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Scopus YÖKSİS DOI Eşleşti SJR Q1

CNN-based bi-directional and directional long-short term memory network for determination of face mask

Biomedical Signal Processing and Control · Ocak 2022

YÖKSİS DOI Eşleşmesi Bulundu

Bu Scopus makalesi YÖKSİS veritabanında da kayıtlı. Aşağıda YÖKSİS verilerini görebilirsiniz.

YÖKSİS Kayıtları
CNN-based Bi-directional and Directional Long-short Term Memory Network for Determination of Face Mask
Biomedical Signal Processing and Control · 2022 SCI-Expanded
Dr. Öğr. Üyesi İLKAY ÇINAR →
CNN-based Bi-directional and Directional Long-short Term Memory Network for Determination of Face Mask
Biomedical Signal Processing and Control · 2022 SCI-Expanded
Doç. Dr. YAVUZ SELİM TAŞPINAR →
CNN-based Bi-directional and Directional Long-short Term Memory Network for Determination of Face Mask
Biomedical Signal Processing and Control · 2022 SCI-Expanded
Doç. Dr. MURAT KÖKLÜ →
CNN-based bi-directional and directional long-short term memory network for determination of face mask
Biomedical Signal Processing and Control · 2021 SCI-Expanded
Doç. Dr. MURAT KÖKLÜ →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 12 kaydı bulundu.

YÖKSİS Kayıtları — ISSN Eşleşmesi
CNN-based Bi-directional and Directional Long-short Term Memory Network for Determination of Face Mask
2022 ISSN: 1746-8094 SCI-Expanded Q2
Doç. Dr. YAVUZ SELİM TAŞPINAR →
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
2022 ISSN: 1746-8094 SCI-Expanded Q2
Dr. Öğr. Üyesi GÜZİN ÖZMEN →
Improving efficiency in convolutional neural networks with 3D image filters
2022 ISSN: 1746-8094 SCI-Expanded Q2
Dr. Öğr. Üyesi NEJAT ÜNLÜKAL →
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
2022 ISSN: 1746-8094 SCI-Expanded Q2
Doç. Dr. ADEM GÖLCÜK →
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
2022 ISSN: 1746-8094 SCI-Expanded Q2
Prof. Dr. ŞAKİR TAŞDEMİR →
Design and implementation of a hybrid FLC + PID controller for pressure control of sleep devices
2022 ISSN: 1746-8094 SCI-Expanded Q2
Doç. Dr. ADEM GÖLCÜK →
Future activity prediction of multiple sclerosis with 3D MRI using 3D discrete wavelet transform
2022 ISSN: 1746-8094 SCI-Expanded Q2
Dr. Öğr. Üyesi ZÜLEYHA YILMAZ ACAR →
Improving efficiency in convolutional neural networks with 3D image filters
2022 ISSN: 1746-8094 SCI Q2
Prof. Dr. ŞAKİR TAŞDEMİR →
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
2022 ISSN: 1746-8094 SCI
Prof. Dr. ŞAKİR TAŞDEMİR →
CNN-based Bi-directional and Directional Long-short Term Memory Network for Determination of Face Mask
2022 ISSN: 1746-8094 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
Involution-based HarmonyNet: An efficient hyperspectral imaging model for automatic detection of neonatal health status
2024 ISSN: 1746-8094 SCI-Expanded Q1
Doç. Dr. MURAT KONAK →
Abc-based weighted voting deep ensemble learning model for multiple eye disease detection
2024 ISSN: 1746-8094 SCI-Expanded Q1
Prof. Dr. ŞAKİR TAŞDEMİR →

Makale Bilgileri

Dergi Biomedical Signal Processing and Control
ISSN17468094
Yayın TarihiOcak 2022
Cilt / Sayfa71
DOI10.1016/j.bspc.2021.103216
Scopus ID2-s2.0-85120179361
Özet Context: The COVID-19 virus, exactly like in numerous other diseases, can be contaminated from person to person by inhalation. In order to prevent the spread of this virus, which led to a pandemic around the world, a series of rules have been set by governments that people must follow. The obligation to use face masks, especially in public spaces, is one of these rules. Objective: The aim of this study is to determine whether people are wearing the face mask correctly by using deep learning methods. Methods: A dataset consisting of 2000 images was created. In the dataset, images of a person from three different angles were collected in four classes, which are “masked”, “non-masked”, “masked but nose open”, and “masked but under the chin”. Using this data, new models are proposed by transferring the learning through AlexNet and VGG16, which are the Convolutional Neural network architectures. Classification layers of these models were removed and, Long-Short Term Memory and Bi-directional Long-Short Term Memory architectures were added instead. Result and conclusions: Although there are four different classes to determine whether the face masks are used correctly, in the six models proposed, high success rates have been achieved. Among all models, the TrVGG16 + BiLSTM model has achieved the highest classification accuracy with 95.67%. Significance: The study has proven that it can take advantage of the proposed models in conjunction with transfer learning to ensure the proper and effective use of the face mask, considering the benefit of society.

Yazarlar (3)

1
Murat Koklu
ORCID: 0000-0002-2737-2360
2
Ilkay Cinar
ORCID: 0000-0003-0611-3316
3
Yavuz Selim Taspinar
ORCID: 0000-0002-7278-4241

Anahtar Kelimeler

AlexNet BiLSTM Convolutional neural network LSTM Transfer learning VGG16

Kurumlar

Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Biomedical Signal Processing and Control
Q1
SJR Skoru1,229
H-Index125
YayıncıElsevier Ltd
ÜlkeUnited Kingdom
Biomedical Engineering (Q1)
Health Informatics (Q1)
Signal Processing (Q1)
Dergi sayfasına git

Metrikler

53
Atıf
3
Yazar
6
Anahtar Kelime

Sistemimizdeki Yazarlar

YT
YAVUZ SELİM TAŞPINAR
Doç. Dr.

Hızlı Erişim

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