Comparisons of Deep Learning Algorithms for MNIST in Real-Time Environment

Palvanov, Akmaljon; Cho, Young Im

doi:10.5391/ijfis.2018.18.2.126

Cited by 31 publications

(11 citation statements)

References 22 publications

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“…Moreover, the CapsNet uses the dynamic routing algorithm to achieve data transmission between the capsule layers (as shown in Fig. 10), which overcomes the shortcomings of the traditional pooling layer 34 . In the dynamic routing algorithm, a non-linear "squashing" function (Eq.…”

Section: Methodsmentioning

confidence: 99%

Microseismic records classification using capsule network with limited training samples in underground mining

Peng

Wang

Jiang

2020

Sci Rep

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The identification of suspicious microseismic events is the first crucial step in microseismic data processing. Existing automatic classification methods are based on the training of a large data set, which is challenging to apply in mines without a long-term manual data processing. In this paper, we present a method to automatically classify microseismic records with limited samples in underground mines based on capsule networks (CapsNet). We divide each microseismic record into 33 frames, then extract 21 commonly used features in time and frequency from each frame. Consequently, a 21 × 33 feature matrix is utilized as the input of CapsNet. On this basis, we use different sizes of training sets to train the classification models separately. The trained model is tested using the same test set containing 3,200 microseismic records and compared to convolutional neural networks (CNN) and traditional machine learning methods. Results show that the accuracy of our proposed method is 99.2% with limited training samples. It is superior to CNN and traditional machine learning methods in terms of Accuracy, Precision, Recall, F1-Measure, and reliability. Underground engineering causes disturbances in the stress state of the rock mass, leading to a large number of microseismic events 1. By post-processing these records (e.g., P-wave arrival picking 2 , event location 3 , and source parameter calculation 4-6), the mechanical state of the corresponding rock mass can be adequately reflected, which is beneficial especially for disaster early warning in underground mining 7-9. However, in the underground mining process, the microseismic monitoring system often receives interference from blasting operations, ore extraction, mechanical operations, high voltage cables, and magnetic fields 10. Therefore, quickly and accurately identifying microseismic records from a large number of suspicious records is a crucial task. Currently, the classification of suspicious microseismic records depends on the visual scanning of waveforms by experienced analysts 11. However, manual classification of microseismic records is a time-consuming, tedious task that is easy to bring into subjective opinions. For these reasons, automatic classification of microseismic records is urgently needed. Throughout the years, many automatic classification methods have been proposed to address the abovementioned problems in seismic and microseismic fields. Scarpetta et al. 12 established a specialized neural discrimination method for low magnitude seismic events, quarry blasts, underwater explosions, and thunder sources at Mt. Vesuvius Volcano, Italy. Langer 13 , Esposito 14 and Curilem 15 used the machine learning to classify seismic records at the Soufriere Hills volcano (Montserrat), Stromboli island (southern Italy) and the Villarrica volcano (Chile), respectively. Malovichko 16 utilized a set of seismic characteristics and the multivariate maximum-likelihood Gaussian classifier, to quantify a probability that a particular event belongs to a population of blas...

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Section: Methodsmentioning

confidence: 99%

Microseismic records classification using capsule network with limited training samples in underground mining

Peng

Wang

Jiang

2020

Sci Rep

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“…Ref. [21] examined the performance of logistic regression models in real-time to demonstrate their effectiveness. Regarding IoT networks, ref.…”

Section: Related Workmentioning

confidence: 99%

Multi-Party Privacy-Preserving Logistic Regression with Poor Quality Data Filtering for IoT Contributors

Edemacu

Kim

2021

Electronics

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Nowadays, the internet of things (IoT) is used to generate data in several application domains. A logistic regression, which is a standard machine learning algorithm with a wide application range, is built on such data. Nevertheless, building a powerful and effective logistic regression model requires large amounts of data. Thus, collaboration between multiple IoT participants has often been the go-to approach. However, privacy concerns and poor data quality are two challenges that threaten the success of such a setting. Several studies have proposed different methods to address the privacy concern but to the best of our knowledge, little attention has been paid towards addressing the poor data quality problems in the multi-party logistic regression model. Thus, in this study, we propose a multi-party privacy-preserving logistic regression framework with poor quality data filtering for IoT data contributors to address both problems. Specifically, we propose a new metric gradient similarity in a distributed setting that we employ to filter out parameters from data contributors with poor quality data. To solve the privacy challenge, we employ homomorphic encryption. Theoretical analysis and experimental evaluations using real-world datasets demonstrate that our proposed framework is privacy-preserving and robust against poor quality data.

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“…Meier et al [31], used an ensemble of 25 neural networks and Ciresan et al [11] used a 6-layers neural network. Palvanov et al [37] showed how Deep Residual Networks recognises digits much faster than the traditional CNN due to its inherent architecture consisting of shortcut connections.…”

Section: Handwritten Digit Recognitionmentioning

confidence: 99%

Handwritten Digit Recognition Using Bayesian ResNet

et al. 2021

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The problem of handwritten digit recognition has seen various developments in the recent times, especially in neural network domain. The methods based on neural network work quite effectively for the seen classes of data by providing deterministic results. However, these methods tend to behave in similar fashion even for unseen class of data. For example, a neural network trained on English language digits will give a deterministic prediction even when tested on digits of other languages. Hence, it is required to predict uncertainty for such methods in this scenario. In this paper, we employ Bayesian inference into the existing ResNet18 framework to bring out uncertainty for handwritten digit recognition when there is a new class of test digit. We term the new architecture as B-ResNet. The novel B-ResNet is first of its kind to be investigated for the handwritten digit recognition. Various experiments on datasets of English, Devanagari, Gujarati, Bengali digits and their all possible combinations demonstrate the efficiency and performance of the B-ResNet for hand written digit recognition.

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Comparisons of Deep Learning Algorithms for MNIST in Real-Time Environment

Cited by 31 publications

References 22 publications

Microseismic records classification using capsule network with limited training samples in underground mining

Microseismic records classification using capsule network with limited training samples in underground mining

Multi-Party Privacy-Preserving Logistic Regression with Poor Quality Data Filtering for IoT Contributors

Handwritten Digit Recognition Using Bayesian ResNet

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