Deep Neural Network-Based Scale Feature Model for BVI Detection and Principal Component Extraction

The increasing in the incidence of stroke with aging world population would quickly place an economic burden on society. In proposed method we use different machine learning classification algorithms like Decision Tree, Deep Neural Network Learning, Maximum Expectization , Random Forest and Gaussian Naïve Bayesian Classifier is used with associated number of attributes to estimate the occurrence of stroke disease. The present research, mainly PCA (Principal Component Analysis) algorithm is used to limit the performance and scaling used to be adopted to extract splendid context statistics from medical records. We used those reduced features to determine whether or not the patient has a stroke disorder. We compared proposed method Deep neural network learning classifier with other machine-learning methods with respect to accuracy, sensitivity and specificity that yields 86.42%, 74.89 and 88.49% respectively. Hence it can be with the aid of both patients and medical doctors to treat viable stroke.

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Principal Component Analysis on Mixed Data For Deep Neural Network Classifier in Banking System

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i5.129134 ◽

2019 ◽

Vol 7 (5) ◽

pp. 129-134

Author(s):

Chittem Leela Krishna ◽

Poli Venkata Subba Reddy

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Deep Neural Network ◽

Banking System ◽

Principal Component ◽

Component Analysis ◽

Mixed Data ◽

Neural Network Classifier

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FPGA design of efficient kidney image classification using algebric histogram feature model and sparse deep neural network (SDNN) techniques

2017 Conference on Emerging Devices and Smart Systems (ICEDSS) ◽

10.1109/icedss.2017.8073687 ◽

2017 ◽

Cited By ~ 2

Author(s):

R. Vinoth ◽

K. Bommannaraja

Keyword(s):

Neural Network ◽

Image Classification ◽

Deep Neural Network ◽

Feature Model ◽

Fpga Design ◽

Histogram Feature

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CARDIAC ADVANCED ARRYTHMIA PREDICTION SYSTEMS- CAAPS

10.36106/ijsr/9204719 ◽

2021 ◽

pp. 54-55

Author(s):

Pradeep Kumar Radhakrishnan ◽

Gayathri Ananyajyothi Ambat ◽

Saihrudya Samhita ◽

Murugan U S ◽

Tarig Ali ◽

...

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Support Vector Machine ◽

Ventricular Arrhythmias ◽

Deep Neural Network ◽

Principal Component ◽

Support Vector ◽

Discrete Wavelet ◽

Term Memory

There is a constant search for novel methods of classication and predicting cardiac rhythm disorders or arrhythmias. We prefer to classify them as wide complex tachyarrhythmia's or ventricular arrhythmias inclusive of malignant ventricular arrhythmias which with hemodynamic compromise is usually life threatening. Long term and fatality predictions warranting AICD implantation are already available. We have a novel method and robust algorithm with preprocessing and optimal feature selection from ECG signal analysis for such rhythm disorders. Variability of ECG recording makes predictability analysis challenging especially when execution time is of prime importance in tackling resuscitative attempts for MVA. Noisy data needs ltering and preprocessing for effective analysis. Portable devices need more of this ltering prior to data input. Deterministic probabilistic nite state automata (DPFA) which generates a probability strings from the broad morphologic patterns of an ECG can generate a classier data for the algorithm without preprocessing for atrial high rate episodes (AHRE). DPFA can be effectively used for atrial tachyarrhythmias for predictive analysis. The method we suggest is use of optimal classier set for prediction of malignant ventricular arrhythmias and use of DFPA for atrial arrhythmias. Here traditional practices of heart rate variability based support vector machine (SVM), discrete wavelet transform (DWT), principal component analysis (PCA), deep neural network (DNN), convoutional neural network (CNN) or CNN with long term memory (LSTM) can be outperformed. AICD - automatic implantable cardiac debrillator, MVA - Malignant Ventricular Arrhythmias, VT - ventricular tachycardia, VF - ventricular brillation,DFPA deterministic probabilistic nite state automata, SVM -Support Vector Machine, DWT discrete wavelet transform, PCA principal component analysis, DNN deep neural network, CNN convoutional neural network, Convoutional LSTM Long short term memory,RNN recurrent neural network

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The Use of Deep Learning to Predict Stroke Patient Mortality

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16111876 ◽

2019 ◽

Vol 16 (11) ◽

pp. 1876 ◽

Cited By ~ 7

Author(s):

Songhee Cheon ◽

Jungyoon Kim ◽

Jihye Lim

Keyword(s):

Neural Network ◽

Health Behavior ◽

Deep Neural Network ◽

Service Use ◽

Medical Service ◽

Area Under The Curve ◽

Principal Component ◽

Warning Signs ◽

Imaging Data ◽

Stroke Incidence

The increase in stroke incidence with the aging of the Korean population will rapidly impose an economic burden on society. Timely treatment can improve stroke prognosis. Awareness of stroke warning signs and appropriate actions in the event of a stroke improve outcomes. Medical service use and health behavior data are easier to collect than medical imaging data. Here, we used a deep neural network to detect stroke using medical service use and health behavior data; we identified 15,099 patients with stroke. Principal component analysis (PCA) featuring quantile scaling was used to extract relevant background features from medical records; we used these to predict stroke. We compared our method (a scaled PCA/deep neural network [DNN] approach) to five other machine-learning methods. The area under the curve (AUC) value of our method was 83.48%; hence; it can be used by both patients and doctors to prescreen for possible stroke.

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Neural network classification of laser-induced 5-ALA-PpIX fluorescence spectra using adaptive principal component extraction

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference ◽

10.1109/iembs.2005.1615494 ◽

2005 ◽

Author(s):

Dailin Xia ◽

Jishan He ◽

Yangde Zhang

Keyword(s):

Neural Network ◽

Fluorescence Spectra ◽

Principal Component ◽

Neural Network Classification ◽

Component Extraction

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Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior

eLife ◽

10.7554/elife.32962 ◽

2018 ◽

Vol 7 ◽

Cited By ~ 52

Author(s):

Iris IA Groen ◽

Michelle R Greene ◽

Christopher Baldassano ◽

Li Fei-Fei ◽

Diane M Beck ◽

...

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

A Priori ◽

Feature Model ◽

Semantic Features ◽

Functional Feature ◽

Scene Representation ◽

Behavioral Assessments ◽

Brain And Behavior ◽

High Level

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.

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Employing Robust Principal Component Analysis for Noise-Robust Speech Feature Extraction in Automatic Speech Recognition with the Structure of a Deep Neural Network

Applied System Innovation ◽

10.3390/asi1030028 ◽

2018 ◽

Vol 1 (3) ◽

pp. 28 ◽

Cited By ~ 4

Author(s):

Jeih-weih Hung ◽

Jung-Shan Lin ◽

Po-Jen Wu

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Speech Recognition ◽

Automatic Speech Recognition ◽

Deep Neural Network ◽

Principal Component ◽

Component Analysis ◽

Robust Principal Component Analysis ◽

Speech Feature ◽

Noise Robust

In recent decades, researchers have been focused on developing noise-robust methods in order to compensate for noise effects in automatic speech recognition (ASR) systems and enhance their performance. In this paper, we propose a feature-based noise-robust method that employs a novel data analysis technique—robust principal component analysis (RPCA). In the proposed scenario, RPCA is employed to process a noise-corrupted speech feature matrix, and the obtained sparse partition is shown to reveal speech-dominant characteristics. One apparent advantage of using RPCA for enhancing noise robustness is that no prior knowledge about the noise is required. The proposed RPCA-based method is evaluated with the Aurora-4 database and a task using a state-of-the-art deep neural network (DNN) architecture as the acoustic models. The evaluation results indicate that the newly proposed method can provide the original speech feature with significant recognition accuracy improvement, and can be cascaded with mean normalization (MN), mean and variance normalization (MVN), and relative spectral (RASTA)—three well-known and widely used feature robustness algorithms—to achieve better performance compared with the individual component method.

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Internal Crack Prediction of Continuous Casting Billet Based on Principal Component Analysis and Deep Neural Network

Metals ◽

10.3390/met11121976 ◽

2021 ◽

Vol 11 (12) ◽

pp. 1976

Author(s):

Leilei Zou ◽

Jiangshan Zhang ◽

Yanshen Han ◽

Fanzheng Zeng ◽

Quanhui Li ◽

...

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Continuous Casting ◽

Deep Neural Network ◽

Principal Component ◽

Component Analysis ◽

Learning Ability ◽

Internal Crack ◽

Strong Nonlinearity ◽

Neural Network Models

The accurate prediction of internal cracks in steel billets is of great importance for the stable production of continuous casting. However, it is challenging, owing to the strong nonlinearity, and coupling among continuous casting process parameters. In this study, an internal crack prediction model based on the principal component analysis (PCA) and deep neural network (DNN) was proposed by collecting sufficient industrial data. PCA was used to reduce the dimensionality of the factors influencing the internal cracks, and the obtained principal components were used as DNN input variables. The 5-fold cross-validation results demonstrate that the prediction accuracy of the DNN model is 92.2%, which is higher than those of the decision tree (DT), extreme learning machine (ELM), and backpropagation (BP) neural network models. Moreover, the variance analysis showed that the prediction results of the DNN model were more stable. The PCA-DNN model can provide a useful reference for real production, owing to its strong learning ability and fault-tolerant ability.

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