{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:16:07Z","timestamp":1781280967111,"version":"3.54.1"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-1617640"],"award-info":[{"award-number":["CNS-1617640"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Emerg. Topics Comput."],"published-print":{"date-parts":[[2021,7,1]]},"DOI":"10.1109\/tetc.2019.2958946","type":"journal-article","created":{"date-parts":[[2019,12,17]],"date-time":"2019-12-17T21:06:48Z","timestamp":1576616808000},"page":"1139-1150","source":"Crossref","is-referenced-by-count":38,"title":["DiabDeep: Pervasive Diabetes Diagnosis Based on Wearable Medical Sensors and Efficient Neural Networks"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6481-6389","authenticated-orcid":false,"given":"Hongxu","family":"Yin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bilal","family":"Mukadam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3098-2714","authenticated-orcid":false,"given":"Xiaoliang","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1539-0369","authenticated-orcid":false,"given":"Niraj K.","family":"Jha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TMSCS.2018.2821154"},{"key":"ref33","article-title":"FitNets: Hints for thin deep nets","author":"romero","year":"2014","journal-title":"arXiv 1412 6550"},{"key":"ref32","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"2015","journal-title":"ArXiv 1503 02531"},{"key":"ref31","article-title":"A microprocessor implemented in 65nm CMOS with configurable and bit-scalable accelerator for programmable in-memory computing","author":"jia","year":"2018","journal-title":"arXiv 1811 04047"},{"key":"ref30","first-page":"1","article-title":"Trained ternary quantization","author":"zhu","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00486"},{"key":"ref36","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.754"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01252-6_17"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2019.2914438"},{"key":"ref40","year":"2019"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2016.09.014"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2016.12.005"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.17216"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s12325-019-0870-x"},{"key":"ref15","first-page":"2079","article-title":"DeepHeart: Semi-supervised sequence learning for cardiovascular risk prediction","author":"ballinger","year":"2018","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1016\/j.procs.2018.05.041","article-title":"Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals","volume":"132","author":"swapna","year":"2018","journal-title":"Procedia Comput Sci"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref18","first-page":"116","article-title":"ShuffleNet V2: Practical guidelines for efficient CNN architecture design","author":"ma","year":"0","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00951"},{"key":"ref28","article-title":"SCANN: Synthesis of compact and accurate neural networks","author":"hassantabar","year":"2019","journal-title":"arXiv 1904 09090"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1089\/pop.2015.0181"},{"key":"ref27","article-title":"Incremental learning using a grow-and-prune paradigm with efficient neural networks","author":"dai","year":"2019","journal-title":"arXiv 1905 10952"},{"key":"ref3","year":"2016"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1561\/1000000054"},{"key":"ref29","article-title":"Grow and prune compact, fast, and accurate LSTMs","author":"dai","year":"2019","journal-title":"IEEE Trans Comput"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/s16122093"},{"key":"ref8","first-page":"595","article-title":"Transfer learning on fMRI datasets","author":"zhang","year":"2018","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TMSCS.2017.2710194"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"917","DOI":"10.2337\/dci18-0007","article-title":"Economic costs of diabetes in the U.S. in 2017","volume":"41","author":"yang","year":"2018","journal-title":"Diabetes Care"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1097\/01.mlr.0000182534.19832.83"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(16)00618-8"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01099"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(88)90021-4"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01166"},{"key":"ref21","first-page":"1","article-title":"Neural architect: A multi-objective neural architecture search with performance prediction","author":"zhou","year":"2018","journal-title":"Proc Conf Syst Mach Learn"},{"key":"ref42","first-page":"343","article-title":"ECG feature extraction using Daubechies wavelets","author":"mahmoodabadi","year":"2005","journal-title":"Proc Int Conf Vis Imag Image Process"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3020078.3021745"},{"key":"ref41","first-page":"1","article-title":"Automatic differentiation in PyTorch","author":"paszke","year":"2017","journal-title":"Proc NIPS Workshop Autodiff"},{"key":"ref23","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","author":"han","year":"2015","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref44","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref26","first-page":"1","article-title":"Exploring sparsity in recurrent neural networks","author":"narang","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-014-1443-1"},{"key":"ref25","first-page":"1","article-title":"Learning intrinsic sparse structures within long short-term memory","author":"wen","year":"0","journal-title":"Proc Int Conf Learn Representations"}],"container-title":["IEEE Transactions on Emerging Topics in Computing"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/6245516\/9540920\/8935429-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6245516\/9540920\/08935429.pdf?arnumber=8935429","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:53:53Z","timestamp":1652194433000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8935429\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,1]]},"references-count":45,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tetc.2019.2958946","relation":{},"ISSN":["2168-6750","2376-4562"],"issn-type":[{"value":"2168-6750","type":"electronic"},{"value":"2376-4562","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,1]]}}}