Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

The eye is a vital part of our body. It consists of several layers like sclera, retina, tunica, and iris. Among these several layers, Iris plays a vital role in human visionary. There are various infections which affect the Iris functioning. The sign, symptoms, and diagnosis of this is still a challenge for doctors. To overcome this many techniques and technologies have been introduced. But still, the existing system has several drawbacks in recognition like a huge amount of dataset, classification, extraction, etc. To overcome this we propose a system where Deep Neural Network plays a major part. It classifies the iris disease in our eyes in a more clear and precise manner. In additional to Deep Neural Network several other algorithms have been used like Stationary Wavelet Transform, for image selection and recognition, Local Binary Pattern, for Feature extraction and at a final stage Deep Neural Network for classification of Iris images.

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NEW APPROACH FOR ONLINE ARABIC MANUSCRIPT RECOGNITION BY DEEP BELIEF NETWORK

Acta Polytechnica ◽

10.14311/ap.2018.58.0297 ◽

2018 ◽

Vol 58 (5) ◽

pp. 297

Author(s):

Benbakreti Samir ◽

Aoued Boukelif

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Deep Learning ◽

Success Rate ◽

Character Recognition ◽

Recognition Rate ◽

Deep Belief Network ◽

New Approach ◽

Belief Network ◽

Network Methods

In this paper, we present a neural approach for an unconstrained Arabic manuscript recognition using the online writing signal rather than images. First, we build the database which contains 2800 characters and 4800 words collected from 20 different handwritings. Thereafter, we will perform the pretreatment, feature extraction and classification phases, respectively. The use of a classical neural network methods has been beneficial for the character recognition, but revealed some limitations for the recognition rate of Arabic words. To remedy this, we used a deep learning through the Deep Belief Network (DBN) that resulted in a 97.08% success rate of recognition for Arabic words.

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Fault Diagnosis Method of DC Charging Points for EVs Based on Deep Belief Network

World Electric Vehicle Journal ◽

10.3390/wevj12010047 ◽

2021 ◽

Vol 12 (1) ◽

pp. 47

Author(s):

Dexin Gao ◽

Xihao Lin

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Fault Diagnosis ◽

Deep Belief Network ◽

Fine Tuning ◽

Convergence Time ◽

Support Vector ◽

Actual Measurement ◽

Belief Network ◽

Diagnosis Method

According to the complex fault mechanism of direct current (DC) charging points for electric vehicles (EVs) and the poor application effect of traditional fault diagnosis methods, a new kind of fault diagnosis method for DC charging points for EVs based on deep belief network (DBN) is proposed, which combines the advantages of DBN in feature extraction and processing nonlinear data. This method utilizes the actual measurement data of the charging points to realize the unsupervised feature extraction and parameter fine-tuning of the network, and builds the deep network model to complete the accurate fault diagnosis of the charging points. The effectiveness of this method is examined by comparing with the backpropagation neural network, radial basis function neural network, support vector machine, and convolutional neural network in terms of accuracy and model convergence time. The experimental results prove that the proposed method has a higher fault diagnosis accuracy than the above fault diagnosis methods.

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Speech Recognition using Deep Neural Network Neural (DNN) and Deep Belief Network (DBN)

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2020.5359 ◽

2020 ◽

Vol 8 (5) ◽

pp. 1543-1548

Author(s):

Prof. Kirti Rajadnya

Keyword(s):

Neural Network ◽

Speech Recognition ◽

Deep Neural Network ◽

Deep Belief Network ◽

Belief Network

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Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data

Sensors ◽

10.3390/s19010204 ◽

2019 ◽

Vol 19 (1) ◽

pp. 204 ◽

Cited By ~ 10

Author(s):

Chenming Li ◽

Yongchang Wang ◽

Xiaoke Zhang ◽

Hongmin Gao ◽

Yao Yang ◽

...

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Optical Sensors ◽

Spatial Information ◽

Hyperspectral Image ◽

Deep Belief Network ◽

Fine Tuning ◽

Sensor Data ◽

Belief Network

With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches.

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CLASSIFICATION OF LOW QUALITY IMAGES USING CONVOLUTIONAL NEURAL NETWORK AND DEEP BELIEF NETWORK

International Journal of Intelligent Computing and Information Sciences ◽

10.21608/ijicis.2016.19822 ◽

2016 ◽

Vol 16 (4) ◽

pp. 19-28

Author(s):

E. El-Ashmony ◽

M. El-Dosuky ◽

Samir Elmougy

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Deep Belief Network ◽

Belief Network

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Deep Neural Network Classification of I-123 Ioflupane SPECT

10.26226/morressier.5e8335ba7cb08a046ef7c709 ◽

2020 ◽

Author(s):

David T. Wang ◽

Brady Williamson ◽

Thomas Eluvathingal ◽

Bruce Mahoney ◽

Jennifer Scheler

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Neural Network Classification

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Method of determination of the text direction on the image with the use of convolutional neural network

Informatization and communication ◽

10.34219/2078-8320-2020-11-2-96-99 ◽

2020 ◽

pp. 96-99

Author(s):

P.L. Nikolaev

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Deep Neural Network ◽

Binary Classification ◽

Synthetic Data ◽

Real Data ◽

Method Of Determination ◽

Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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