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https://www.readbyqxmd.com/read/29181238/development-of-a-stress-classification-model-using-deep-belief-networks-for-stress-monitoring
#1
Se-Hui Song, Dong Keun Kim
Objectives: Stress management is related to public healthcare and quality of life; an accurate stress classification method is necessary for the design of stress monitoring systems. Therefore, the goal of this study was to design a novel stress classification model using a deep learning method. Methods: In this paper, we present a stress classification model using the dataset from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze stress-related health data...
October 2017: Healthcare Informatics Research
https://www.readbyqxmd.com/read/29095063/a-hybrid-classifier-based-on-nonlinear-pca-and-deep-belief-networks-with-applications-in-dysphagia-diagnosis
#2
Chong Su, Yue Gao, Yuxiao Xie, Yong Xue, Lijun Ge, Hongguang Li
Traditional dysphagia prescreening diagnostic methods require doctors specialists to give patients a total score based on a water swallow test scale. This method is limited by the high dimensionality of the diagnostic elements in the water swallow test scale with heavy workload (Towards each patient, the scale requires the doctors give score for 18 diagnostic elements respectively) as well as the difficulties of extracting and using the diagnostic scale data's non-linear features and hidden expertise information (Even with the scale scores, specific diagnostic conclusions are still given by expert doctors under the expertise)...
November 2, 2017: Computer Assisted Surgery (Abingdon, England)
https://www.readbyqxmd.com/read/29072144/a-new-method-for-enhancer-prediction-based-on-deep-belief-network
#3
Hongda Bu, Yanglan Gan, Yang Wang, Shuigeng Zhou, Jihong Guan
BACKGROUND: Studies have shown that enhancers are significant regulatory elements to play crucial roles in gene expression regulation. Since enhancers are unrelated to the orientation and distance to their target genes, it is a challenging mission for scholars and researchers to accurately predicting distal enhancers. In the past years, with the high-throughout ChiP-seq technologies development, several computational techniques emerge to predict enhancers using epigenetic or genomic features...
October 16, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29060616/a-separated-feature-learning-based-dbn-structure-for-classification-of-ssmvep-signals
#4
Yaguang Jia, Jun Xie, Guanghua Xu, Min Li, Sicong Zhang, Ailing Luo, Xingliang Han
Signal processing is one of the key points in brain computer interface (BCI) application. The common methods in BCI signal classification include canonical correlation analysis (CCA), support vector machine (SVM) and so on. However, because BCI signals are very complex and valid signals often come with confounded background noise, many current classification methods would lose meaningful information embedded in human EEGs. Otherwise, due to the huge inter-subject variability with respect to characteristics and patterns of BCI signals, there often exists large difference of classification accuracy among different subjects...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29060497/recognition-physical-activities-with-optimal-number-of-wearable-sensors-using-data-mining-algorithms-and-deep-belief-network
#5
Ali H Al-Fatlawi, Hayder K Fatlawi, Sai Ho Ling
Daily physical activities monitoring is benefiting the health care field in several ways, in particular with the development of the wearable sensors. This paper adopts effective ways to calculate the optimal number of the necessary sensors and to build a reliable and a high accuracy monitoring system. Three data mining algorithms, namely Decision Tree, Random Forest and PART Algorithm, have been applied for the sensors selection process. Furthermore, the deep belief network (DBN) has been investigated to recognise 33 physical activities effectively...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29018640/a-multi-classifier-system-for-automatic-mitosis-detection-in-breast-histopathology-images-using-deep-belief-networks
#6
K Sabeena Beevi, Madhu S Nair, G R Bindu
Mitotic count is an important diagnostic factor in breast cancer grading and prognosis. Detection of mitosis in breast histopathology images is very challenging mainly due to diffused intensities along object boundary and shape variation in different stages of mitosis. This paper demonstrates an accurate technique for detecting the mitotic cells in Hematoxyline and Eosin stained images by step by step refinement of segmentation and classification stages. Krill Herd Algorithm-based localized active contour model precisely segments cell nuclei from background stroma...
2017: IEEE Journal of Translational Engineering in Health and Medicine
https://www.readbyqxmd.com/read/28991060/hemodynamic-instability-and-cardiovascular-events-after-traumatic-brain-injury-predict-outcome-after-artifact-removal-with-deep-belief-network-analysis
#7
Hakseung Kim, Seung-Bo Lee, Yunsik Son, Marek Czosnyka, Dong-Joo Kim
BACKGROUND: Hemodynamic instability and cardiovascular events heavily affect the prognosis of traumatic brain injury. Physiological signals are monitored to detect these events. However, the signals are often riddled with faulty readings, which jeopardize the reliability of the clinical parameters obtained from the signals. A machine-learning model for the elimination of artifactual events shows promising results for improving signal quality. However, the actual impact of the improvements on the performance of the clinical parameters after the elimination of the artifacts is not well studied...
October 5, 2017: Journal of Neurosurgical Anesthesiology
https://www.readbyqxmd.com/read/28946991/deep-learning-ensemble-with-asymptotic-techniques-for-oscillometric-blood-pressure-estimation
#8
Soojeong Lee, Joon-Hyuk Chang
BACKGROUND AND OBJECTIVE: This paper proposes a deep learning based ensemble regression estimator with asymptotic techniques, and offers a method that can decrease uncertainty for oscillometric blood pressure (BP) measurements using the bootstrap and Monte-Carlo approach. While the former is used to estimate SBP and DBP, the latter attempts to determine confidence intervals (CIs) for SBP and DBP based on oscillometric BP measurements. METHOD: This work originally employs deep belief networks (DBN)-deep neural networks (DNN) to effectively estimate BPs based on oscillometric measurements...
November 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28919042/new-tools-for-the-visualization-of-biological-pathways
#9
Tomojit Ghosh, Xiaofeng Ma, Michael Kirby
This paper presents several geometrically motivated techniques for the visualization of high-dimensional biological data sets. The Grassmann manifold provides a robust framework for measuring data similarity in a subspace context. Sparse radial basis function classification as a visualization technique leverages recent advances in radial basis function learning via convex optimization. In the spirit of deep belief networks, supervised centroid-encoding is proposed as a way to exploit class label information...
September 15, 2017: Methods: a Companion to Methods in Enzymology
https://www.readbyqxmd.com/read/28875051/statistics-and-deep-belief-network-based-cardiovascular-risk-prediction
#10
Jaekwon Kim, Ungu Kang, Youngho Lee
OBJECTIVES: Cardiovascular predictions are related to patients' quality of life and health. Therefore, a risk prediction model for cardiovascular conditions is needed. METHODS: In this paper, we propose a cardiovascular disease prediction model using the sixth Korea National Health and Nutrition Examination Survey (KNHANES-VI) 2013 dataset to analyze cardiovascular-related health data. First, statistical analysis was performed to find variables related to cardiovascular disease using health data related to cardiovascular disease...
July 2017: Healthcare Informatics Research
https://www.readbyqxmd.com/read/28737705/emotion-recognition-from-chinese-speech-for-smart-affective-services-using-a-combination-of-svm-and-dbn
#11
Lianzhang Zhu, Leiming Chen, Dehai Zhao, Jiehan Zhou, Weishan Zhang
Accurate emotion recognition from speech is important for applications like smart health care, smart entertainment, and other smart services. High accuracy emotion recognition from Chinese speech is challenging due to the complexities of the Chinese language. In this paper, we explore how to improve the accuracy of speech emotion recognition, including speech signal feature extraction and emotion classification methods. Five types of features are extracted from a speech sample: mel frequency cepstrum coefficient (MFCC), pitch, formant, short-term zero-crossing rate and short-term energy...
July 24, 2017: Sensors
https://www.readbyqxmd.com/read/28727565/danoc-an-efficient-algorithm-and-hardware-codesign-of-deep-neural-networks-on-chip
#12
Xichuan Zhou, Shengli Li, Fang Tang, Shengdong Hu, Zhi Lin, Lei Zhang
Deep neural networks (NNs) are the state-of-the-art models for understanding the content of images and videos. However, implementing deep NNs in embedded systems is a challenging task, e.g., a typical deep belief network could exhaust gigabytes of memory and result in bandwidth and computational bottlenecks. To address this challenge, this paper presents an algorithm and hardware codesign for efficient deep neural computation. A hardware-oriented deep learning algorithm, named the deep adaptive network, is proposed to explore the sparsity of neural connections...
July 18, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28715343/deep-belief-networks-for-electroencephalography-a-review-of-recent-contributions-and-future-outlooks
#13
Faezeh Movahedi, James L Coyle, Ervin Sejdic
Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this manuscript, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state of- the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications...
July 14, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28708556/deep-learning-on-sparse-manifolds-for-faster-object-segmentation
#14
Jacinto C Nascimento, Gustavo Carneiro
We propose a new combination of deep belief networks and sparse manifold learning strategies for the 2D segmentation of non-rigid visual objects. With this novel combination, we aim to reduce the training and inference complexities while maintaining the accuracy of machine learning based non-rigid segmentation methodologies. Typical non-rigid object segmentation methodologies divide the problem into a rigid detection followed by a non-rigid segmentation, where the low dimensionality of the rigid detection allows for a robust training (i...
July 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28677638/unsupervised-fault-diagnosis-of-a-gear-transmission-chain-using-a-deep-belief-network
#15
Jun He, Shixi Yang, Chunbiao Gan
Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others...
July 4, 2017: Sensors
https://www.readbyqxmd.com/read/28606870/a-deep-learning-approach-for-predicting-the-quality-of-online-health-expert-question-answering-services
#16
Ze Hu, Zhan Zhang, Haiqin Yang, Qing Chen, Decheng Zuo
Recently, online health expert question-answering (HQA) services (systems) have attracted more and more health consumers to ask health-related questions everywhere at any time due to the convenience and effectiveness. However, the quality of answers in existing HQA systems varies in different situations. It is significant to provide effective tools to automatically determine the quality of the answers. Two main characteristics in HQA systems raise the difficulties of classification: (1) physicians' answers in an HQA system are usually written in short text, which yields the data sparsity issue; (2) HQA systems apply the quality control mechanism, which refrains the wisdom of crowd...
July 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28562217/deep-restricted-kernel-machines-using-conjugate-feature-duality
#17
Johan A K Suykens
The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal components analysis (PCA), matrix singular value decomposition, and Parzen-type models...
May 31, 2017: Neural Computation
https://www.readbyqxmd.com/read/28502383/rolling-bearing-fault-diagnosis-using-adaptive-deep-belief-network-with-dual-tree-complex-wavelet-packet
#18
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
#19
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
#20
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
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