scholarly journals A Novel Method of Billing System Using Deep Learning

2021 ◽  
Vol 1084 (1) ◽  
pp. 012035
Author(s):  
J Haritha ◽  
K Prakash ◽  
B Navina ◽  
S Saveetha
Keyword(s):  
Deep Learning ◽  
Billing System ◽  
Novel Method

Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

2021 ◽  
pp. 136943322098663
Author(s):  
Diana Andrushia A ◽  
Anand N ◽  
Eva Lubloy ◽  
Prince Arulraj G
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Detection Method ◽  
Concrete Structures ◽  
Vital Role ◽  
Subjective Knowledge ◽  
Thermal Cracks ◽  
Reinforced Cement ◽  
Novel Method ◽  
Human Inspection

Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

A novel method based on machine vision system and deep learning to detect fraud in turmeric powder

2021 ◽  
pp. 104728
Author(s):  
Ahmad Jahanbakhshi ◽  
Yousef Abbaspour-Gilandeh ◽  
Kobra Heidarbeigi ◽  
Mohammad Momeny
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Vision System ◽  
Machine Vision System ◽  
Novel Method

scholarly journals Technical Note: Quantifying music-dance synchrony with the application of a deep learning-based 2D pose estimator

2020 ◽  
Author(s):  
Filip Potempski ◽  
Andrea Sabo ◽  
Kara K Patterson
Keyword(s):  
Deep Learning ◽  
Motor Performance ◽  
Data Extraction ◽  
Effective Means ◽  
Technical Note ◽  
Novel Method ◽  
Synchronous Condition ◽  
The Mean ◽  
Repeated Training

AbstractDance interventions are more effective at improving gait and balance outcomes than other rehabilitation interventions. Repeated training may culminate in superior motor performance compared to other interventions without synchronization. This technical note will describe a novel method using a deep learning-based 2D pose estimator: OpenPose, alongside beat analysis of music to quantify movement-music synchrony during salsa dancing. This method has four components: i) camera setup and recording, ii) tempo/downbeat analysis and waveform cleanup, iii) OpenPose estimation and data extraction, and iv) synchronization analysis. Two trials were recorded: one in which the dancer danced synchronously to the music and one where they did not. The salsa dancer performed a solo basic salsa step continuously for 90 seconds to a salsa track while their movements and the music were recorded with a webcam. This data was then extracted from OpenPose and analyzed. The mean synchronization value for both feet was significantly lower in the synchronous condition than the asynchronous condition, indicating that this is an effective means to track and quantify a dancer’s movement and synchrony while performing a basic salsa step.

scholarly journals A Deep Learning Model for Molecular Label Transfer that Enables Cancer Cell Identification from Histopathology Images

2021 ◽  
Author(s):  
Andrew Su ◽  
HoJoon Lee ◽  
Xiao Tan ◽  
Carlos J Suarez ◽  
Noemi Andor ◽  
...  
Keyword(s):  
Deep Learning ◽  
Initial Step ◽  
Classification Systems ◽  
Interactive Tools ◽  
Histopathologic Evaluation ◽  
Histopathological Images ◽  
Novel Method ◽  
Histopathology Images ◽  
Transfer Method ◽  
Learning Software

Deep learning cancer classification systems have the potential to improve cancer diagnosis. However, development of these computational approaches depends on prior annotation through a pathologist. This initial step relying on a manual, low-resolution, time-consuming process is highly variable and subject to observer variance. To address this issue, we developed a novel method, H&E Molecular neural network (HEMnet). This two-step process utilises immunohistochemistry as an initial molecular label for cancer cells on a H&E image and then we train a cancer classifier on the overlapping clinical histopathological images. Using this molecular transfer method, we show that HEMnet accurately distinguishes colorectal cancer from normal tissue at high resolution without the need for an initial manual histopathologic evaluation. Our validation study using histopathology images from TCGA samples accurately estimates tumour purity. Overall, our method provides a path towards a fully automated delineation of any type of tumor so long as there is a cancer-oriented molecular stain available for subsequent learning. Software, tutorials and interactive tools are available at: https://github.com/BiomedicalMachineLearning/HEMnet

Boosting Automated Sleep Staging Performance in Big Datasets using Population Sub-grouping

SLEEP ◽  
2021 ◽  
Author(s):  
Samaneh Nasiri ◽  
Gari D Clifford
Keyword(s):  
Deep Learning ◽  
Feature Space ◽  
Learning Approaches ◽  
Sleep Staging ◽  
Learning Framework ◽  
Sensitivity Score ◽  
Instrument Noise ◽  
Novel Method ◽  
Clustering Approach ◽  
Statistical Relationships

Abstract Current approaches to automated sleep staging from the electroencephalogram (EEG) rely on constructing a large labeled training and test corpora by aggregating data from different individuals. However, many of the subjects in the training set may exhibit changes in the EEG that are very different from the subjects in the test set. Training an algorithm on such data without accounting for this diversity can cause underperformance. Moreover, test data may have unexpected sensor misplacement or different instrument noise and spectral responses. This work proposes a novel method to learn relevant individuals based on their similarities effectively. The proposed method embeds all training patients into a shared and robust feature space. Individuals that share strong statistical relationships and are similar based on their EEG signals are clustered in this feature space before being passed to a deep learning framework for classification. Using 994 patient EEGs from the 2018 Physionet Challenge (≈ 6,561 hours of recording), we demonstrate that the clustering approach significantly boosts performance compared to state-of-the-art deep learning approaches. The proposed method improves, on average, a precision score from 0.72 to 0.81, a sensitivity score from 0.74 to 0.82, and a Cohen’s Kappa coefficient from 0.64 to 0.75 under 10-fold cross-validation.

scholarly journals ConvoSource: Radio-Astronomical Source-Finding with Convolutional Neural Networks

Galaxies ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 3
Author(s):  
Vesna Lukic ◽  
Francesco de Gasperin ◽  
Marcus Brüggen
Keyword(s):  
Deep Learning ◽  
Galactic Nuclei ◽  
Correlated Noise ◽  
Artificial Data ◽  
Star Forming ◽  
Gaussian Fitting ◽  
Fitting Method ◽  
Novel Method ◽  
Square Kilometer Array ◽  
Total Integration

Finding and classifying astronomical sources is key in the scientific exploitation of radio surveys. Source-finding usually involves identifying the parts of an image belonging to an astronomical source, against some estimated background. This can be problematic in the radio regime, owing to the presence of correlated noise, which can interfere with the signal from the source. In the current work, we present ConvoSource, a novel method based on a deep learning technique, to identify the positions of radio sources, and compare the results to a Gaussian-fitting method. Since the deep learning approach allows the generation of more training images, it should perform well in the source-finding task. We test the source-finding methods on artificial data created for the data challenge of the Square Kilometer Array (SKA). We investigate sources that are divided into three classes: star forming galaxies (SFGs) and two classes of active galactic nuclei (AGN). The artificial data are given at two different frequencies (560 MHz and 1400 MHz), three total integration times (8 h, 100 h, 1000 h), and three signal-to-noise ratios (SNRs) of 1, 2, and 5. At lower SNRs, ConvoSource tends to outperform a Gaussian-fitting approach in the recovery of SFGs and all sources, although at the lowest SNR of one, the better performance is likely due to chance matches. The Gaussian-fitting method performs better in the recovery of the AGN-type sources at lower SNRs. At a higher SNR, ConvoSource performs better on average in the recovery of AGN sources, whereas the Gaussian-fitting method performs better in the recovery of SFGs and all sources. ConvoSource usually performs better at shorter total integration times and detects more true positives and misses fewer sources compared to the Gaussian-fitting method; however, it detects more false positives.

scholarly journals A deep learning view of the census of galaxy clusters in IllustrisTNG

2020 ◽  
Vol 498 (4) ◽  
pp. 5620-5628
Author(s):  
Y Su ◽  
Y Zhang ◽  
G Liang ◽  
J A ZuHone ◽  
D J Barnes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Galaxy Clusters ◽  
Large Scale ◽  
Learning Algorithm ◽  
X Ray ◽  
Deep Learning Algorithm ◽  
Cluster Evolution ◽  
Novel Method ◽  
Massive Clusters ◽  
Activation Mapping

ABSTRACT The origin of the diverse population of galaxy clusters remains an unexplained aspect of large-scale structure formation and cluster evolution. We present a novel method of using X-ray images to identify cool core (CC), weak cool core (WCC), and non-cool core (NCC) clusters of galaxies that are defined by their central cooling times. We employ a convolutional neural network, ResNet-18, which is commonly used for image analysis, to classify clusters. We produce mock Chandra X-ray observations for a sample of 318 massive clusters drawn from the IllustrisTNG simulations. The network is trained and tested with low-resolution mock Chandra images covering a central 1 Mpc square for the clusters in our sample. Without any spectral information, the deep learning algorithm is able to identify CC, WCC, and NCC clusters, achieving balanced accuracies (BAcc) of 92 per cent, 81 per cent, and 83 per cent, respectively. The performance is superior to classification by conventional methods using central gas densities, with an average ${\rm BAcc}=81{{\ \rm per\ cent}}$, or surface brightness concentrations, giving ${\rm BAcc}=73{{\ \rm per\ cent}}$. We use class activation mapping to localize discriminative regions for the classification decision. From this analysis, we observe that the network has utilized regions from cluster centres out to r ≈ 300 kpc and r ≈ 500 kpc to identify CC and NCC clusters, respectively. It may have recognized features in the intracluster medium that are associated with AGN feedback and disruptive major mergers.

scholarly journals Predictive Analysis of Cryptocurrency Price Using Deep Learning

2018 ◽  
Vol 7 (3.27) ◽  
pp. 258 ◽  
Author(s):  
Yecheng Yao ◽  
Jungho Yi ◽  
Shengjun Zhai ◽  
Yuwen Lin ◽  
Taekseung Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
International Relations ◽  
Short Term Memory ◽  
Training Data ◽  
Short Term ◽  
Effective Learning ◽  
Learning Techniques ◽  
Benchmark Datasets ◽  
Novel Method ◽  
Long Short Term Memory

The decentralization of cryptocurrencies has greatly reduced the level of central control over them, impacting international relations and trade. Further, wide fluctuations in cryptocurrency price indicate an urgent need for an accurate way to forecast this price. This paper proposes a novel method to predict cryptocurrency price by considering various factors such as market cap, volume, circulating supply, and maximum supply based on deep learning techniques such as the recurrent neural network (RNN) and the long short-term memory (LSTM),which are effective learning models for training data, with the LSTM being better at recognizing longer-term associations. The proposed approach is implemented in Python and validated for benchmark datasets. The results verify the applicability of the proposed approach for the accurate prediction of cryptocurrency price.

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