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Front Matter: Volume 11313

Bennett A. Landman, Ivana Išgum
<span title="2020-04-23">2020</span> <i title="SPIE"> Medical Imaging 2020: Image Processing </i> &nbsp;
The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon.  ...  SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model. A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  . 11313 1131301-5 Combining model-and deep-learning-based methods for the accurate and robust segmentation of the intra-cochlear anatomy in clinical head CT images [11313-48] Cone-beam Computed Tomography  ... 
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Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks [article]

Rachel Lea Draelos, Lawrence Carin
<span title="2021-11-21">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Explanation methods facilitate the development of models that learn meaningful concepts and avoid exploiting spurious correlations.  ...  Overall, this work advances convolutional neural network explanation approaches and may aid in the development of trustworthy models for sensitive applications.  ...  The authors also thank Paidamoyo Chapfuwa, PhD, for thoughtful comments on the manuscript, David Dov, PhD, for discussion of multiple instance learning, and Geoffrey D.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.08891v4">arXiv:2011.08891v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yo6gawyxdraedbe3nmwnwzvxu4">fatcat:yo6gawyxdraedbe3nmwnwzvxu4</a> </span>
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Deep learning-based attenuation correction in the absence of structural information for whole-body PET imaging

Xue Dong, Yang Lei, Tonghe Wang, Kristin Higgins, Tian Liu, Walter J Curran, Hui Mao, Jonathon A Nye, Xiaofeng Yang
<span title="2019-12-23">2019</span> <i title="IOP Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zuggg3zvsfaatpw6invuoevl7a" style="color: black;">Physics in Medicine and Biology</a> </i> &nbsp;
This work presents a deep learning-based attenuation correction (DL-AC) method to generate attenuation corrected PET (AC PET) from non-attenuation corrected PET (NAC PET) images for whole-body PET imaging  ...  Deriving accurate structural maps for attenuation correction (AC) of whole-body PET remains challenging.  ...  ACKNOWLEDGEMENTS This research was supported in part by the National Cancer Institute of the National Institutes of Health under Award No.  ... 
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Hocus-Socus: An Error Catastrophe for Complex Hebbian Learning Implies Neocortical Proofreading [article]

Kingsley J.A. Cox, Paul R. Adams
<span title="2010-12-04">2010</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Causes (such as objects) can then be inferred from a learned internal model.  ...  Here we report that in single model neurons learning from hocs fails, and defaults to socs, if nonlinear Hebbian rules are not sufficiently connection-specific.  ...  Acknowledgements We thank the following for comments on a draft of this paper: H. Barlow, P. Dayan  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1012.0946v1">arXiv:1012.0946v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pr7zjimj4ja6bpqvarz2dhjcrq">fatcat:pr7zjimj4ja6bpqvarz2dhjcrq</a> </span>
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2019 Subject Index (A-M) IEEE Transactions on Magnetics Vol. 55

<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vszaq5iywbbidortfyofkor4lu" style="color: black;">IEEE transactions on magnetics</a> </i> &nbsp;
., +, TMAG June 2019 8103405 Deep Learning for Magnetic Field Estimation.  ...  ., +, TMAG April 2019 3400705 Learning (artificial intelligence) Deep Learning for Magnetic Field Estimation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tmag.2020.2971157">doi:10.1109/tmag.2020.2971157</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zxevj2qaozdsbkjtongdalsfdu">fatcat:zxevj2qaozdsbkjtongdalsfdu</a> </span>
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Use of Transfer Learning for Automatic Dietary Monitoring through Throat Microphone Recordings

Mehmet Ali Tugtekin Turan, Engin Erzin
<span title="2019-03-21">2019</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Recently, the teacher/student (T/S) learning paradigm is introduced as a model compression framework, where it describes a class of learning methods for training a smaller student network by mimicking  ...  This allows the use of a significantly larger set of adaptation data, adds robustness to the resulting model, and significantly improves the performance of food in [...]  ...  for the layers used in this deep learning approach.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.3841957">doi:10.5281/zenodo.3841957</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/so4kiaj4ljbw5aay36xd6dlx2q">fatcat:so4kiaj4ljbw5aay36xd6dlx2q</a> </span>
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Use of Transfer Learning for Automatic Dietary Monitoring through Throat Microphone Recordings

Mehmet Ali Tugtekin Turan, Engin Erzin
<span title="2019-03-21">2019</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Recently, the teacher/student (T/S) learning paradigm is introduced as a model compression framework, where it describes a class of learning methods for training a smaller student network by mimicking  ...  This allows the use of a significantly larger set of adaptation data, adds robustness to the resulting model, and significantly improves the performance of food in [...]  ...  for the layers used in this deep learning approach.  ... 
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A combined local and global motion estimation and compensation method for cardiac CT

Qiulin Tang, Beshan Chiang, Akinola Akinyemi, Alexander Zamyatin, Bibo Shi, Satoru Nakanishi, Bruce R. Whiting, Christoph Hoeschen
<span title="2014-03-19">2014</span> <i title="SPIE"> Medical Imaging 2014: Physics of Medical Imaging </i> &nbsp;
, scanner-independent organ dose estimates for head CT exams The purpose of this study is to develop a method for determining patient-specific, scanner-independent organ dose estimates for head CT exams  ...  At the same time the identification of these correspondences is the weak point of such model-based methods.  ...  Studies have shown that there is variation in the agreement between operators viewing the same tissue [1] suggesting that a complimentary technique for verification could improve the robustness of the  ... 
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American Personnel and Guidance Association

<span title="">1963</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6z5ge74kj5gflj64n3wsg2pgiq" style="color: black;">The Personnel and Guidance Journal</a> </i> &nbsp;
The final unit emphasizes the importance of '.he trainers' serving as models of continued development of helping skills. (JAC)  ...  Unit X provides tips for workers in special areas; a variety of vignettes and methods for handling delicate situations are outlined.  ...  This section presents three models of supervision and discusses the implications of one for the supervision of service providers in the aging network.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/j.2164-4918.1963.tb04380.x">doi:10.1002/j.2164-4918.1963.tb04380.x</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7wdumai6izaftmxtbdu53pcfhq">fatcat:7wdumai6izaftmxtbdu53pcfhq</a> </span>
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Program

<span title="2021-07-27">2021</span> <i title="IEEE"> 2021 National Conference on Communications (NCC) </i> &nbsp;
In the final section, advanced concepts related to the use of modern machine learning and deep learning algorithms on automotive radar data for varied applications such as pedestrian detection, object  ...  A notable application area where model-based schemes are typically preferable, and whose characteristics are fundamentally different from conventional deep learning applications, is wireless communications  ...  His main area of interest lie in computer vision, synthetic datasets for deep learning and generative models.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/ncc52529.2021.9530194">doi:10.1109/ncc52529.2021.9530194</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ahdw5ezvtrh4nb47l2qeos3dwq">fatcat:ahdw5ezvtrh4nb47l2qeos3dwq</a> </span>
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Intelligent Computing Research Studies in Life Science

Dr. Thilagamani S
<span title="2019-10-29">2019</span> <i title="International Journal of Pharma and Bio Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gvzrb7u4pfcbpivmwt6kb3m344" style="color: black;">International Journal of Pharma and Bio Sciences</a> </i> &nbsp;
SYSTEM OVERVIEW The proposed model comprises of sensor, Arduino, GSM Module, cell phone, verification server, the board framework of medical clinic called emergency clinic framework and the focal expert  ...  Artificial intelligence for Cloud computing in health care, for diagnosis in health care, for safety in day to day life, for biorhythm monitoring for the man kind health needs are the need of the hour  ...  SEMI-SUPERVISED LEARNING Semi-supervised learning are classes of issues where the information are sorted out.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.22376/ijpbs/10.sp01/oct/2019.1-142">doi:10.22376/ijpbs/10.sp01/oct/2019.1-142</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6fc5ghwmjbgrphetiablavajxm">fatcat:6fc5ghwmjbgrphetiablavajxm</a> </span>
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A Defense of (s)crappy Robots [article]

Ryan Jenkins, Mairéad Hurley, Eva Durall, Sebastian Martin
<span title="2020-05-26">2020</span> <i title="Zenodo"> Zenodo </i> &nbsp;
As makerspaces and STEAM learning environments have become more and more common, many commercial kits for robotics, electric circuits and digital technology have been created for schools and individual  ...  The experience of trying the activity as a learner will inform a discussion about the qualities of learning that we noticed in the w [...]  ...  We hope that others will join us in these efforts to explore other and novel uses of ML-ABMs for Constructionist learning and design.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.5501878">doi:10.5281/zenodo.5501878</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/snjtztj37fbbpeszscwhk63uj4">fatcat:snjtztj37fbbpeszscwhk63uj4</a> </span>
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2021 Index IEEE Transactions on Power Delivery Vol. 36

<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nzadtwufmrbdzbg2fbjqxggdye" style="color: black;">IEEE Transactions on Power Delivery</a> </i> &nbsp;
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  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.  ...  TPWRD April 2021 507 IEEE Standards A Learning-Image classification Transformer Winding Fault Diagnosis Using Vibration Image and Deep Learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tpwrd.2021.3134378">doi:10.1109/tpwrd.2021.3134378</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/j6k564co6rgxlpbupmrvns35ui">fatcat:j6k564co6rgxlpbupmrvns35ui</a> </span>
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Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases [chapter]

Rajesh Singh, Anita Gehlot, Dharam Buddhi
<span title="">2022</span> <i title="River Publisher"> Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases </i> &nbsp;
A comparative study of different machine learning classifiers for chronic disease prediction viz Heart Disease & Diabetes Disease is done in this paper.  ...  Precise diagnosis of these diseases on time is very significant for maintaining a healthy life.  ...  Acknowledgement The authors of this manuscript thanks Uttaranchal University for encouragement and motivation in using advance technology in Horticulture. 7. many research studies are being done to improve  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.13052/rp-9788770227667">doi:10.13052/rp-9788770227667</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/da47mjbbyzfwnbpde7rgbrlppe">fatcat:da47mjbbyzfwnbpde7rgbrlppe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220424110656/https://www.riverpublishers.com/pdf/ebook/RP9788770227667_ICIS2022.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.13052/rp-9788770227667"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

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<span title="">2019</span> <i title="IEEE"> 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) </i> &nbsp;
The database would be very useful to carry out deep learning research and various image processing tasks.  ...  This paper proposes a novel power allocation scheme for MU-MIMO systems, which minimizes the variance of channel capacities of each spatial stream of all users to achieve user fairness in terms of video  ...  A typical deep learning approach includes a deep convolutional neural network (CNN) and a database for the training and testing of the CNN.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/ispacs48206.2019.8986291">doi:10.1109/ispacs48206.2019.8986291</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gu4zaxsqkncp5n2ebj5fybk7ce">fatcat:gu4zaxsqkncp5n2ebj5fybk7ce</a> </span>
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