Deep Learning Guided Double Hidden Layer Neural Synchronization Through Mutual Learning

2021 ◽  
Author(s):  
Arindam Sarkar
Keyword(s):  
Deep Learning ◽  
Neural Synchronization ◽  
Mutual Learning ◽  
Hidden Layer

scholarly journals A Novel Design of Classification of Coronary Artery Disease Using Deep Learning and Data Mining Algorithms

2021 ◽  
Vol 35 (3) ◽  
pp. 209-215
Author(s):  
Pratibha Verma ◽  
Vineet Kumar Awasthi ◽  
Sanat Kumar Sahu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Deep Learning ◽  
Regression Tree ◽  
Principal Component ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
R Programming ◽  
Hidden Layer

Data mining techniques are included with Ensemble learning and deep learning for the classification. The methods used for classification are, Single C5.0 Tree (C5.0), Classification and Regression Tree (CART), kernel-based Support Vector Machine (SVM) with linear kernel, ensemble (CART, SVM, C5.0), Neural Network-based Fit single-hidden-layer neural network (NN), Neural Networks with Principal Component Analysis (PCA-NN), deep learning-based H2OBinomialModel-Deeplearning (HBM-DNN) and Enhanced H2OBinomialModel-Deeplearning (EHBM-DNN). In this study, experiments were conducted on pre-processed datasets using R programming and 10-fold cross-validation technique. The findings show that the ensemble model (CART, SVM and C5.0) and EHBM-DNN are more accurate for classification, compared with other methods.

scholarly journals Classification of cardiac arrhythmias using deep learning

2018 ◽  
Vol 7 (3.3) ◽  
pp. 401
Author(s):  
Jeong Hwan Kim ◽  
Jeong Whan Lee ◽  
Kyeong Seop Kim
Keyword(s):  
Deep Learning ◽  
Normal Sinus Rhythm ◽  
Premature Ventricular Contraction ◽  
Heart Rhythm ◽  
Future Research ◽  
Normal Sinus ◽  
Input Layer ◽  
Hidden Layer ◽  
Electrocardiogram Ecg

Background/Objectives: The main objective of this research is to design Deep Learning (DL) architecture to classify an electrocardiogram (ECG) signal into normal sinus rhythm (NSR), premature ventricular contraction (PVC), atrial premature contraction (APC) or right/left bundle branch block (RBBB/LBBB) arrhythmia by empirically optimizing the numbers of hidden layers, the number of neurons in each hidden layer and the number of neurons in input layer in DL model.Methods/Statistical analysis: For our experimental simulations, PhysioBank-MIT/BIH annotated ECG database was considered to classify heart beats into abnormal rhythms (PVC, APC, RBBB, LBBB) or normal sinus. The performance of classifying ECG beats by the proposed DL architecture was evaluated by computing the overall accuracy of classifying NSR or four different arrhythmias.Findings: Base on testing MIT/BIH arrhythmia database, the proposed DL model can classify the heart rhythm into one of NSR, PVC, APC, RBBB or LBBB beat with the mean accuracy of 95.5% by implementing DL architecture with 200 neurons in input layer, 100 neurons in the first and second hidden layer, respectively and 80 neurons in the 3rd hidden layer.Improvements/Applications: Our experimental results show that the proposed DL model might not be quite accurate for detecting APC beats due to its morphological resemblance of NSR. Therefore, we might need to design more sophisticated DL architecture by including more temporal characteristics of APC to increase the classification accuracy of APC arrhythmia in the future research efforts. 

scholarly journals Optimasi Deep Learning untuk Prediksi Saham di Masa Pandemi Covid-19

2021 ◽  
Vol 7 (2) ◽  
pp. 133
Author(s):  
Widi Hastomo ◽  
Adhitio Satyo Bayangkari Karno ◽  
Nawang Kalbuana ◽  
Ervina Nisfiani ◽  
Lussiana ETP
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Hidden Layer ◽  
Gated Recurrent Unit ◽  
Blue Chip

Penelitian ini bertujuan untuk meningkatkan akurasi dengan menurunkan tingkat kesalahan prediksi dari 5 data saham blue chip di Indonesia. Dengan cara mengkombinasikan desain 4 hidden layer neural nework menggunakan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Dari tiap data saham akan dihasilkan grafik rmse-epoch yang dapat menunjukan kombinasi layer dengan akurasi terbaik, sebagai berikut; (a) BBCA dengan layer LSTM-GRU-LSTM-GRU (RMSE=1120,651, e=15), (b) BBRI dengan layer LSTM-GRU-LSTM-GRU (RMSE =110,331, e=25), (c) INDF dengan layer GRU-GRU-GRU-GRU (RMSE =156,297, e=35 ), (d) ASII dengan layer GRU-GRU-GRU-GRU (RMSE =134,551, e=20 ), (e) TLKM dengan layer GRU-LSTM-GRU-LSTM (RMSE =71,658, e=35 ). Tantangan dalam mengolah data Deep Learning (DL) adalah menentukan nilai parameter epoch untuk menghasilkan prediksi akurasi yang tinggi.

Deep learning survival model on transcriptomes level in patients with non-small cell lung cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e20518-e20518
Author(s):  
Hao Yu ◽  
Li Yang ◽  
Ka-On Lam ◽  
Jian-Yue Jin ◽  
Chen Hu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cox Regression ◽  
Metabolic Process ◽  
Survival Model ◽  
Rna Seq ◽  
Small Cell Lung ◽  
Positive Regulation ◽  
Clinical Variables ◽  
Hidden Layer ◽  
Nsclc Patients

e20518 Background: Non-small cell lung cancer (NSCLC) is associated with poor prognosis. Global gene expression profiling with overall survival (OS) may help improving individualize survival. In this study, we identify biological important gene clusters and studied their prognostic abilities for OS by deep learning method. Methods: Using GEO genomics data repository, we identified 196 NSCLC patients (trainset: GSE37745) and 181 NSCLC patients (testset: GSE50081) with clinical information and long-term follow-up. In both cohorts, expression profiling was performed on RNA from tumor tissues using Affymetrix microarrays HG-U133-Plus2; and normalized using the Robust Multiarray Averaging (RMA). We established deep learning survival models through neural network extension of the Cox regression model for predicting OS, which were developed by 5-folds cross-validation in GSE37745 and independently validated in GSE50081. Significant RNA-seq and clinical variables were multiple inputs. Concordance index (CI) was evaluated and compared with multivariable Cox regression. Then we conducted Uniform Manifold Approximation and Projection (UMAP) using weights in hidden layer of the model for clustering the important RNA-seq and then performed enrichment analysis though GO/KEGG for revealing biological progresses. Results: Total 1039 RNA-seq levels were found significant with OS ( P < 0.05) by Cox proportional hazard model adjusted by clinical variables (age, gender, cancer stage, histology) in trainset. The deep learning survival model with 20 most significant RNA-seq and clinical variables had best average performances as CI = 0.74±0.04 in trainset (GSE37745) and CI = 0.68±0.06 in testset (GSE50081) in 10 iterations, better than multivariable Cox regression ( P < 0.05). The deep learning survival model with all significant RNA-seq were also established and the weights in the hidden layer were clustered by UMAP into 5 positive and 5 negative clusters. The clusters were enriched, such as in positive clusters, negative regulation of RNA metabolic process, negative regulation of RNA biosynthetic process and positive regulation of protein modification process were top three significant biological processes for shorten survival; while in negative clusters, DNA metabolic process, positive regulation of phosphate metabolic process and positive regulation of RNA metabolic process were the top three for prolonged survival. Conclusions: In this study, the deep learning survival algorithm was established for survival prediction based on a transcriptome level in patients with NSCLC. Given the models’ robustness and better performances, our study would be useful at predicting and applying more biological information for survival.

scholarly journals Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems

2020 ◽  
Vol 10 (24) ◽  
pp. 8846
Author(s):  
Jaehwan Lee ◽  
Hyeonseong Choi ◽  
Hyeonwoo Jeong ◽  
Baekhyeon Noh ◽  
Ji Sun Shin
Keyword(s):  
Deep Learning ◽  
Selection Method ◽  
Learning System ◽  
Threshold Selection ◽  
L1 Norm ◽  
Optimization Scheme ◽  
Communication Time ◽  
Local Cache ◽  
Hidden Layer ◽  
Data Optimization

In a distributed deep learning system, a parameter server and workers must communicate to exchange gradients and parameters, and the communication cost increases as the number of workers increases. This paper presents a communication data optimization scheme to mitigate the decrease in throughput due to communication performance bottlenecks in distributed deep learning. To optimize communication, we propose two methods. The first is a layer dropping scheme to reduce communication data. The layer dropping scheme we propose compares the representative values of each hidden layer with a threshold value. Furthermore, to guarantee the training accuracy, we store the gradients that are not transmitted to the parameter server in the worker’s local cache. When the value of gradients stored in the worker’s local cache is greater than the threshold, the gradients stored in the worker’s local cache are transmitted to the parameter server. The second is an efficient threshold selection method. Our threshold selection method computes the threshold by replacing the gradients with the L1 norm of each hidden layer. Our data optimization scheme reduces the communication time by about 81% and the total training time by about 70% in a 56 Gbit network environment.

Deep learning to develop transcriptomic model for survival prediction in TCGA patients with hepatocellular carcinoma.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14057-e14057
Author(s):  
Hao Yu ◽  
Wei Dai ◽  
Chi Leung Chiang ◽  
Shisuo Du ◽  
Zhao-Chong Zeng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Creatinine Level ◽  
Clinical Data ◽  
Cox Regression ◽  
P Value ◽  
Survival Prediction ◽  
Signal Pathways ◽  
Test Set ◽  
Hidden Layer

e14057 Background: This study aimed to investigate the prognostic value of transcriptome and clinical data of Hepatocellular carcinoma (HCC) patients for overall survival (OS) by deep learning method. Methods: A total of 371 HCC patients with 20530 level three RNA-sequencing data were from The Cancer Genome Atlas (TCGA). Cox-nnet model, a deep learning model through an artificial neural network extension of the Cox regression model, was used for OS prediction. The patients were randomly split into train-set and test-set (7:3). In train-set, the significant genes associated with OS under univariate Cox regression were considered for modeling. Clinical parameters, including age, gender, pathologic stage, child pugh classification, creatinine level etc. were also considered. The Cox-nnet model was developed by cross-validation. Its discrimination was determined by the concordance index (CI) in the independent test-set and compared with multivariable Cox regression. The clustering method Uniform Manifold Approximation and Projection (UMAP) was used for revealing biological information from the hidden layer in the model. Results: In the train-set (n = 259), 1505 genes and two clinical variables (child pugh score and creatinine level) were significantly associated with OS (adjusted P-value < 0.05). To avoid overfitting, only 40 most significant genes were included in the Cox-nnet model. In the test-set (n = 112), the CI of Cox-nnet (0.76, se = 0.04) is better than the CI of multivariable Cox regression (0.71, se = 0.05). The difference between good or poor survival subgroups classified by Cox-nnet was remarkably significant ( P-value = 1e-4, median OS: 80.7 vs. 25.1 months). In the Cox-nnet model with all significant variables, the weights in the hidden layer were clustered by UMAP into 3 positive clusters and 2 negative clusters, which are enriched in GO/KEGG. The “cell cycle” and “complement and coagulation cascades” are the most important signal pathways in positive and negative clusters, respectively. Conclusions: Combining transcriptomic and clinical data, and with deep learning algorithm, we built and validated a robust model for survival prediction in HCC patients. Our study would be useful to explore the clinical implications in survival prediction and corresponding genetic mechanisms. Clinical trial information: 5U24CA143799, 5U24CA143835, 5U24CA143840, 5U24CA143843, 5U24CA143845, 5U24CA143848, 5U24CA1438.

scholarly journals Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Shan Pang ◽  
Xinyi Yang
Keyword(s):  
Deep Learning ◽  
Extreme Learning Machine ◽  
Neuron Network ◽  
Learning Methods ◽  
Training Time ◽  
Fast Training ◽  
Handwritten Digit ◽  
Learning Machine ◽  
Hidden Layer ◽  
High Level

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

scholarly journals Wi-Fi Fingerprint-Based Indoor Mobile User Localization Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Junhang Bai ◽  
Yongliang Sun ◽  
Weixiao Meng ◽  
Cheng Li
Keyword(s):  
Deep Learning ◽  
Mobile User ◽  
Penalty Term ◽  
Indoor Location ◽  
Feature Representations ◽  
Current Location ◽  
Sparse Autoencoder ◽  
Hidden Layer ◽  
High Level ◽  
Previous Location

In recent years, deep learning has been used for Wi-Fi fingerprint-based localization to achieve a remarkable performance, which is expected to satisfy the increasing requirements of indoor location-based service (LBS). In this paper, we propose a Wi-Fi fingerprint-based indoor mobile user localization method that integrates a stacked improved sparse autoencoder (SISAE) and a recurrent neural network (RNN). We improve the sparse autoencoder by adding an activity penalty term in its loss function to control the neuron outputs in the hidden layer. The encoders of three improved sparse autoencoders are stacked to obtain high-level feature representations of received signal strength (RSS) vectors, and an SISAE is constructed for localization by adding a logistic regression layer as the output layer to the stacked encoders. Meanwhile, using the previous location coordinates computed by the trained SISAE as extra inputs, an RNN is employed to compute more accurate current location coordinates for mobile users. The experimental results demonstrate that the mean error of the proposed SISAE-RNN for mobile user localization can be reduced to 1.60 m.

scholarly journals Deep Learning Convolutional Neural Network for Defect Identification and Classification in Woven Fabric

2021 ◽  
Vol 1 (2) ◽  
pp. 9-13
Author(s):  
Subrata Das ◽  
Sundaramurthy S ◽  
Aiswarya M ◽  
Suresh Jayaram
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Textile Industry ◽  
Woven Fabric ◽  
Defect Identification ◽  
Complete Inspection ◽  
Hidden Layer ◽  
Deep Learning Neural Network

Inspection is the most important role in textile industry which declares the quality of the apparel product. Many Industries were improving their production or quality using Artificial Intelligence. Inspection of fabric in textile industry takes more time and labours. In order to reduce the number of labours and time taken to complete inspection, computerized image processing is done to identify the defects. It gives the accurate result in less time, thereby saves time and increases the production. The convolutional neural network in deep learning is mainly used for image processing for defect detection and classification. The high quality images are given as input, and then the images were used to train the deep learning neural network. Thewoven fabricdefects such as Holes, Selvedge tails, Stains, Wrong drawing and Snarlswere identified by using Convolutional Neural Network. The sample images were collected from the SkyCotex India Pvt.Ltd. The sample images were processed in CNN based machine learning ingoogle platform; the network has a input layer, n number of hidden layer and output layer. The neural network is trained and tested with the samples and the result obtained is used to calculate the efficiency of defect identification.

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