TY - JOUR
T1 - Transfer Learning-Aided Collaborative Computational Method for Intelligent Transportation System Applications
AU - Nguyen, Tu N.
AU - Gao, Jiechao
AU - Manogaran, Gunasekaran
AU - Samuel, R. Dinesh Jackson
AU - Alazab, Mamoun
PY - 2022/9/1
Y1 - 2022/9/1
N2 - Intelligent Transportation System (ITS) assists communication and navigation for users and vehicles in roadside movements. It integrates information technology, computational intelligence, and distributed service platforms for providing classified assistance. The classified assistance ensures object detection, navigation, route identification, and messaging application support. This article introduces a novel Collaborative Computational Method (CCM) using Transfer Learning (TL) for condensed information analysis. In this method, application-centric computations are performed for decision-making and thwarting replicated and false information handling. The information is computed by exploiting the previous application-accuracy knowledge segregating different inputs. This selective computation relies on current and previous information knowledge collaboratively. The learning process is responsible for shift-based validation of computation accuracy using collaborative information. The proposed method’s performance is analyzed using accuracy, computation time, complexity, and information backlogs. The proposed CCM-TL improves by 8.5% and 4.91% accuracy and information sharing. It similarly reduces computation time, complexity, and backlogs by 14.97%, 6.7%, and 16.67%, respectively.
AB - Intelligent Transportation System (ITS) assists communication and navigation for users and vehicles in roadside movements. It integrates information technology, computational intelligence, and distributed service platforms for providing classified assistance. The classified assistance ensures object detection, navigation, route identification, and messaging application support. This article introduces a novel Collaborative Computational Method (CCM) using Transfer Learning (TL) for condensed information analysis. In this method, application-centric computations are performed for decision-making and thwarting replicated and false information handling. The information is computed by exploiting the previous application-accuracy knowledge segregating different inputs. This selective computation relies on current and previous information knowledge collaboratively. The learning process is responsible for shift-based validation of computation accuracy using collaborative information. The proposed method’s performance is analyzed using accuracy, computation time, complexity, and information backlogs. The proposed CCM-TL improves by 8.5% and 4.91% accuracy and information sharing. It similarly reduces computation time, complexity, and backlogs by 14.97%, 6.7%, and 16.67%, respectively.
KW - Collaboration
KW - Collaborative Computation
KW - Decision making
KW - Decision-Making
KW - Information Processing
KW - Intelligent transportation systems
KW - ITS
KW - Navigation
KW - Roads
KW - Safety
KW - Transfer Learning.
KW - Transportation
UR - http://www.scopus.com/inward/record.url?scp=85129672046&partnerID=8YFLogxK
U2 - 10.1109/TGCN.2022.3171511
DO - 10.1109/TGCN.2022.3171511
M3 - Article
AN - SCOPUS:85129672046
SN - 2473-2400
VL - 6
SP - 1355
EP - 1367
JO - IEEE Transactions on Green Communications and Networking
JF - IEEE Transactions on Green Communications and Networking
IS - 3
ER -