keyword
MENU ▼
Read by QxMD icon Read
search

Problem base learning

keyword
https://www.readbyqxmd.com/read/28230767/an-adaptive-multi-sensor-data-fusion-method-based-on-deep-convolutional-neural-networks-for-fault-diagnosis-of-planetary-gearbox
#1
Luyang Jing, Taiyong Wang, Ming Zhao, Peng Wang
A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections...
February 21, 2017: Sensors
https://www.readbyqxmd.com/read/28230161/robust-high-dimensional-bioinformatics-data-streams-mining-by-odr-iovfdt
#2
Dantong Wang, Simon Fong, Raymond K Wong, Sabah Mohammed, Jinan Fiaidhi, Kelvin K L Wong
Outlier detection in bioinformatics data streaming mining has received significant attention by research communities in recent years. The problems of how to distinguish noise from an exception and deciding whether to discard it or to devise an extra decision path for accommodating it are causing dilemma. In this paper, we propose a novel algorithm called ODR with incrementally Optimized Very Fast Decision Tree (ODR-ioVFDT) for taking care of outliers in the progress of continuous data learning. By using an adaptive interquartile-range based identification method, a tolerance threshold is set...
February 23, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28229247/achieving-population-level-change-through-a-system-contextual-approach-to-supporting-competent-parenting
#3
REVIEW
Matthew R Sanders, Kylie Burke, Ronald J Prinz, Alina Morawska
The quality of parenting children receive affects a diverse range of child and youth outcomes. Addressing the quality of parenting on a broad scale is a critical part of producing a more nurturing society. To achieve a meaningful population-level reduction in the prevalence rates of child maltreatment and social and emotional problems that are directly or indirectly influenced by parenting practices requires the adoption of a broad ecological perspective in supporting families to raise children. We make the case for adopting a multilevel, whole of population approach to enhance competent parenting and describe the essential tasks that must be accomplished for the approach to be successful and its effects measurable...
February 22, 2017: Clinical Child and Family Psychology Review
https://www.readbyqxmd.com/read/28227327/sleep-spindle-identification-on-eeg-signals-from-polysomnographie-recordings-using-correntropy
#4
Sebastian Ulloa, Pablo A Estevez, Pablo Huijse, Claudio M Held, Claudio A Perez, Rodrigo Chamorro, Marcelo Garrido, Cecilia Algarin, Patricio Peirano, Sebastian Ulloa, Pablo A Estevez, Pablo Huijse, Claudio M Held, Claudio A Perez, Rodrigo Chamorro, Marcelo Garrido, Cecilia Algarin, Patricio Peirano, Pablo A Estevez, Marcelo Garrido, Sebastian Ulloa, Claudio A Perez, Pablo Huijse, Rodrigo Chamorro, Cecilia Algarin, Patricio Peirano, Claudio M Held
Sleep spindles (SSs) are characteristic electroencephalographic (EEG) waveforms of sleep stages N2 and N3. One of the main problems associated with SS detection is the high number of false positives. In this paper we propose a new periodogram based on correntropy to detect SSs and enhance their characterization. Correntropy is a generalized correlation, under the information theoretic learning framework. A non-negative matrix factorization decomposition of correntropy allows us to obtain a new periodogram, which shows an improved resolution capability compared to the conventional power spectrum density...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227321/fall-detection-algorithms-for-real-world-falls-harvested-from-lumbar-sensors-in-the-elderly-population-a-machine-learning-approach
#5
Alan K Bourke, Jochen Klenk, Lars Schwickert, Kamiar Aminian, Espen A F Ihlen, Sabato Mellone, Jorunn L Helbostad, Lorenzo Chiari, Clemens Becker, Alan K Bourke, Jochen Klenk, Lars Schwickert, Kamiar Aminian, Espen A F Ihlen, Sabato Mellone, Jorunn L Helbostad, Lorenzo Chiari, Clemens Becker, Lorenzo Chiari, Lars Schwickert, Kamiar Aminian, Clemens Becker, Jorunn L Helbostad, Jochen Klenk, Alan K Bourke, Sabato Mellone, Espen A F Ihlen
Automatic fall detection will promote independent living and reduce the consequences of falls in the elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable of distinguishing falls from normal activities. However less than 7% of fall-detection algorithm studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events and to develop fall detection algorithms to combat the problems associated with falls...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227266/dictionary-learning-for-sparse-representation-and-classification-of-neural-spikes
#6
Ahmed H Dallal, Yiran Chen, Douglas Weber, Zhi-Hong Mao, Ahmed H Dallal, Yiran Chen, Douglas Weber, Zhi-Hong Mao, Douglas Weber, Zhi-Hong Mao, Yiran Chen, Ahmed H Dallal
Spike sorting is the problem of identifying and clustering neurons spiking activity from recorded extracellular electro-physiological data. This is important for experimental neuroscience. Existing approaches to solve this problem consist of three steps: spike detection, feature extraction, and clustering. In our method, we use Fisher discriminant based dictionary learning to learn dictionary, whose sub-dictionaries are class specific, and estimate discriminative sparse coding coefficients by minimizing the within class scatter and maximizing the between class scatter...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227214/activity-recognition-in-patients-with-lower-limb-impairments-do-we-need-training-data-from-each-patient
#7
Luca Lonini, Aakash Gupta, Konrad Kording, Arun Jayaraman, Luca Lonini, Aakash Gupta, Konrad Kording, Arun Jayaraman, Luca Lonini, Konrad Kording, Arun Jayaraman, Aakash Gupta
Machine learning allows detecting specific physical activities using data from wearable sensors. Such a quantification of patient mobility over time promises to accurately inform clinical decisions for physical rehabilitation. There are two strategies of setting up the machine learning problem: detect one patient's activities using data from the same patient (personal model) or detect their activities using data from other patients (global model), and we currently do not know if personal models are necessary...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226811/day-to-day-variability-in-hybrid-passive-brain-computer-interfaces-comparing-two-studies-assessing-cognitive-workload
#8
Samantha L Klosterman, Justin R Estepp, Jason W Monnin, James C Christensen, Samantha L Klosterman, Justin R Estepp, Jason W Monnin, James C Christensen, Jason W Monnin, Samantha L Klosterman, Justin R Estepp, James C Christensen
As hybrid, passive brain-computer interface systems become more advanced, it is important to grow our understanding of how to produce generalizable pattern classifiers of physiological data. One of the most difficult problems in applying machine learning algorithms to these data types is nonstationarity, which can evolve over the course of hours and days, and is more susceptible to changes resulting from complex cognitive function in comparison to simple, stimulus-based processes. This nonstationarity, referenced as day-to-day variability, results in the inability of many learning algorithms to generalize to new data...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226748/integrating-holistic-and-local-deep-features-for-glaucoma-classification
#9
Annan Li, Jun Cheng, Damon Wing Kee Wong, Jiang Liu, Annan Li, Jun Cheng, Damon Wing Kee Wong, Jiang Liu, Jun Cheng, Damon Wing Kee Wong, Jiang Liu, Annan Li
Automated glaucoma detection is an important application of retinal image analysis. Compared with segmentation based approaches, image classification based approaches have a potential of better performance. However, it still remains a challenging problem for two reasons. Firstly, due to insufficient sample size, learning effective features is difficult. Secondly, the shape variations of optic disc introduce misalignment. To address these problem, a new classification based approach for glaucoma detection is proposed, in which deep convolutional networks derived from large-scale generic dataset is used to representing the visual appearance and holistic and local features are combined to mitigate the influence of misalignment...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226618/eeg-based-driver-fatigue-detection-using-hybrid-deep-generic-model
#10
Phyo Phyo San, Sai Ho Ling, Rifai Chai, Yvonne Tran, Ashley Craig, Hung Nguyen, Phyo Phyo San, Sai Ho Ling, Rifai Chai, Yvonne Tran, Ashley Craig, Hung Nguyen, Ashley Craig, Sai Ho Ling, Hung Nguyen, Phyo Phyo San, Yvonne Tran, Rifai Chai
Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer...
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
#11
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/28225448/predicting-hcahps-scores-from-hospitals-social-media-pages-a-sentiment-analysis
#12
John W Huppertz, Peter Otto
BACKGROUND: Social media is an important communication channel that can help hospitals and consumers obtain feedback about quality of care. However, despite the potential value of insight from consumers who post comments about hospital care on social media, there has been little empirical research on the relationship between patients' anecdotal feedback and formal measures of patient experience. PURPOSE: The aim of the study was to test the association between informal feedback posted in the Reviews section of hospitals' Facebook pages and scores on two global items from the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, Overall Hospital Rating and Willingness to Recommend the Hospital...
February 22, 2017: Health Care Management Review
https://www.readbyqxmd.com/read/28225216/the-multiple-consultant-report-new-assessment-same-old-problems
#13
Omar Mukhtar, Jessica Griffin, Salma Naheed, Catherine Bryant
BACKGROUND: In December 2013 the Multiple Consultant Report (MCR) was introduced as an assessment tool for junior doctors (i.e. doctors below specialist status) in the UK, including those undertaking core medical training (CMT). It aims to capture the views of consultant supervisors about a doctor's clinical performance. OBJECTIVE: Despite the mandatory nature of the MCR there is no published academic evaluation of this tool as an assessment of, or for, learning...
February 22, 2017: Clinical Teacher
https://www.readbyqxmd.com/read/28223891/what-should-we-tell-our-patients-about-marijuana-cannabis-indica-and-cannabis-sativa
#14
EDITORIAL
Joseph Pizzorno
With several states allowing medicinal use of marijuana and a growing number decriminalizing recreational use, many of our patients are using this herbal drug. Approximately 43% of US adults have tried marijuana, with 13% using it regularly. These users are seeking help from integrative medicine practitioners regarding safety. They are looking for advice based on research and clinical experience, not politics or philosophical bias. The major health problems caused by marijuana appear to be bronchial irritation, decreased motivation, learning difficulties, and injuries...
December 2016: Integrative Medicine
https://www.readbyqxmd.com/read/28223854/mohs-surgical-reconstruction-educational-activity-a-resident-education-tool
#15
Julie A Croley, C Helen Malone, Brandon P Goodwin, Linda G Phillips, Eric L Cole, Richard F Wagner
BACKGROUND: Surgical reconstructive planning following Mohs surgery can be a difficult subject for dermatology residents to master. Prior research demonstrates that active learning is preferred and more effective compared to passive learning models and that dermatology residents desire greater complexity and volume in surgical training. We present a novel, active, problem-based learning tool for the education of Mohs reconstruction with the goal of improving residents' ability to plan surgical reconstructions...
2017: Advances in Medical Education and Practice
https://www.readbyqxmd.com/read/28223187/deepnat-deep-convolutional-neural-network-for-segmenting-neuroanatomy
#16
Christian Wachinger, Martin Reuter, Tassilo Klein
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground...
February 18, 2017: NeuroImage
https://www.readbyqxmd.com/read/28223022/early-identification-of-mild-cognitive-impairment-using-incomplete-random-forest-robust-support-vector-machine-and-fdg-pet-imaging
#17
Shen Lu, Yong Xia, Weidong Cai, Michael Fulham, David Dagan Feng
Alzheimer's disease (AD) is the most common type of dementia and will be an increasing health problem in society as the population ages. Mild cognitive impairment (MCI) is considered to be a prodromal stage of AD. The ability to identify subjects with MCI will be increasingly important as disease modifying therapies for AD are developed. We propose a semi-supervised learning method based on robust optimization for the identification of MCI from [18F]Fluorodeoxyglucose PET scans. We extracted three groups of spatial features from the cortical and subcortical regions of each FDG-PET image volume...
February 7, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28222363/very-short-term-reactive-forecasting-of-the-solar-ultraviolet-index-using-an-extreme-learning-machine-integrated-with-the-solar-zenith-angle
#18
Ravinesh C Deo, Nathan Downs, Alfio V Parisi, Jan F Adamowski, John M Quilty
Exposure to erythemally-effective solar ultraviolet radiation (UVR) that contributes to malignant keratinocyte cancers and associated health-risk is best mitigated through innovative decision-support systems, with global solar UV index (UVI) forecast necessary to inform real-time sun-protection behaviour recommendations. It follows that the UVI forecasting models are useful tools for such decision-making. In this study, a model for computationally-efficient data-driven forecasting of diffuse and global very short-term reactive (VSTR) (10-min lead-time) UVI, enhanced by drawing on the solar zenith angle (θs) data, was developed using an extreme learning machine (ELM) algorithm...
February 18, 2017: Environmental Research
https://www.readbyqxmd.com/read/28222299/adaptive-low-rank-subspace-learning-with-online-optimization-for-robust-visual-tracking
#19
Risheng Liu, Di Wang, Yuzhuo Han, Xin Fan, Zhongxuan Luo
In recent years, sparse and low-rank models have been widely used to formulate appearance subspace for visual tracking. However, most existing methods only consider the sparsity or low-rankness of the coefficients, which is not sufficient enough for appearance subspace learning on complex video sequences. Moreover, as both the low-rank and the column sparse measures are tightly related to all the samples in the sequences, it is challenging to incrementally solve optimization problems with both nuclear norm and column sparse norm on sequentially obtained video data...
February 10, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28221996/hierarchical-latent-concept-discovery-for-video-event-detection
#20
Chao Li, Zi Huang, Yang Yang, Jiewei Cao, Xiaoshui Sun, Heng Tao Shen
Semantic information is important for video event detection. How to automatically discover, model and utilize semantic information to facilitate video event detection has been a challenging problem. In this paper, we propose a novel hierarchical video event detection model, which deliberately unifies the processes of underlying semantics discovery and event modelling from video data. Specially, different from most approaches based on manually pre-defined concepts, we devise an effective model to automatically uncover video semantics by hierarchically capturing latent static-visual concepts in frame-level and latent activity concepts (i...
February 17, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
keyword
keyword
109996
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"