A Hybrid Deep Learning-Based (HYDRA) Framework for Multifault Diagnosis Using Sparse MDT Reports-Reference-Cited by-同舟云学术

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A Hybrid Deep Learning-Based (HYDRA) Framework for Multifault Diagnosis Using Sparse MDT Reports

Published:2022 Issue: Volume:10 Page:67140-67151
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Riaz Muhammad Sajid¹^ORCID,Qureshi Haneya Naeem¹^ORCID,Masood Usama¹^ORCID,Rizwan Ali²^ORCID,Abu-Dayya Adnan³^ORCID,Imran Ali¹^ORCID

Affiliation:

1. AI4Networks Research Center, School of Electrical and Computer Engineering, The University of Oklahoma, Tulsa, OK, USA

2. Qatar Mobility Innovations Center, Qatar University, Doha, Qatar

3. Department of Electrical Engineering, Qatar University, Doha, Qatar

Funder

National Science Foundation

Qatar National Research Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

https://ieeexplore.ieee.org/ielam/6287639/9668973/9804486-aam.pdf

Reference47 articles.

1. ConvXGB: A new deep learning model for classification problems based on CNN and XGBoost

2. Comparative Analysis of Artificial Neural Network and XGBoost Algorithm for PolSAR Image Classification

3. An Image Inpainting Technique Based on the Fast Marching Method

4. Fixed Rank Kriging for Cellular Coverage Analysis

5. Construction of the RSRP map using sparse MDT measurements by regression clustering

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2. iPREDICT: AI enabled proactive pandemic prediction using biosensing wearable devices;Informatics in Medicine Unlocked;2024

3. AI-Assisted Joint Search Approach for mmwave Cell Discovery Using Sparse MDT Data;IEEE Transactions on Vehicular Technology;2023-12

4. Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management;Algorithms;2022-11-17

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