Deep Mobile Path Prediction With Shift-and-Join and Carry-Ahead-Reference-Cited by-同舟云学术

Deep Mobile Path Prediction With Shift-and-Join and Carry-Ahead

Published:2023-06 Issue:3 Volume:9 Page:811-825
ISSN:2332-7731
Container-title:IEEE Transactions on Cognitive Communications and Networking
language:
Short-container-title:IEEE Trans. Cogn. Commun. Netw.

Author:

Yang Huigyu¹^ORCID,Raza Syed M.²^ORCID,Kim Moonseong³^ORCID,Choo Hyunseung³^ORCID

Affiliation:

1. Department of Superintelligence Engineering, Sungkyunkwan University, Suwon, South Korea

2. Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea

3. Department of IT Convergence Software, Seoul Theological University, Bucheon, South Korea

Funder

Ministry of Education, Institute of Information and Communications Technology Planning and Evaluation (IITP), and the National Research Foundation (NRF), Korea, under the GITRC support program

ICT Creative Consilience program

High-Potential Individuals Global Training Program

mid-career support program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/6687307/10146027/10036938.pdf?arnumber=10036938

Reference53 articles.

1. Mobility Prediction: A Survey on State-of-the-Art Schemes and Future Applications

2. Exploring Trajectory Prediction Through Machine Learning Methods

3. A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series

4. Comparison of SOM and conventional neural network data division for PV reliability power prediction

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