{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:09:00Z","timestamp":1772039340344,"version":"3.50.1"},"reference-count":27,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1109\/iscc50000.2020.9219631","type":"proceedings-article","created":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T21:03:51Z","timestamp":1602536631000},"page":"1-6","source":"Crossref","is-referenced-by-count":9,"title":["Using DenseNet for IoT multivariate time series classification"],"prefix":"10.1109","author":[{"given":"Joseph","family":"Azar","sequence":"first","affiliation":[]},{"given":"Abdallah","family":"Makhoul","sequence":"additional","affiliation":[]},{"given":"Raphael","family":"Couturier","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-019-00619-1"},{"key":"ref11","article-title":"Time series classification from scratch with deep neural networks: A strong baseline","volume":"abs 1611 6455","author":"wang","year":"2016","journal-title":"CoRR"},{"key":"ref12","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume":"abs 1502 3167","author":"ioffe","year":"2015","journal-title":"CoRR"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref14","article-title":"Neural machine translation by jointly learning to align and translate","volume":"abs 1409 473","author":"bahdanau","year":"2014","journal-title":"CoRR"},{"key":"ref15","article-title":"Multivariate lstm-fcns for time series classification","volume":"abs 1801 4503","author":"karim","year":"2018","journal-title":"CoRR"},{"key":"ref16","article-title":"The ucr time series classification archive","author":"chen","year":"2015"},{"key":"ref17","article-title":"Mustafa baydogan - multivariate time series classification data sets","year":"0"},{"key":"ref18","article-title":"Fully convolutional networks for semantic segmentation","volume":"abs 1411 4038","author":"long","year":"2014","journal-title":"CoRR"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.113"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s12530-017-9190-z","article-title":"Predictive intelligence to the edge: impact on edge analytics","volume":"9","author":"harth","year":"2018","journal-title":"Evolving Systems"},{"key":"ref27","article-title":"TensorFlow: Large-scale machine learning on heterogeneous systems","author":"abadi","year":"2015"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.02.005"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2014.04.007"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CogInfoCom.2015.7390577"},{"key":"ref8","article-title":"The great time series classification bake off: An experimental evaluation of recently proposed algorithms. extended version","author":"bagnall","year":"2016"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2014.12.002"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2017.9"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2416723"},{"key":"ref1","article-title":"Towards a definition of the internet of things (iot)","year":"0"},{"key":"ref20","article-title":"Squeeze-and-excitation networks","author":"hu","year":"2017"},{"key":"ref22","article-title":"Densenets for time series classification: towards automation of time series preprocessing with cnns","author":"richard","year":"0"},{"key":"ref21","article-title":"Densely connected convolutional networks","author":"huang","year":"2016"},{"key":"ref24","article-title":"UCI machine learning repository","author":"dua","year":"2017"},{"key":"ref23","article-title":"Adam: A method for stochastic optimization","volume":"abs 1412 6980","author":"kingma","year":"2014","journal-title":"CoRR"},{"key":"ref26","article-title":"Keras","author":"chollet","year":"2015"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2010.5543273"}],"event":{"name":"2020 IEEE Symposium on Computers and Communications (ISCC)","location":"Rennes, France","start":{"date-parts":[[2020,7,7]]},"end":{"date-parts":[[2020,7,10]]}},"container-title":["2020 IEEE Symposium on Computers and Communications (ISCC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9213178\/9219543\/09219631.pdf?arnumber=9219631","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T21:56:41Z","timestamp":1656453401000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9219631\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/iscc50000.2020.9219631","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}