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2018 Index IEEE Journal of Biomedical and Health Informatics Vol. 22
2018
IEEE journal of biomedical and health informatics
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The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. ...
The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. ...
Chen, F., +, JBHI July 2018 1209-1217 Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks. ...
doi:10.1109/jbhi.2018.2880294
fatcat:3cy3e7no55emlgbxfe3mwef3vu
2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24
2020
IEEE journal of biomedical and health informatics
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., and Inan, O.T., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre ...
2020 3529-3538 Honda, O., see Xu, R., 2041-2052 Hong, H., see 2833-2843 Hong, H., see Xue, B., JBHI Feb. 2020 614-625 Hoog Antink, C., Mai, Y., Aalto, R., Bruser, C., Leonhardt, S., Oksala, N., and ...
Efficient and Effective Training of COVID-19 Classification Networks With Self-Supervised Dual-Track Learning to Rank. ...
doi:10.1109/jbhi.2020.3048808
fatcat:iifrkwtzazdmboabdqii7x5ukm
DeepHealth: Review and challenges of artificial intelligence in health informatics
[article]
2020
arXiv
pre-print
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Artificial intelligence has provided us with an exploration of a whole new research era. ...
infectious disease outbreaks with high accuracy. ...
Deep Belief Networks Deep belief network (DBN) is composed of a stacked RBM and a belief network [82] [83] [84] . ...
arXiv:1909.00384v2
fatcat:sy7pm2c2uvdd3pal2russn4xri
A Review on Deep Learning Techniques for IoT Data
2022
Electronics
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Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. ...
In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. ...
Conflicts of Interest: The authors declare that they have no conflict of interest. ...
doi:10.3390/electronics11101604
fatcat:xdqompeur5a6pb72e7d5gtpqxa
Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review
2022
Sensors
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The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson's disease patients, their caregivers ...
Parkinson's disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. ...
others; and neural networks, such as MLPs, CNNs, LSTMs, RNNs, ESNs, self-organizing maps (SOMs) and AEs, among others. ...
doi:10.3390/s22051799
pmid:35270944
pmcid:PMC8915040
fatcat:rqwpjamitvazxhkmntrkqrsdha
Self-Organising Map Based Framework for Investigating Accounts Suspected of Money Laundering
2021
Frontiers in Artificial Intelligence
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Our method takes advantage of the competitive and adaptive properties of SOM to represent the accounts in a lower-dimensional space. ...
In this study, we present a self-organising map (SOM) based approach to predict which bank accounts are possibly involved in money laundering cases, given their financial transaction histories. ...
Remote Tracking of Parkinson's Disease Progression Using Ensembles of Deep Belief Network and Self-Organizing Map. Expert Syst. ...
doi:10.3389/frai.2021.761925
pmid:34970642
pmcid:PMC8713506
fatcat:krvbibcv4fch5n6r477g2mkjg4
AI Techniques for COVID-19
2020
IEEE Access
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It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. ...
We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. ...
The author reasoned that CNN was better than a deep belief network (DBN) and stacked denoising autoencoder (SDAE) in diagnosing threatening lung knobs with an AUC of 0.899. ...
doi:10.1109/access.2020.3007939
pmid:34976554
pmcid:PMC8545328
fatcat:h7v76znrhnerdiw46lt23qbaai
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
[article]
2021
arXiv
pre-print
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We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis. ...
It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. ...
The performance of the fine-tuned model was then assessed using the testing set of the Asian cohort. 2) Parkinson's disease: Parkinson's Disease (PD) is a neurological disorder characterized by motor and ...
arXiv:2105.13137v1
fatcat:gm7d2ziagba7bj3g34u4t3k43y
Attention, please! A survey of Neural Attention Models in Deep Learning
[article]
2021
arXiv
pre-print
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By critically analyzing 650 works, we describe the primary uses of attention in convolutional, recurrent networks and generative models, identifying common subgroups of uses and applications. ...
For the last six years, this property has been widely explored in deep neural networks. ...
These architectures and all that use self-attention belong to a new category of neural networks, called Self-Attentive Neural Networks. ...
arXiv:2103.16775v1
fatcat:lwkw42lrircorkymykpgdmlbwq
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
2022
Sensors
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Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. ...
Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human–computer interaction, that measure and improve our daily lives. ...
Acknowledgments: Special thanks to Haik Kalamtarian and Krystina Neuman for their valuable feedback.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s22041476
pmid:35214377
pmcid:PMC8879042
fatcat:vp6jssypezbd5cnyzn4g35eqrm
2021 Index IEEE Transactions on Instrumentation and Measurement Vol. 70
2021
IEEE Transactions on Instrumentation and Measurement
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The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, and article number. ...
Article numbers are based on specified topic areas and corresponding codes associated with the publication. ...
., +, TIM 2021 3000311 Defining Optimal Exercises for Efficient Detection of Parkinson's Disease Using Machine Learning and Wearable Sensors. ...
doi:10.1109/tim.2022.3156705
fatcat:dmqderzenrcopoyipv3v4vh4ry
Smartphone Sensors for Health Monitoring and Diagnosis
2019
Sensors
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However, most of these diseases can be avoided and/or properly managed through continuous monitoring. ...
The ever-increasing penetration of smartphones, coupled with embedded sensors and modern communication technologies, make it an attractive technology for enabling continuous and remote monitoring of an ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s19092164
fatcat:5lex22tgevbwbj4on5nugg3a34
29th Annual Computational Neuroscience Meeting: CNS*2020
2020
BMC Neuroscience
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Investigations of this question have, to date, focused largely on deep neural networks trained using supervised learning, in tasks such as image classification. ...
Deep RL offers a rich framework for studying the interplay among learning, representation and decision-making, offering to the brain sciences a new set of research tools and a wide range of novel hypotheses ...
Acknowledgements: This research is funded by the National Science Foundation (grants #1822517 and #1921515 to SJ), the National Institute of Mental Health (grant #MH117488 to SJ), the California Nano-Systems ...
doi:10.1186/s12868-020-00593-1
pmid:33342424
fatcat:edosycf35zfifm552a2aogis7a
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
[article]
2018
arXiv
pre-print
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In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely Deep Learning (DL), to facilitate the analytics and learning in the IoT domain. ...
The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. ...
Hinton et al. presented the concept of deep belief networks [24] . ...
arXiv:1712.04301v2
fatcat:kr64lst37rhlfcpaxckgzlozvu
Program
2022
2022 International Conference on Decision Aid Sciences and Applications (DASA)
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The selected features from EEG recordings of 23 subjects (AD-12 and NC-11) are used to train and test the Leastsquare support vector machine (LS-SVM) classifier with three different kernel functions. ...
Alzheimer's disease (AD) diagnosis is performed through the patient's interviews or questionnaires by an experienced psychiatrist. This process is time-consuming, biases, and subject-specific. ...
Monitoring farmlands using IoT system and networks of devices allows remote sensing with real-time data. ...
doi:10.1109/dasa54658.2022.9765271
fatcat:ttqppf4j3navnaxe653mrzmezi
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