Image De-Noising Using Deep Learning

2014 ◽  
Vol 641-642 ◽  
pp. 1287-1290
Author(s):  
Lan Zhang ◽  
Yu Feng Nie ◽  
Zhen Hai Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Input Data ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Large Data ◽  
Image Database ◽  
Learning Problems ◽  
Deep Learning Algorithm

Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.

Conservation of Mangroves by Trait-Based Using Deep Learning Algorithm

2020 ◽  
Vol 17 (8) ◽  
pp. 3328-3332
Author(s):  
S. Gowri ◽  
U. Srija ◽  
P. A. Shirley Divya ◽  
J. Jabez ◽  
J. S. Vimali
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Soil Erosion ◽  
Endangered Species ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
Mangrove Species ◽  
Deep Learning Algorithm ◽  
Wind Turbulence

Classifying and predicting the Mangrove species is one of the most important applications in our ecosystem. Mangroves are the most endangered species that contributes in playing a greater role in our ecosystem. It mainly prevents the calamities like soil erosion, Tsunami, storms, wind turbulence, etc. These Mangroves has to be afforested and conserved in order to maintain a healthy ecosystem. To attain this the study of mangrove is to be done first. To classify the mangroves in its habitat, we use an algorithm from Deep Neural Network.

scholarly journals Deep learning the holographic black hole with charge

2019 ◽  
Vol 28 (12) ◽  
pp. 1950153 ◽  
Author(s):  
Jing Tan ◽  
Chong-Bin Chen
Keyword(s):  
Neural Network ◽  
Black Hole ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
Equation Of Motion ◽  
Batch Size ◽  
Training Process ◽  
Data Set ◽  
Deep Learning Algorithm

We use the deep learning algorithm to learn the Reissner–Nordström (RN) black hole metric by building a deep neural network. Plenty of data are determined in boundary of AdS and we propagate them to the black hole horizon through AdS metric and equation of motion (e.o.m). We label these data according to the values near the horizon, and together with initial data they constitute a data set. Then we construct corresponding deep neural network and train it with the data set to obtain the Reissner–Nordström (RN) black hole metric. Finally, we discuss the effects of learning rate, batch-size and initialization on the training process.

Deep Learning Algorithm for Image Classification of Waveforms Obtained From Electrically Stimulated Hypoxic Skeletal Muscle Bundles

2020 ◽  
Author(s):  
Weston Upchurch ◽  
Alex Deakyne ◽  
David A. Ramirez ◽  
Paul A. Iaizzo
Keyword(s):  
Neural Network ◽  
Skeletal Muscle ◽  
Deep Learning ◽  
Emergency Treatment ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
Hypoxic Conditions ◽  
Deep Learning Algorithm ◽  
Compartmental Pressure

Abstract Acute compartment syndrome is a serious condition that requires urgent surgical treatment. While the current emergency treatment is straightforward — relieve intra-compartmental pressure via fasciotomy — the diagnosis is often a difficult one. A deep neural network is presented here that has been trained to detect whether isolated muscle bundles were exposed to hypoxic conditions and became ischemic.

Abstract 238: A Deep Neural Network Approach for Continuous Electrocardiogram-based Shock Advisory System During Cardio Pulmonary Resuscitation

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_4) ◽  
Author(s):  
Shirin Hajeb Mohammadalipour ◽  
Alicia Cascella ◽  
Matt Valentine ◽  
K.H. Chon
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
Motion Artifacts ◽  
Learning Approaches ◽  
Neural Network Approach ◽  
Advisory System ◽  
Deep Learning Algorithm ◽  
Cardio Pulmonary Resuscitation

The ability of an automatic external defibrillator (AED) to make a reliable shock decision during cardio pulmonary resuscitation (CPR) would improve the survival rate of patients with out-of-hospital cardiac arrest. Since chest compressions induce motion artifacts in the electrocardiogram (ECG), current AEDs instruct the user to stop CPR while an automated rhythm analysis is performed. It has been shown that minimizing interruptions in CPR increases the chance of survival. While deep learning approaches have been used successfully for arrhythmia classification, their performance has not been evaluated for creating an AED shock advisory system that can coexist with CPR. To this end, the objective of this study was to apply a deep-learning algorithm using convolutional layers and residual networks to classify shockable versus non-shockable rhythms in the presence and absence of CPR artifact using only the ECG data. The feasibility of the deep learning method was validated using 8-sec segments of ECG with and without CPR. Two separate databases were used: 1) 40 subjects’ data without CPR from Physionet with 1131 shockable and 2741 non-shockable classified recordings, and 2) CPR artifacts that were acquired from a commercial AED during asystole delivered by 43 different resuscitators. For each 8-second ECG segment, randomly chosen CPR data from 43 different types were added to it so that 5 non-shockable and 10 shockable CPR-contaminated ECG segments were created. We used 30 subjects’ and the remaining 10 for training and test datasets, respectively, for the database 1). For the database 2), we used 33 and 10 subjects’ data for training and testing, respectively. Using our deep neural network model, the sensitivity and specificity of the shock versus no-shock decision for both datasets using the four-fold cross-validation were found to be 95.21% and 86.03%, respectively. For shockable versus non-shockable classification of ECG without CPR artifact, the sensitivity was 99.04% and the specificity was 95.2%. A sensitivity of 94.21% and a specificity of 86.14% were obtained for ECG with CPR artifact. These results meet the AHA sensitivity requirement (>90%).

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.

scholarly journals Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment

2021 ◽  
Vol 11 (15) ◽  
pp. 7148
Author(s):  
Bedada Endale ◽  
Abera Tullu ◽  
Hayoung Shi ◽  
Beom-Soo Kang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Input Data ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Object Classification ◽  
Training Data ◽  
Gradient Descent Algorithm

Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.

scholarly journals An Efficient Method for Detection of DDoS Attacks on the Web Using Deep Learning Algorithms

2021 ◽  
Vol 10 (4) ◽  
pp. 2821-2829
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Ddos Attacks ◽  
Problem Statement ◽  
Neural Network Approach ◽  
Learning Techniques ◽  
Attack Data ◽  
Deep Learning Neural Network

Recently, DDoS attacks is the most significant threat in network security. Both industry and academia are currently debating how to detect and protect against DDoS attacks. Many studies are provided to detect these types of attacks. Deep learning techniques are the most suitable and efficient algorithm for categorizing normal and attack data. Hence, a deep neural network approach is proposed in this study to mitigate DDoS attacks effectively. We used a deep learning neural network to identify and classify traffic as benign or one of four different DDoS attacks. We will concentrate on four different DDoS types: Slowloris, Slowhttptest, DDoS Hulk, and GoldenEye. The rest of the paper is organized as follow: Firstly, we introduce the work, Section 2 defines the related works, Section 3 presents the problem statement, Section 4 describes the proposed methodology, Section 5 illustrate the results of the proposed methodology and shows how the proposed methodology outperforms state-of-the-art work and finally Section VI concludes the paper.

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