Konu "Deep learning" için Araştırma Çıktıları | Web of Science İndeksli Yayınlar Koleksiyonu listeleme
Toplam kayıt 34, listelenen: 1-20
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Automated detection of Covid-19 disease using deep fused features from chest radiography images
(Elsevier, 2021)The health systems of many countries are desperate in the face of Covid-19, which has become a pandemic worldwide and caused the death of hundreds of thousands of people. In order to keep Covid-19, which has a very high ... -
Automatic segmentation of COVID-19 from computed tomography images using modified U-Net model-based majority voting approach
(Springer, 2022)The coronavirus disease (COVID-19) is an important public health problem that has spread rapidly around the world and has caused the death of millions of people. Therefore, studies to determine the factors affecting the ... -
Breast cancer diagnosis using deep belief networks on ROI images
(Pamukkale Üniversitesi, 2022)Hand-crafted features are efficient methods for image processing, recognition, and computer vision. However, the advancements in data size and image resolution lead to inconvenience in feature extraction. Moreover, they ... -
Chronic obstructive pulmonary disease severity analysis using deep learning on multi-channel lung sounds
(Türkiye Klinikleri, 2020)Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases which cannot be treated but can be kept under control in certain stages. COPD has five severities, including at-risk, mild, moderate, severe, ... -
Classification of Different Tympanic Membrane Conditions Using Fused Deep Hypercolumn Features and Bidirectional LSTM
(Elsevier, 2022)Objectives: Middle ear inflammatory diseases are global health problem that can have serious consequences such as hearing loss and speech disorders. The high cost of medical devices such as otoendoscope and oto-microscope ... -
Classification of myositis from muscle ultrasound images using deep learning
(Elsevier, 2022)Inflammatory myopathies, are rare muscle diseases. As a result of the body's own immune system attacking by targeting the muscle cells, muscle weakness develops due to inflammation in the muscles. Early and definitive ... -
A comparative study of estimating solar radiation using machine learning approaches: DL, SMGRT, and ANFIS
(Taylor and Francis Ltd., 2022)Solar energy has a key role in producing clean and emissions-free power compare to conventional methods. However, sustainable development also requires a reliable and predictable energy source. It also needs methods to ... -
Comparison of different techniques for estimation of incoming longwave radiation
(Springer, 2020)Global warming and climate change have left developing countries fragile in terms of agricultural production, and this vulnerability is expected to increase in the near future. The surface energy budget approach is a ... -
CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations
(Oxford Academic, 2021)Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of ... -
Data driven surrogate modeling of horn antennas for optimal determination of radiation pattern and size using deep learning
(Wiley, 2023)Horn antenna designs are favored in many applications where ultra-wide-band operation range alongside of a high-performance radiation pattern characteristics are requested. Scattering-parameter characteristics of antennas ... -
Data-Driven Surrogate-Assisted Optimization of Metamaterial-Based Filtenna Using Deep Learning
(MDPI, 2023)In this work, a computationally efficient method based on data-driven surrogate models is proposed for the design optimization procedure of a Frequency Selective Surface (FSS)-based filtering antenna (Filtenna). A Filtenna ... -
Deep Convolutional Generalized Classifier Neural Network
(Springer, 2020)Up to date technological implementations of deep convolutional neural networks are at the forefront of many issues, such as autonomous device control, effective image and pattern recognition solutions. Deep neural networks ... -
Deep Learning for COPD Analysis Using Lung Sounds
(Baku State University, 2018)In this study, Hilbert-Huang Transform (HHT) was applied to the lung sounds from RespiratoryDatabase@TR and the statistical features were calculated from the different modulations of the HHT. The statistical features were ... -
Deep Learning on Computerized Analysis of Chronic Obstructive Pulmonary Disease
(Institute of Electrical and Electronics Engineers Inc., 2020)Goal: Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases in the world. Because COPD is an incurable disease and requires considerable time to be diagnosed even by an experienced specialist, it ... -
Deep learning with 3D-second order difference plot on respiratory sounds
(Elsevier, 2018)The second order difference plot (SODP) is a nonlinear signal analysis method that visualizes two consecutive data points for many types of biomedical signals. The proposed method is based on analysing quantization of ... -
Deep Learning with ConvNet Predicts Imagery Tasks Through EEG
(Springer, 2021)Deep learning with convolutional neural networks (ConvNets) has dramatically improved the learning capabilities of computer vision applications just through considering raw data without any prior feature extraction. Nowadays, ... -
Deep neural network approach to estimation of power production for an organic Rankine cycle system
(Springer, 2020)In this study, the possibility of using Stepwise multilinear regression and deep learning models to estimate the behaviour of the organic Rankine cycle (ORC) has been investigated. It was found that a number of parameters ... -
DeepOCT: An explainable deep learning architecture to analyze macular edema on OCT images
(Elsevier, 2022)Macular edema (ME) is one of the most common retinal diseases that occur as a result of the detachment of the retinal layers on the macula. This study provides computer-aided identification of ME for even small pathologies ... -
Differential convolutional neural network
(Elsevier, 2019)Convolutional neural networks with strong representation ability of deep structures have ever increasing popularity in many research areas. The main difference of Convolutional Neural Networks with respect to existing ... -
Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques
(Nature Research, 2021)Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and ...