scholarly journals Self-Supervised Denoising Image Filter Based on Recursive Deep Neural Network Structure

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7827
Author(s):  
Changhee Kang ◽  
Sang-ug Kang
Keyword(s):  
Neural Network ◽  
Loss Function ◽  
Deep Neural Network ◽  
Opportunity To Learn ◽  
Noise Removal ◽  
Superior Performance ◽  
Learning Stage ◽  
Proposed Model ◽  
Recursive Learning ◽  
Image Details

The purpose of this paper is to propose a novel noise removal method based on deep neural networks that can remove various types of noise without paired noisy and clean data. Because this type of filter generally has relatively poor performance, the proposed noise-to-blur-estimated clean (N2BeC) model introduces a stage-dependent loss function and a recursive learning stage for improved denoised image quality. The proposed loss function regularizes the existing loss function so that the proposed model can better learn image details. Moreover, the recursive learning stage provides the proposed model with an additional opportunity to learn image details. The overall deep neural network consists of three learning stages and three corresponding loss functions. We determine the essential hyperparameters via several simulations. Consequently, the proposed model showed more than 1 dB superior performance compared with the existing noise-to-blur model.

scholarly journals Optimising Performance for NB-IoT UE Devices through Data Driven Models

2021 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Omar Nassef ◽  
Toktam Mahmoodi ◽  
Foivos Michelinakis ◽  
Kashif Mahmood ◽  
Ahmed Elmokashfi
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Gradient Descent ◽  
Deep Neural Network ◽  
Narrow Band ◽  
Learning Algorithm ◽  
Base Station ◽  
User Equipment ◽  
Data Driven ◽  
Superior Performance

This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation.

First-Break Picking Classification Models Using Recurrent Neural Network

2021 ◽  
Author(s):  
Mohammed Ayub ◽  
SanLinn Kaka
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Contextual Information ◽  
Classification Model ◽  
Superior Performance ◽  
Learning Models ◽  
Neural Network Models ◽  
Minimum Number ◽  
Machine Learning Models

Abstract Manual first-break picking from a large volume of seismic data is extremely tedious and costly. Deployment of machine learning models makes the process fast and cost effective. However, these machine learning models require high representative and effective features for accurate automatic picking. Therefore, First- Break (FB) picking classification model that uses effective minimum number of features and promises performance efficiency is proposed. The variants of Recurrent Neural Networks (RNNs) such as Long ShortTerm Memory (LSTM) and Gated Recurrent Unit (GRU) can retain contextual information from long previous time steps. We deploy this advantage for FB picking as seismic traces are amplitude values of vibration along the time-axis. We use behavioral fluctuation of amplitude as input features for LSTM and GRU. The models are trained on noisy data and tested for generalization on original traces not seen during the training and validation process. In order to analyze the real-time suitability, the performance is benchmarked using accuracy, F1-measure and three other established metrics. We have trained two RNN models and two deep Neural Network models for FB classification using only amplitude values as features. Both LSTM and GRU have the accuracy and F1-measure with a score of 94.20%. With the same features, Convolutional Neural Network (CNN) has an accuracy of 93.58% and F1-score of 93.63%. Again, Deep Neural Network (DNN) model has scores of 92.83% and 92.59% as accuracy and F1-measure, respectively. From the pexperiment results, we see significant superior performance of LSTM and GRU to CNN and DNN when used the same features. For robustness of LSTM and GRU models, the performance is compared with DNN model that is trained using nine features derived from seismic traces and observed that the performance superiority of RNN models. Therefore, it is safe to conclude that RNN models (LSTM and GRU) are capable of classifying the FB events efficiently even by using a minimum number of features that are not computationally expensive. The novelty of our work is the capability of automatic FB classification with the RNN models that incorporate contextual behavioral information without the need for sophisticated feature extraction or engineering techniques that in turn can help in reducing the cost and fostering classification model robust and faster.

Automatic Segmentation and Classification of COVID-19 CT Image Using Deep Learning and Multi-Scale Recurrent Neural Network Based Classifier

2021 ◽  
Vol 11 (10) ◽  
pp. 2618-2625
Author(s):  
R. T. Subhalakshmi ◽  
S. Appavu Alias Balamurugan ◽  
S. Sasikala
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Superior Performance ◽  
Multi Scale ◽  
Proposed Model ◽  
Class Labels

In recent times, the COVID-19 epidemic turn out to be increased in an extreme manner, by the accessibility of an inadequate amount of rapid testing kits. Consequently, it is essential to develop the automated techniques for Covid-19 detection to recognize the existence of disease from the radiological images. The most ordinary symptoms of COVID-19 are sore throat, fever, and dry cough. Symptoms are able to progress to a rigorous type of pneumonia with serious impediment. As medical imaging is not recommended currently in Canada for crucial COVID-19 diagnosis, systems of computer-aided diagnosis might aid in early COVID-19 abnormalities detection and help out to observe the disease progression, reduce mortality rates potentially. In this approach, a deep learning based design for feature extraction and classification is employed for automatic COVID-19 diagnosis from computed tomography (CT) images. The proposed model operates on three main processes based pre-processing, feature extraction, and classification. The proposed design incorporates the fusion of deep features using GoogLe Net models. Finally, Multi-scale Recurrent Neural network (RNN) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the proposed model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity, specificity, and accuracy.

scholarly journals Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1620 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Syafrudin ◽  
Norma Latif Fitriyani ◽  
Muhammad Anshari ◽  
Pavel Stasa ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Risk Factors ◽  
Diabetic Retinopathy ◽  
Prediction Model ◽  
Deep Neural Network ◽  
Early Stage ◽  
Individual Risk ◽  
Proposed Model ◽  
Individual Risk Factors

Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a deep neural network (DNN) combined with recursive feature elimination (RFE) to provide early prediction of diabetic retinopathy (DR) based on individual risk factors. The proposed model uses RFE to remove irrelevant features and DNN to classify the diseases. A publicly available dataset was utilized to predict DR during initial stages, for the proposed and several current best-practice models. The proposed model achieved 82.033% prediction accuracy, which was a significantly better performance than the current models. Thus, important risk factors for retinopathy can be successfully extracted using RFE. In addition, to evaluate the proposed prediction model robustness and generalization, we compared it with other machine learning models and datasets (nephropathy and hypertension–diabetes). The proposed prediction model will help improve early-stage retinopathy diagnosis based on individual risk factors.

A novel deep neural network for noise removal from underwater image

2020 ◽  
Vol 87 ◽  
pp. 115921
Author(s):  
Qin Jiang ◽  
Yang Chen ◽  
Guoyu Wang ◽  
Tingting Ji
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Noise Removal ◽  
Underwater Image

Challenging the Recognition of Facial Expression via Deep Learning

2014 ◽  
Vol 571-572 ◽  
pp. 717-720
Author(s):  
De Kun Hu ◽  
Yong Hong Liu ◽  
Li Zhang ◽  
Gui Duo Duan
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Gradient Descent ◽  
Deep Neural Network ◽  
Back Propagation ◽  
Stochastic Gradient Descent ◽  
Excellent Performance ◽  
Proposed Model ◽  
Input Layer ◽  
Fully Connected

A deep Neural Network model was trained to classify the facial expression in unconstrained images, which comprises nine layers, including input layer, convolutional layer, pooling layer, fully connected layers and output layer. In order to optimize the model, rectified linear units for the nonlinear transformation, weights sharing for reducing the complexity, “mean” and “max” pooling for subsample, “dropout” for sparsity are applied in the forward processing. With large amounts of hard training faces, the model was trained via back propagation method with stochastic gradient descent. The results of shows the proposed model achieves excellent performance.

scholarly journals Polytechnic Students’ Academic Performance Prediction Based On Using Deep Neural Network

2021 ◽  
pp. 1-11
Author(s):  
S. M. Abdullah Al Shuaeb ◽  
Shamsul Alam ◽  
Md. Mizanur Rahman ◽  
Md. Abdul Matin
Keyword(s):  
Neural Network ◽  
Academic Performance ◽  
Performance Prediction ◽  
Deep Neural Network ◽  
Huge Amount ◽  
Novel Technique ◽  
Student Academic Performance ◽  
Academic Activities ◽  
Proposed Model ◽  
Prediction Systems

Students’ academic achievement plays a significant role in the polytechnic institute. It is an important task for the technical student to achieve good results. It becomes more challenging by virtue of the huge amount of data in the polytechnic student databases. Recently, the lack of monitoring of academic activities and their performance has not been harnessed. This is not a good way to evaluate the academic performance of polytechnic students in Bangladesh at present. The study on existing academic prediction systems is still not enough for the polytechnic institutions. Consequently, we have proposed a novel technique to improve student academic performance. In this study, we have used the deep neural network for predicting students' academic final marks. The main objective of this paper is to improve students' results. This paper also explains how the prediction deep neural network model can be used to recognize the most vital attributes in a student's academic data namely midterm_marks, class_ test, attendance, assignment, and target_ marks. By using the proposed model, we can more effectively improve polytechnic student achievement and success.

scholarly journals A Novel CapsNet based Image Reconstruction and Regression Analysis

2020 ◽  
Vol 2 (3) ◽  
pp. 156-164 ◽  
Author(s):  
Dr. Akey Sungheetha ◽  
Dr. Rajesh Sharma R
Keyword(s):  
Neural Network ◽  
Regression Analysis ◽  
Network Models ◽  
Superior Performance ◽  
Neural Network Models ◽  
Innovative Method ◽  
Proposed Model ◽  
Retrieval Efficiency ◽  
High Level ◽  
Better Than

In the field of image processing, all types of computation models are almost evolved to solve the issues through encoded neurons. However, compared with decoding orientation and regression analysis, still the doors are open due to its complexity. At present technologies uses two steps such as, decoding the intermediate terms and reconstruction using decoded information. The performance in terms of regression analysis is lagging due to the decoded intermediate terms. Conventional neural network models perform better in feature classification and representation, though the performance is reduced while handling high level features. Considering these issues in image classification and regression, the proposed model is designed with capsule network as an innovative method which is suitable to handle high level features. The experimental results of the proposed model are compared with conventional neural network models such as BPNN and CNN to validate the superior performance. The proposed model achieves better retrieval efficiency of 95.4% which is much better than other neural network models.

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