iELMNet: Integrating Novel Improved Extreme Learning Machine and Convolutional Neural Network Model for Traffic Sign Detection-Reference-Cited by-同舟云学术

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iELMNet: Integrating Novel Improved Extreme Learning Machine and Convolutional Neural Network Model for Traffic Sign Detection

Published:2022-01-06 Issue: Volume: Page:
ISSN:2167-6461
Container-title:Big Data
language:en
Short-container-title:Big Data

Author:

Batool Aisha¹^ORCID,Nisar Muhammad Wasif¹,Shah Jamal Hussain¹,Khan Muhammad Attique²^ORCID,El-Latif Ahmed A. Abd³^ORCID

Affiliation:

1. Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan.

2. Department of Computer Science, HITEC University Taxila, Taxila, Pakistan.

3. Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom, Egypt.

Publisher

Mary Ann Liebert Inc

Subject

Information Systems and Management,Computer Science Applications,Information Systems

Link

https://www.liebertpub.com/doi/pdf/10.1089/big.2021.0279

Reference50 articles.

1. A Shallow Network with Combined Pooling for Fast Traffic Sign Recognition

2. Pedestrian Detection Based on Light-Weighted Separable Convolution for Advanced Driver Assistance Systems

3. Deep Learning Modalities for Biometric Alteration Detection in 5G Networks-Based Secure Smart Cities

4. A biometric cryptosystem scheme based on random projection and neural network

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1. Deep Learning Technique to Analyze and Perceive Traffic Sign in the Intelligent Transport System;Information and Communication Technology for Competitive Strategies (ICTCS 2022);2023

2. A Deep Learning Approach Towards Building Intelligent Transport System;2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA);2022-09-08

3. Analytical study of two feature extraction methods in comparison with deep learning methods for classification of small metal objects;Visual Computing for Industry, Biomedicine, and Art;2022-05-10

4. Image Retrieval via Canonical Correlation Analysis and Binary Hypothesis Testing;Information;2022-02-23

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