Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk st...
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Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk stratification. Brain connectome studies in adults and older children have shown that abnormal network properties may be useful as discriminative features and have exploited machine learning models for early diagnosis in a variety of neurological conditions. However, analogous studies in neonates are rare and with limited significant findings. In this paper, we propose an artificial neural network (ANN) framework for early prediction of cognitive deficits in very preterm infants based on functional connectome data from resting state fMRI. Specifically, we conducted feature selection via stacked sparse autoencoder and outcome prediction via support vector machine (SVM). The proposed ANN model was unsupervised learned using brain connectome data from 884 subjects in autism brain imaging data exchange database and SVM was cross-validated on 28 very preterm infants (born at 23-31 weeks of gestation and without brain injury;scanned at term-equivalent postmenstrual age). Using 90 regions of interests, we found that the ANN model applied to functional connectome data from very premature infants can predict cognitive outcome at 2 years of corrected age with an accuracy of 70.6% and area under receiver operating characteristic curve of 0.76. We also noted that several frontal lobe and somatosensory regions, significantly contributed to prediction of cognitive deficits 2 years later. Our work can be considered as a proof of concept for utilizing ANN models on functional connectome data to capture the individual variability inherent in the developing brains of preterm infants. The full potential of ANN will be realized and more robust conclusions drawn when applied to much larger neuroimaging datasets, as we plan to do.
Along with the rapid development of communication network construction, the operation energy consumption grows significantly in recent years, and the expensive electricity cost is hard to he ignored. Therefore, it is ...
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ISBN:
(纸本)9780769556680
Along with the rapid development of communication network construction, the operation energy consumption grows significantly in recent years, and the expensive electricity cost is hard to he ignored. Therefore, it is necessary to develop an operation energy anomaly detection mechanism to enhance the control ability of electricity cost. According to the practical distribution and data characteristic of smart meters, this paper presents a distributed anomaly detection method of operation energy consumption based on deep learning methods. An IOT-based distributed structure is implemented to execute data interaction. stacked sparse autoencoder is used to extract the high-level representation from massive monitoring data acquired automatically from actual smart meter network. Then softmax is used for classification to detect anomaly and send alarm messages using web technologies. The experimental results show that the proposed method with good prospect for intelligent applications achieves better accuracy and meanwhile decreases computing delay caused by central arithmetic method.
Fault diagnosis is an integral component of Condition Monitoring systems used in industries. Deep learning techniques have recently been proven to be very effective in handling heterogeneous real time monitoring data....
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ISBN:
(纸本)9781538660577
Fault diagnosis is an integral component of Condition Monitoring systems used in industries. Deep learning techniques have recently been proven to be very effective in handling heterogeneous real time monitoring data. This paper presents a computational framework for machine fault diagnosis based on deep learning approaches and autoencoder variants. The framework includes methods for extraction of time domain and frequency domain features from raw data, training of autoencoders, and finally, detecting the presence of fault(s) using classifiers. A total of four different variants of autoencoders have been tested and analyzed for fault diagnosis framework;additionally, the option of reducing the learning complexity in autoencoder with layer wise feature selection strategy with ITER ranking has also been applied. For validation purpose, four standard fault diagnosis datasets were used. To obtain inferences, statistical tests was performed across all tested feature extraction techniques, autoencoder variants, and classifiers. The analysis suggests that stacked denoising sparse encoder in conjunction with frequency domain features and support vector classifiers gives consistent classification accuracy thus builds effective fault diagnosis system.
Features are important for polarimetric synthetic aperture radar (PolSAR) image classification. Various methods focus on extracting feature artificially. Compared with them, we have developed a method to learn feature...
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ISBN:
(纸本)9781479957750
Features are important for polarimetric synthetic aperture radar (PolSAR) image classification. Various methods focus on extracting feature artificially. Compared with them, we have developed a method to learn feature automatically. The method is based on deep learning which can learn multilayer features. In this paper, stacked sparse autoencoder (SAE) as one of the deep learning models is applied as a useful strategy to achieve the goal. For improving the classification result, we use a small amount of labels to fine-tuning the parameters of the proposed method Finally, a real PolSAR dataset is used to verify the effectiveness. Experiment result confirms that the proposed method provides noteworthy improvements in classification accuracy and visual effect.
Effective early diagnosis of autism can have a significant impact on its intervention and treatment. In this paper, an approach is proposed for comprehensively considering genetic factors and environmental factors to ...
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ISBN:
(纸本)9781728138046
Effective early diagnosis of autism can have a significant impact on its intervention and treatment. In this paper, an approach is proposed for comprehensively considering genetic factors and environmental factors to predict the severity of autism. According to the Childhood Autism Rating Scale (CARS), a sample set was collected from the autism clinic and a predictive model based on a stacked sparse autoencoder combined with a softmax classifier was constructed. We compared the proposed model with decision trees and support vector machines. Experiments show that the proposed model has a highest accuracy in predicting the severity of autism. Our method can help patients predict their condition and assist doctors in accurate diagnosis.
The fault of power grid will cause serious personal safety problems and economic losses. It is very important to diagnose the power grid fault accurately and quickly. In order to improve the fault diagnosis accuracy f...
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ISBN:
(纸本)9781665422482
The fault of power grid will cause serious personal safety problems and economic losses. It is very important to diagnose the power grid fault accurately and quickly. In order to improve the fault diagnosis accuracy for hybrid AC-DC power grid, this paper proposes a stacked sparse autoencoder-convolutional neural network method. The paper uses stacked sparse autoencoder (SSAE) to reduce the dimensionality of high dimensional data sets, and then uses convolutional neural network (CNN) to extracts data features to diagnose different line faults and different types of faults in the power grid. Finally, the effectiveness of the proposed method is validated by MATLAB simulation, and shows that the proposed method has a high accuracy to distinguish different faults.
Facial expression recognition has important practical applications. In this paper, we propose a method based on the combination of optical flow and a deep neural network-stacked sparse autoencoder (SAE). This method c...
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ISBN:
(纸本)9781479957118
Facial expression recognition has important practical applications. In this paper, we propose a method based on the combination of optical flow and a deep neural network-stacked sparse autoencoder (SAE). This method classifies facial expressions into six categories (i.e. happiness, sadness, anger, fear, disgust and surprise). In order to extract the representation of facial expressions, we choose the optical flow method because it could analyze video image sequences effectively and reduce the influence of personal appearance difference on facial expression recognition. Then, we train the stacked SAE with the optical flow field as the input to extract high-level features. To achieve classification, we apply a softmax classifier on the top layer of the stacked SAE. This method is applied to the Extended Cohn-Kanade Dataset (CK+). The expression classification result shows that the SAE performances the classification effectively and successfully. Further experiments (transformation and purification) are carried out to illustrate the application of the feature extraction and input reconstruction ability of SAE.
Translucency, defined as a jelly-like appearance, is a common clinical feature of basal cell carcinoma, the most common skin cancer. The feature plays an important role in diagnosing basal cell carcinoma in an early s...
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ISBN:
(纸本)9789881476852
Translucency, defined as a jelly-like appearance, is a common clinical feature of basal cell carcinoma, the most common skin cancer. The feature plays an important role in diagnosing basal cell carcinoma in an early stage because the feature can be observed readily in clinical examinations with a high specificity of 93%. Therefore, translucency detection is a critical component of computer aided systems which aim at early detection of basal cell carcinoma. To address this problem, we proposed an automated method for analyzing patches of clinical basal cell carcinoma images using stacked sparse autoencoder ( SSAE). SSAE learns high-level features in unsupervised manner and all learned features are fed into a softmax classifier for translucency detection. Across the 4401 patches generated from 32 clinical images, the proposed method achieved a 93% detection accuracy from a five-fold cross-validation. The preliminary result suggested that the proposed method could detect translucency from skin images.
Classifying remote sensing images with high spectral and spatial resolution became an important topic and challenging task in computer vision and remote sensing (RS) fields because of their huge dimensionality and com...
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ISBN:
(纸本)9783030007676;9783030007669
Classifying remote sensing images with high spectral and spatial resolution became an important topic and challenging task in computer vision and remote sensing (RS) fields because of their huge dimensionality and computational complexity. Recently, many studies have already demonstrated the efficiency of employing spatial information where a combination of spectral and spatial information in a single classification framework have attracted special attention because of their capability to improve the classification accuracy. Shape and texture features are considered as two important types of spatial features in various applications of image processing. In this study, we extracted multiple features from spectral and spatial domains where we utilized texture and shape features, as well as spectral features, in order to obtain high classification accuracy. The spatial features considered in this study are produced by Gray Level Co-occurrence Matrix (GLCM) and Extended Multi-Attribute Profiles (EMAP), while, the extraction of deep spectral features is done by stacked sparse autoencoders. The obtained spectral-spatial features are concatenated directly as a simple feature fusion and are fed into the Support Vector Machine (SVM) classifier. We tested the proposed method on hyperspectral (HS) and multispectral (MS) images where the experiments demonstrated significantly the efficiency of the proposed framework in comparison with some recent spectral-spatial classification methods and with different classification frameworks based on the used extractors.
作者：
Shan, JuanLi, LinPace Univ
Seidenberg Sch CSIS Dept Comp Sci New York NY 10038 USA Seattle Univ
Coll Sci & Engn Dept Comp Sci & Software Engn Seattle WA 98122 USA
Diabetic Retinopathy (DR) is the leading cause of blindness in the working-age population. Microaneurysms (MAs), due to leakage from retina blood vessels, are the early signs of DR. However, automated MA detection is ...
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ISBN:
(纸本)9781509009435
Diabetic Retinopathy (DR) is the leading cause of blindness in the working-age population. Microaneurysms (MAs), due to leakage from retina blood vessels, are the early signs of DR. However, automated MA detection is complicated because of the small size of MA lesions and the low contrast between the lesion and its retinal background. Recently deep learning (DL) strategies have been used for automatic feature extraction and classification problems, especially for image analysis. In this paper, a stacked sparse autoencoder (SSAE), an instance of a DL strategy, is presented for MA detection in fundus images. Small image patches are generated from the original fundus images. The SSAE learns high-level features from pixel intensities alone in order to identify distinguishing features of MA. The high-level features learned by SSAE are fed into a classifier to categorize each image patch as MA or non-MA. The public benchmark DIARETDB is utilized to provide the training/testing data and ground truth. Among the 89 images, totally 2182 image patches with MA lesions, serve as positive data, and another 6230 image patches without MA lesions are generated by a randomly sliding window operation, to serve as negative data. Without any blood vessel removal or complicated preprocessing operations, SSAE learned directly from the raw image patches, and automatically extracted the distinguishing features to classify the patches using Softmax Classifier. By employing the fine-tuning operation, an improved F-measure 91.3% and an average area under the ROC curve (AUC) 96.2% were achieved using 10-fold cross-validation.
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