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https://www.readbyqxmd.com/read/28502383/rolling-bearing-fault-diagnosis-using-adaptive-deep-belief-network-with-dual-tree-complex-wavelet-packet
#1
Haidong Shao, Hongkai Jiang, Fuan Wang, Yanan Wang
Automatic and accurate identification of rolling bearing fault categories, especially for the fault severities and compound faults, is a challenge in rotating machinery fault diagnosis. For this purpose, a novel method called adaptive deep belief network (DBN) with dual-tree complex wavelet packet (DTCWPT) is developed in this paper. DTCWPT is used to preprocess the vibration signals to refine the fault characteristics information, and an original feature set is designed from each frequency-band signal of DTCWPT...
May 11, 2017: ISA Transactions
https://www.readbyqxmd.com/read/28486479/multi-label-spacecraft-electrical-signal-classification-method-based-on-dbn-and-random-forest
#2
Ke Li, Nan Yu, Pengfei Li, Shimin Song, Yalei Wu, Yang Li, Meng Liu
In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied...
2017: PloS One
https://www.readbyqxmd.com/read/28473055/automatic-feature-learning-using-multichannel-roi-based-on-deep-structured-algorithms-for-computerized-lung-cancer-diagnosis
#3
Wenqing Sun, Bin Zheng, Wei Qian
This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images...
April 13, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28356908/random-deep-belief-networks-for-recognizing-emotions-from-speech-signals
#4
REVIEW
Guihua Wen, Huihui Li, Jubing Huang, Danyang Li, Eryang Xun
Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28326009/improving-eeg-based-driver-fatigue-classification-using-sparse-deep-belief-networks
#5
Rifai Chai, Sai Ho Ling, Phyo Phyo San, Ganesh R Naik, Tuan N Nguyen, Yvonne Tran, Ashley Craig, Hung T Nguyen
This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28297857/mean-field-message-passing-equations-in-the-hopfield-model-and-its-generalizations
#6
Marc M├ęzard
Motivated by recent progress in using restricted Boltzmann machines as preprocessing algorithms for deep neural network, we revisit the mean-field equations [belief-propagation and Thouless-Anderson Palmer (TAP) equations] in the best understood of such machines, namely the Hopfield model of neural networks, and we explicit how they can be used as iterative message-passing algorithms, providing a fast method to compute the local polarizations of neurons. In the "retrieval phase", where neurons polarize in the direction of one memorized pattern, we point out a major difference between the belief propagation and TAP equations: The set of belief propagation equations depends on the pattern which is retrieved, while one can use a unique set of TAP equations...
February 2017: Physical Review. E
https://www.readbyqxmd.com/read/28269713/an-adaptive-deep-learning-approach-for-ppg-based-identification
#7
V Jindal, J Birjandtalab, M Baran Pouyan, M Nourani
Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269053/deep-learning-framework-for-detection-of-hypoglycemic-episodes-in-children-with-type-1-diabetes
#8
Phyo Phyo San, Sai Ho Ling, Hung T Nguyen
Most Type 1 diabetes mellitus (T1DM) patients have hypoglycemia problem. Low blood glucose, also known as hypoglycemia, can be a dangerous and can result in unconsciousness, seizures and even death. In recent studies, heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal are found as the most common physiological parameters to be effected from hypoglycemic reaction. In this paper, a state-of-the-art intelligent technology namely deep belief network (DBN) is developed as an intelligent diagnostics system to recognize the onset of hypoglycemia...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268855/the-effects-of-deep-network-topology-on-mortality-prediction
#9
Hao Du, Mohammad M Ghassemi, Mengling Feng
Deep learning has achieved remarkable results in the areas of computer vision, speech recognition, natural language processing and most recently, even playing Go. The application of deep-learning to problems in healthcare, however, has gained attention only in recent years, and it's ultimate place at the bedside remains a topic of skeptical discussion. While there is a growing academic interest in the application of Machine Learning (ML) techniques to clinical problems, many in the clinical community see little incentive to upgrade from simpler methods, such as logistic regression, to deep learning...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268407/semi-advised-learning-model-for-skin-cancer-diagnosis-based-on-histopathalogical-images
#10
Ammara Masood, Adel Al-Jumaily
Computer aided classification of skin cancer images is an active area of research and different classification methods has been proposed so far. However, the supervised classification models based on insufficient labeled training data can badly influence the diagnosis process. To deal with the problem of limited labeled data availability this paper presents a semi advised learning model for automated recognition of skin cancer using histopathalogical images. Deep belief architecture is constructed using unlabeled data by making efficient use of limited labeled data for fine tuning done the classification model...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268362/pain-detection-from-facial-images-using-unsupervised-feature-learning-approach
#11
Reza Kharghanian, Ali Peiravi, Farshad Moradi
In this paper a new method for continuous pain detection is proposed. One approach to detect the presence of pain is by processing images taken from the face. It has been reported that expression of pain from the face can be detected utilizing Action Units (AUs). In this manner, each action units must be detected separately and then combined together through a linear expression. Also, pain detection can be directly done from a painful face. There are different methods to extract features of both shape and appearance...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268351/single-channel-eeg-based-mental-fatigue-detection-based-on-deep-belief-network
#12
Pinyi Li, Wenhui Jiang, Fei Su
Mental fatigue has a pernicious influence on road and work place safety as well as a negative symptom of many acute and chronic illnesses, since the ability of concentrating, responding and judging quickly decreases during the fatigue or drowsiness stage. Electroencephalography (EEG) has been proven to be a robust physiological indicator of human cognitive state over the last few decades. But most existing EEG-based fatigue detection methods have poor performance in accuracy. This paper proposed a single-channel EEG-based mental fatigue detection method based on Deep Belief Network (DBN)...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28247185/cell-segmentation-in-histopathological-images-with-deep-learning-algorithms-by-utilizing-spatial-relationships
#13
Nuh Hatipoglu, Gokhan Bilgin
In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images...
February 28, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28245811/rna-protein-binding-motifs-mining-with-a-new-hybrid-deep-learning-based-cross-domain-knowledge-integration-approach
#14
Xiaoyong Pan, Hong-Bin Shen
BACKGROUND: RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e...
February 28, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28238168/learning-representation-hierarchies-by-sharing-visual-features-a-computational-investigation-of-persian-character-recognition-with-unsupervised-deep-learning
#15
Zahra Sadeghi, Alberto Testolin
In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy...
February 25, 2017: Cognitive Processing
https://www.readbyqxmd.com/read/28227975/an-adaptive-deep-learning-approach-for-ppg-based-identification
#16
V Jindal, J Birjandtalab, M Baran Pouyan, M Nourani, V Jindal, J Birjandtalab, M Baran Pouyan, M Nourani, V Jindal, M Baran Pouyan, J Birjandtalab, M Nourani
Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227270/deep-learning-framework-for-detection-of-hypoglycemic-episodes-in-children-with-type-1-diabetes
#17
Phyo Phyo San, Sai Ho Ling, Hung T Nguyen, Phyo Phyo San, Sai Ho Ling, Hung T Nguyen, Sai Ho Ling, Hung T Nguyen, Phyo Phyo San
Most Type 1 diabetes mellitus (T1DM) patients have hypoglycemia problem. Low blood glucose, also known as hypoglycemia, can be a dangerous and can result in unconsciousness, seizures and even death. In recent studies, heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal are found as the most common physiological parameters to be effected from hypoglycemic reaction. In this paper, a state-of-the-art intelligent technology namely deep belief network (DBN) is developed as an intelligent diagnostics system to recognize the onset of hypoglycemia...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227053/the-effects-of-deep-network-topology-on-mortality-prediction
#18
Hao Du, Mohammad M Ghassemi, Mengling Feng, Hao Du, Mohammad M Ghassemi, Mengling Feng, Mengling Feng, Hao Du, Mohammad M Ghassemi
Deep learning has achieved remarkable results in the areas of computer vision, speech recognition, natural language processing and most recently, even playing Go. The application of deep-learning to problems in healthcare, however, has gained attention only in recent years, and it's ultimate place at the bedside remains a topic of skeptical discussion. While there is a growing academic interest in the application of Machine Learning (ML) techniques to clinical problems, many in the clinical community see little incentive to upgrade from simpler methods, such as logistic regression, to deep learning...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226577/semi-advised-learning-model-for-skin-cancer-diagnosis-based-on-histopathalogical-images
#19
Ammara Masood, Adel Al-Jumaily, Ammara Masood, Adel Al-Jumaily, Ammara Masood, Adel Al-Jumaily
Computer aided classification of skin cancer images is an active area of research and different classification methods has been proposed so far. However, the supervised classification models based on insufficient labeled training data can badly influence the diagnosis process. To deal with the problem of limited labeled data availability this paper presents a semi advised learning model for automated recognition of skin cancer using histopathalogical images. Deep belief architecture is constructed using unlabeled data by making efficient use of limited labeled data for fine tuning done the classification model...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226525/pain-detection-from-facial-images-using-unsupervised-feature-learning-approach
#20
Reza Kharghanian, Ali Peiravi, Farshad Moradi, Reza Kharghanian, Ali Peiravi, Farshad Moradi, Ali Peiravi, Reza Kharghanian, Farshad Moradi
In this paper a new method for continuous pain detection is proposed. One approach to detect the presence of pain is by processing images taken from the face. It has been reported that expression of pain from the face can be detected utilizing Action Units (AUs). In this manner, each action units must be detected separately and then combined together through a linear expression. Also, pain detection can be directly done from a painful face. There are different methods to extract features of both shape and appearance...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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