neural network model diagnosis
A co-operative hybrid algorithm for fault diagnosis in power transmission
... The algorithm is a hierarchical model which combines several reasoning methods such as heuristic, temporal and model-based diagnosis and incorporates a network of neural net[r] ...
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<p>Artificial Neural Network Model for Liver Cirrhosis Diagnosis in Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma</p>
... ANN model make it maintain satisfactory reliability and stability in diagnosing LC ...ANN model has a strong applic- ability in processing complex biological data, but other existing statistical techniques ...
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DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network
... convolutional neural network (CNN) framework aimed at FHR ...CNN model by means of self-learning in- formative features from the input ...to diagnosis coronary artery disease using an ...
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Application of artificial neural network model in diagnosis of Alzheimer’s disease
... early diagnosis and prediction models for AD, but they may have some ...in diagnosis of AD were better than the model in this ...their model mostly include invasive clinical examinations or ...
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Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application
... Duplex Neural- Lumped Thermal Network (DNLTN) has been developed to predict temperature as well as fault in relation to temperature ...thermal model with machine learning has been presented in the ...
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Application of Genetic Neural Network in Fault Diagnosis
... fault diagnosis model adopted in this paper is BP neural network optimized by genetic ...fault diagnosis, the structure of the neural network is determined according to ...
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Neural Network based Fault Diagnosis in Analog Electronic Circuit using Polynomial Curve Fitting
... After the circuit simulation, we have different frequency responses graphs indicating the different parametric variation faults in each of the components present in the circuit. The collected graphs are applied to the ...
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The research of how to identify the computer network fault by improving neural network
... new diagnosis system structure model. This model employs fuzzy neural network in order to get the diagnosis matrix from the previous statistical diagnosis examples, by ...
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Fault Diagnosis of Power Network Based on GIS Platform and Bayesian Networks
... power network quickly and give troubleshooting solutions, this paper obtains a simplify structure of relay protection and circuit-breaker as key equipment by analyzing the power network topology of GIS ...
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Convolutional Neural Network in Medical Diagnosis
... VGGNet is a CNN developed by Karen Simonyan and Andrew Zisserman [13]. It was the runner-up in ILSVRC 2014 challenge. It achieved a top-5 error rate of 7.3%. The final VGGNet consists of 16 Convolutional/Fully Connected ...
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ADAPTIVE COLOR FILTER ARRAY INTERPOLATION ALGORITHM BASED ON HUE TRANSITION AND EDGE DIRECTION
... Dimensionality reduction is generally carried out to reduce the complexity of the computations in the large data set environment by removing redundant or de-pendent attributes. For the Lung cancer disease prediction, in ...
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Support Vector Machine Neural Network Based Optimal Binary Classifier for Diabetic Retinopathy
... the neural network as optimal binary classifier for diabetic ...layer neural network and principal component based performance analysis is ...direct diagnosis. Result shows that this ...
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OPTIMIZATION OF HIGH VOLTAGE POWER SUPPLY FOR INDUSTRIAL MICROWAVE GENERATORS FOR ONE MAGNETRON
... fault diagnosis of nonlinear system has become a hot and difficult problem in today's fields of fault diagnosis; transformer fault diagnosis is one of ...Because neural network has the ...
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Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network
... We developed a web-based CAD system for patients and ophthalmologists at Zhong- shan Ophthalmic Center at Sun Yat-sen University to promote future clinical application use of our model. The website provides ...
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Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks
... automatic diagnosis system for brain tumour detection and ...convolutional neural network (CNN), and feature classification using the error-correcting output codes support vector machine (ECOC-SVM) ...
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Application research on rough set neural network in the fault diagnosis system of ball mill
... fault diagnosis method of rough set to optimize neural network, and by using width algorithm, so that the fault sample set of ball mill had been processed with the discrete ...a diagnosis ...
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A Neural Network Approach for Numeral Font Recognition
... Artifiсial Neural Network (ANN) is an information proсessing struсture that is adapted from biologiсal nervous systems, suсh as the nervous system, ...biologiсal neural network is made up of a ...
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Investigating electrochemical drilling (ECD) using statistical and soft computing techniques
... (modular neural network), GFF (generalized feed forward), and CANFIS (coactive neuro-fuzzy inference system) models were compared with the intention of identifying the model with higher ...
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Fault Diagnosis of Motor Bearing Based on the Bayesian Network
... probability network used to describe the relationship between variables, and it is also an acyclic graph with probability label which can visually show the distribution function of joint probability between ...
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Techniques for Lung Cancer Nodule Detection: A Survey
... trained neural network based on Bayes Classification known as Multivariate Multinomial Distributed Bayes Classification which categorizes the image under normal and abnormal ...
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