PMID- 34720665
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240403
IS  - 1380-7501 (Print)
IS  - 1573-7721 (Electronic)
IS  - 1380-7501 (Linking)
VI  - 80
IP  - 9
DP  - 2021
TI  - Investigating response time and accuracy in online classifier learning for 
      multimedia publish-subscribe systems.
PG  - 13021-13057
LID - 10.1007/s11042-020-10277-x [doi]
AB  - The enormous growth of multimedia content in the field of the Internet of Things 
      (IoT) leads to the challenge of processing multimedia streams in real-time. 
      Event-based systems are constructed to process event streams. They cannot 
      natively consume multimedia event types produced by the Internet of Multimedia 
      Things (IoMT) generated data to answer multimedia-based user subscriptions. 
      Machine learning-based techniques have enabled rapid progress in solving 
      real-world problems and need to be optimised for the low response time of the 
      multimedia event processing paradigm. In this paper, we describe a classifier 
      construction approach for the training of online classifiers, that can handle 
      dynamic subscriptions with low response time and provide reasonable accuracy for 
      the multimedia event processing. We find that the current object detection 
      methods can be configured dynamically for the construction of classifiers in 
      real-time, by tuning hyperparameters even when training from scratch. Our 
      experiments demonstrate that deep neural network-based object detection models, 
      with hyperparameter tuning, can improve the performance within less training time 
      for the answering of previously unknown user subscriptions. The results from this 
      study show that the proposed online classifier training based model can achieve 
      accuracy of 79.00% with 15-min of training and 84.28% with 1-hour training from 
      scratch on a single GPU for the processing of multimedia events.
CI  - (c) The Author(s) 2021.
FAU - Aslam, Asra
AU  - Aslam A
AUID- ORCID: 0000-0002-2654-4255
AD  - Insight Centre for Data Analytics, NUI Galway, Galway, Ireland. GRID: 
      grid.6142.1. ISNI: 0000 0004 0488 0789
FAU - Curry, Edward
AU  - Curry E
AD  - Insight Centre for Data Analytics, NUI Galway, Galway, Ireland. GRID: 
      grid.6142.1. ISNI: 0000 0004 0488 0789
LA  - eng
PT  - Journal Article
DEP - 20210109
PL  - United States
TA  - Multimed Tools Appl
JT  - Multimedia tools and applications
JID - 101555932
PMC - PMC8550296
OTO - NOTNLM
OT  - Event-based systems
OT  - Hyperparameter tuning
OT  - Internet of Multimedia Things
OT  - Multimedia stream processing
OT  - Object detection
OT  - Online training
OT  - Smart cities
EDAT- 2021/11/02 06:00
MHDA- 2021/11/02 06:01
PMCR- 2021/01/09
CRDT- 2021/11/01 09:03
PHST- 2019/08/01 00:00 [received]
PHST- 2020/10/17 00:00 [revised]
PHST- 2020/11/24 00:00 [accepted]
PHST- 2021/11/01 09:03 [entrez]
PHST- 2021/11/02 06:00 [pubmed]
PHST- 2021/11/02 06:01 [medline]
PHST- 2021/01/09 00:00 [pmc-release]
AID - 10277 [pii]
AID - 10.1007/s11042-020-10277-x [doi]
PST - ppublish
SO  - Multimed Tools Appl. 2021;80(9):13021-13057. doi: 10.1007/s11042-020-10277-x. 
      Epub 2021 Jan 9.