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Bulunan: 5 Adet 0.001 sn
- Eklemek veya çıkarmak istediğiniz kriterleriniz için 'Dahil' / 'Hariç' seçeneğini kullanabilirsiniz. Sorgu satırları birbirine 'VE' bağlacı ile bağlıdır.
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Filtreler
Filtreler
Bulunan: 5 Adet 0.001 sn
Yayın Dili : en ✕Konu Başlıkları : Deep Learning ✕
Koleksiyon [5]
İlgili Araştırmacılar [3]
Ambargo Durumu [1]
Tam Metin [1]
Yayın Türü [2]
Yazar/lar [13]
Atıf Dizini [3]
Konu Başlıkları [20]
Yayıncı [3]
Yayın Tarihi [5]
Yayın Dili [1]
Erişime Açık

A Multistage Deep Learning Algorithm for Detecting Arrhythmia

ALLAHVERDİ, Novruz | KUTLU, Yakup | ALTAN, Gökhan

Deep Belief Networks (DBN) is a deep learning algorithm that has both greedy layer-wise unsupervised and supervised training. Arrhythmia is a cardiac irregularity caused by a problem of the heart. In this study, a multi-stage DBN classification is proposed for achieving the efficiency of the DBN on arrhythmia disorders. Heartbeats from the MITBIH Arrhythmia database are classified into five groups which are recommended by AAMI. The Wavelet packet decomposition, higher order statistics, morphology and Discrete Fourier transform techniques were utilized to extract features. The classification pe ...Daha fazlası

Konferans Bildirisi 2017 IEEE 6 Görüntülenme
Erişime Açık

Deep Learning for COPD Analysis Using Lung Sounds

ALLAHVERDİ, Novruz | ALTAN, Gökhan | KUTLU, Yakup

Deep Learning(DL) algotithms have become popular with the detailed analyzing capabilities with many hidden layers in recent years. The size of hidden layer in the classifier models is complately correlated with the analyzing capability of the proposed mode. Multiple hidden layers and neuron size in the hidden layers enhance the analyzing capability of the models,whereas increasing the training time.

Konferans Bildirisi 2018 3 Görüntülenme
Erişime Açık

Short-Term Prediction of PM2. 5 Pollution with Deep Learning Methods

AYTURAN, Yasin Akın | AYTURAN, Zeynep Cansu | ALTUN, Hüseyin Oktay | KONGOLİ, Cezar | TUNÇEZ, Fatma Didem | DURSUN, Şükrü | ÖZTÜRK, Ali

Particulate matter (PM), classified according to aerodynamic diameter, is one of the harmful pollutants causing health damaging effects. It is considered as cancerogenic by the World Health Organization (WHO) because of the substances found in the chemical composition of PM. In this study, short-term prediction of PM2.5 pollution at 1, 2 and 3 hours was modelled using deep learning methods. Three deep learning algorithms and the combination thereof were evaluated: Long-short term memory units (LSTM), recurrent neural networks (RNN) and gated recurrent unit (GRU). Air Quality Monitoring Station ...Daha fazlası

Makale 2020 Global Nest Journal-GLOBAL NETWORK ENVIRONMENTAL SCIENCE & TECHNOLOGY 11 Görüntülenme
Erişime Açık

A Multistage Deep Belief Networks Application on Arrhythmia Classification

ALLAHVERDİ, Novruz | ALTAN, Gökhan | KUTLU, Yakup

An electrocardiogram (ECG) is a biomedical signal type that determines the normality and abnormality of heart beats using the electrical activity of the heart and has a great importance for cardiac disorders. The computer-aided analysis of biomedical signals has become a fabulous utilization method over the last years. This study introduces a multistage deep learning classification model for automatic arrhythmia classification. The proposed model includes a multi-stage classification system that uses ECG waveforms and the Second Order Difference Plot (SODP) features using a Deep Belief Network ...Daha fazlası

Makale 2016 4 Görüntülenme
Erişime Açık

Health status detection of neonates using infrared thermography and deep convolutional neural networks

ÖRNEK, Ahmet Haydar | CEYLAN, Murat | ERVURAL, Saim

Protection of body temperature is critically important for health. Diseases and infections cause local temperature imbalances in the body. Infrared Thermography (IRT), which is a non-invasive and non-contact method, has been used in medical applications for decades. Pre-diagnosis and follow-up treatment systems can be realized by monitoring the temperature distribution in the body. In this study, IRT and deep Convolutional Neural Networks (CNNs) models were used together for the first time to detect the health status of neonates. Neonatal thermal images have been taken in the Neonatal Intensiv ...Daha fazlası

Makale 2019 Elsevier B.V. 1 Görüntülenme
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