Image Dataset (IDRiD) dataset was used for disease grading and segmentation. Since images of the dataset have different brightness and contrast, we employed three techniques for generating processed images from the original images, which include brightness, color and, contrast (BCC) enhancing, color jitters (CJ), and contrast limited adaptive histogram equalization (CLAHE). After image preporcessing, we used pre-trained ResNet50, VGG16, and VGG19 models on these different preprocessed images both for determining the severity of the retinopathy and also the chances of macular edema. UNet was also applied to segment different types of diseases. To train and test these models, image dataset was divided into training, testing, and validation data at 70%, 20%, and 10% ratios, respectively. During model training, data augmentation method was also applied to increase the number of training images. Study results show that for detecting the severity of retinopathy and macular edema, ResNet50 showed the best accuracy using BCC and original images with an accuracy of 60.2% and 82.5%, respectively, on validation dataset. In segmenting different types of diseases, UNet yielded the highest testing accuracy of 65.22% and 91.09% for microaneurysms and hard exudates using BCC images, 84.83% for optic disc using CJ images, 59.35% and 89.69% for hemorrhages and soft exudates using CLAHE images, respectively. Thus, image preprocessing can play an important role to improve efficacy and performance of deep learning models.

scholarly journals Image classification of different clove (Syzygium aromaticum) quality using deep learning method with convolutional neural network algorithm

2021 ◽  
Vol 905 (1) ◽  
pp. 012018
Author(s):  
I Y Prayogi ◽  
Sandra ◽  
Y Hendrawan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Test Data ◽  
Training Data ◽  
Syzygium Aromaticum ◽  
Learning Method ◽  
Validation Data ◽  
Reading Accuracy

Abstract The objective of this study is to classify the quality of dried clove flowers using deep learning method with Convolutional Neural Network (CNN) algorithm, and also to perform the sensitivity analysis of CNN hyperparameters to obtain best model for clove quality classification process. The quality of clove as raw material in this study was determined according to SNI 3392-1994 by PT. Perkebunan Nusantara XII Pancusari Plantation, Malang, East Java, Indonesia. In total 1,600 images of dried clove flower were divided into 4 qualities. Each clove quality has 225 training data, 75 validation data, and 100 test data. The first step of this study is to build CNN model architecture as first model. The result of that model gives 65.25% reading accuracy. The second step is to analyze CNN sensitivity or CNN hyperparameter on the first model. The best value of CNN hyperparameter in each step then to be used in the next stage. Finally, after CNN hyperparameter carried out the reading accuracy of the test data is improved to 87.75%.

Accurate prediction of somatic variants using deep learning model.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e13659-e13659
Author(s):  
Peng Zhang ◽  
Kai Wang ◽  
Ming Yao ◽  
Aodi Wang ◽  
Lijuan Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sequence Data ◽  
Variant Calling ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Validation Data ◽  
Data Set ◽  
Candidate Mutation ◽  
And Control ◽  
Panel Sequencing

e13659 Background: Efficient and accurate identification of somatic variant is important for understanding the formation, progression, and treatment of cancer. It is necessary to conduct manual review by Integrative Genomic Viewer (IGV) in traditional variant calling process. However, the traditional manual is heavy workload when evaluating tumor with a high variant burden. In this study, a new convolutional neural network (CNN) method was created to train models for somatic mutation identification, which was suitable for Panel sequencing platform with different tumor purities. Methods: A total of 1000 tumor samples from next generation sequencing (NGS)-based genetic testing by a College of American Pathologists (CAP) accredited and Clinical Laboratory Improvement Amendments (CLIA) certified laboratory. Through variant calling program, like GATK, the candidate mutation locations were identified and standardized by manual confirmation. For each candidate mutation location, reads of both tumor and control tissue were extracted. A 2-dimensional feature matrix M of size (2k+1) * 32 in each candidate base was created. The rows of 2k+1 represented the length of candidate region, and the 32 columns included the reads coverage frequency, mapping quality messages, and genome local scores of different tumor and control tissues. CNN model, which includes nine convolutional layers structured by Temporal Convolutional Networks (TCN) but with a different structure to adapt to the proposed input matrix, was used for training. The training data set including manually validated sequence data was used as benchmark test, and optimized by Stochastic Gradient Descent (SGD) optimizer with a learning rate of 0.01 was used for training. Results: The validation data set included 15 mixed samples which were composed of different proportions of known cell lines and real mixed blood samples. The pooled DNA contained 2,359 somatic variants, with expected variant allele frequencies ranged from 3% to 97% in each pool. The overall sensitivity and positive predictive value (PPV) of single nucleotide variants (SNVs) were 99.3% and 99.8%, respectively. Conclusions: A novel and sensitive computational tool for somatic variation detection in DNA Panel sequencing was developed. Our result showed that the deep learning CNN model could call variant in Panel sequencing data.

Optimization-Inspired Deep Learning High-Resolution Inversion for Seismic Data

Geophysics ◽  
2021 ◽  
pp. 1-51
Author(s):  
Hongling Chen ◽  
Jinghuai Gao ◽  
Xiudi Jiang ◽  
Zhaoqi Gao ◽  
Wei Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Resolution ◽  
Field Data ◽  
Critical Role ◽  
Training Data ◽  
Noise Model ◽  
Observation Data ◽  
High Resolution Data ◽  
Training Time

Seismic high-resolution processing plays a critical role in reservoir target detection. As one of the most common approaches, regularization can achieve a high-resolution inversion result. However, the performance of regularization depends on the settings of the associated parameters and constraint functions. Further, it is difficult to solve an objective function with complex constraints, and it requires designing an optimization algorithm. In addition, existing algorithms have high computational complexity, which impedes the inversion of the large data volume. To address these problems, an optimization-inspired deep learning inversion solver is proposed to solve the blind high-resolution inverse (BHRI) problems of various seismic wavelets rapidly, called BHRI-Net. The method builds on ideas from classical regularization theory and recent advances in deep learning, which makes full use of prior information encoded in the forward operator and noise model to learn an accurate mapping relationship. It unrolls the alternating iterative BHRI algorithm into a deep neural network, and it applies the convolutional neural network to learn proximal mappings, in which all parameters of the BHRI algorithm are learned from training data. Further, the proposed network can be split into two parts and incorporate the transfer learning strategy to invert field data, which increases the flexibility of the proposed network and reduces training time. Finally, the tests on synthetic and field data show that the proposed method can effectively invert the high-resolution data and seismic wavelet from observation data with improved accuracy and high computational efficiency.

scholarly journals Brain Tumor Magnetic Resonance Image Classification: A Deep Learning Approach

2021 ◽  
Author(s):  
Jaya Lakshmi Machiraju ◽  
S. Nagaraja Rao
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Loss Function ◽  
Magnetic Resonance Images ◽  
Training Data ◽  
Learning Approach ◽  
Detection Mechanism ◽  
Validation Data ◽  
Proposed Model

Abstract From the past decade, many researchers are focused on the brain tumor detection mechanism using magnetic resonance images. The traditional approaches follow the feature extraction process from bottom layer in the network. This scenario is not suitable to the medical images. To address this issue, the proposed model employed Inception-v3 convolution neural network model which is a deep learning mechanism. This model extracts the multi-level features and classifies them to find the early detection of brain tumor. The proposed model uses the deep learning approach and hyper parameters. These parameters are optimized using the Adam Optimizer and loss function. The loss function helps the machines to model the algorithm with input data. The softmax classifier is used in the proposed model to classify the images in to multiple classes. It is observed that the accuracy of the Inception-v3 algorithm is recorded as 99.34% in training data and 89% accuracy at validation data.

scholarly journals The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines

2020 ◽  
Vol 61 (4) ◽  
pp. 607-616
Author(s):  
Krzysztof Kotlarz ◽  
Magda Mielczarek ◽  
Tomasz Suchocki ◽  
Bartosz Czech ◽  
Bernt Guldbrandtsen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Next Generation Sequencing ◽  
Training Data ◽  
Next Generation ◽  
Validation Data ◽  
Data Set ◽  
Genotype Information ◽  
Naive Algorithm ◽  
Generation Sequencing

Abstract A downside of next-generation sequencing technology is the high technical error rate. We built a tool, which uses array-based genotype information to classify next-generation sequencing–based SNPs into the correct and the incorrect calls. The deep learning algorithms were implemented via Keras. Several algorithms were tested: (i) the basic, naïve algorithm, (ii) the naïve algorithm modified by pre-imposing different weights on incorrect and correct SNP class in calculating the loss metric and (iii)–(v) the naïve algorithm modified by random re-sampling (with replacement) of the incorrect SNPs to match 30%/60%/100% of the number of correct SNPs. The training data set was composed of data from three bulls and consisted of 2,227,995 correct (97.94%) and 46,920 incorrect SNPs, while the validation data set consisted of data from one bull with 749,506 correct (98.05%) and 14,908 incorrect SNPs. The results showed that for a rare event classification problem, like incorrect SNP detection in NGS data, the most parsimonious naïve model and a model with the weighting of SNP classes provided the best results for the classification of the validation data set. Both classified 19% of truly incorrect SNPs as incorrect and 99% of truly correct SNPs as correct and resulted in the F1 score of 0.21 — the highest among the compared algorithms. We conclude the basic models were less adapted to the specificity of a training data set and thus resulted in better classification of the independent, validation data set, than the other tested models.

scholarly journals Deep learning algorithms to segment and quantify the choroidal thickness and vasculature in swept-source optical coherence tomography images

2020 ◽  
pp. 2140002
Author(s):  
Gu Zheng ◽  
Yanfeng Jiang ◽  
Ce Shi ◽  
Hanpei Miao ◽  
Xiangle Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Choroidal Thickness ◽  
Automatic Segmentation ◽  
Learning Algorithms ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Practical Method ◽  
Training Data ◽  
Validation Data ◽  
Swept Source

Accurate segmentation of choroidal thickness (CT) and vasculature is important to better analyze and understand the choroid-related ocular diseases. In this paper, we proposed and implemented a novel and practical method based on the deep learning algorithms, residual U-Net, to segment and quantify the CT and vasculature automatically. With limited training data and validation data, the residual U-Net was capable of identifying the choroidal boundaries as precise as the manual segmentation compared with an experienced operator. Then, the trained deep learning algorithms was applied to 217 images and six choroidal relevant parameters were extracted, we found high intraclass correlation coefficients (ICC) of more than 0.964 between manual and automatic segmentation methods. The automatic method also achieved great reproducibility with ICC greater than 0.913, indicating good consistency of the automatic segmentation method. Our results suggested the deep learning algorithms can accurately and efficiently segment choroid boundaries, which will be helpful to quantify the CT and vasculature.

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