PMID- 32375400
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20200511
LR  - 20200611
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 20
IP  - 9
DP  - 2020 May 4
TI  - An Approach towards Increasing Prediction Accuracy for the Recovery of Missing 
      IoT Data Based on the GRNN-SGTM Ensemble.
LID - 10.3390/s20092625 [doi]
LID - 2625
AB  - The purpose of this paper is to improve the accuracy of solving prediction tasks 
      of the missing IoT data recovery. To achieve this, the authors have developed a 
      new ensemble of neural network tools. It consists of two successive General 
      Regression Neural Network (GRNN) networks and one neural-like structure of the 
      Successive Geometric Transformation Model (SGTM). The principle of ensemble 
      topology construction on two successively connected general regression neural 
      networks, supplemented with an SGTM neural-like structure, is mathematically 
      substantiated, which improves the accuracy of prediction results. The 
      effectiveness of the method is based on the replacement of the summation of the 
      results of the two GRNNs with a weighted summation, which improves the accuracy 
      of the ensemble operation in general. A detailed algorithmic implementation of 
      the ensemble method as well as a flowchart of its operation is presented. The 
      parameters of the ensemble operation are determined by optimization using the 
      brute-force method. Based on the developed ensemble method, the solution of the 
      task of completing the partially missing values ​​in the real monitoring dataset 
      of the air environment collected by the IoT device is presented. By comparing the 
      performance of the developed ensemble with the existing methods, the highest 
      accuracy of its performance (by the parameters of Mean Absolute Percentage Error 
      (MAPE) and Root Mean Squared Error (RMSE) accuracy) among the most similar in 
      this class has been proved.
FAU - Tkachenko, Roman
AU  - Tkachenko R
AD  - Department of Publishing Information Technologies, Lviv Polytechnic National 
      University, 12 Bandera str., 79000 Lviv, Ukraine.
FAU - Izonin, Ivan
AU  - Izonin I
AD  - Department of Publishing Information Technologies, Lviv Polytechnic National 
      University, 12 Bandera str., 79000 Lviv, Ukraine.
FAU - Kryvinska, Natalia
AU  - Kryvinska N
AUID- ORCID: 0000-0003-3678-9229
AD  - Department of Information Systems, Faculty of Management, Comenius University in 
      Bratislava, 82005 Bratislava 25, Slovakia.
AD  - Department of e-Business, School of Business, Economics and Statistics, 
      University of Vienna, A-1090 Vienna, Austria.
FAU - Dronyuk, Ivanna
AU  - Dronyuk I
AUID- ORCID: 0000-0003-1667-2584
AD  - Department of Automated Control Systems, Lviv Polytechnic National University, 12 
      Bandera str., 79000 Lviv, Ukraine.
FAU - Zub, Khrystyna
AU  - Zub K
AD  - Center of Information Support, Lviv Polytechnic National University, 12 Bandera 
      str., 79000 Lviv, Ukraine.
LA  - eng
PT  - Journal Article
DEP - 20200504
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC7249176
OTO - NOTNLM
OT  - ANN techniques
OT  - GRNN
OT  - IoT sensors
OT  - Successive Geometric Transformation Model
OT  - data imputation
OT  - hybrid systems
OT  - missing data
OT  - neural-like structures
OT  - non-iterative training
OT  - weighted summation
COIS- The authors declare no conflict of interest.
EDAT- 2020/05/08 06:00
MHDA- 2020/05/08 06:01
PMCR- 2020/05/01
CRDT- 2020/05/08 06:00
PHST- 2020/04/10 00:00 [received]
PHST- 2020/04/27 00:00 [revised]
PHST- 2020/05/02 00:00 [accepted]
PHST- 2020/05/08 06:00 [entrez]
PHST- 2020/05/08 06:00 [pubmed]
PHST- 2020/05/08 06:01 [medline]
PHST- 2020/05/01 00:00 [pmc-release]
AID - s20092625 [pii]
AID - sensors-20-02625 [pii]
AID - 10.3390/s20092625 [doi]
PST - epublish
SO  - Sensors (Basel). 2020 May 4;20(9):2625. doi: 10.3390/s20092625.