Konu "Deep neural networks" için Araştırma Çıktıları | Web of Science İndeksli Yayınlar Koleksiyonu listeleme
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Accurate Modeling of Antenna Structures by Means of Domain Confinement and Pyramidal Deep Neural Networks
(Institute of Electrical and Electronics Engineers Inc., 2022)The importance of surrogate modeling techniques has been gradually increasing in the design of antenna structures over the recent years. Perhaps the most important reason is a high cost of full-wave electromagnetic (EM) ... -
Accurate Modeling of Frequency Selective Surfaces Using Fully-Connected Regression Model With Automated Architecture Determination and Parameter Selection Based on Bayesian Optimization
(Institute of Electrical and Electronics Engineers Inc., 2021)Surrogate modeling has become an important tool in the design of high-frequency structures. Although full-wave electromagnetic (EM) simulation tools provide an accurate account for the circuit characteristics and performance, ... -
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 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 ... -
Deep neural network model with Bayesian optimization for tuberculosis detection from X-Ray images
(Springer, 2023)Tuberculosis is a chronic lung disease caused by bacterial infection, and more than 10 million people get this disease every year, especially in developing countries. Early diagnosis of tuberculosis is important for effective ... -
DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations
(Royal Soc Chemistry, 2020)The identification of physical interactions between drug candidate compounds and target biomolecules is an important process in drug discovery. Since conventional screening procedures are expensive and time consuming, ... -
Improved Modeling of Microwave Structures Using Performance-Driven Fully-Connected Regression Surrogate
(Institute of Electrical and Electronics Engineers Inc., 2021)Fast replacement models (or surrogates) have been widely applied in the recent years to accelerate simulation-driven design procedures in microwave engineering. The fundamental reason is a considerable-and often prohibitive-CPU ... -
Performance Comparision of Different Momentum Techniques on Deep Reinforcement Learning
(Institute of Electrical and Electronics Engineers Inc., 2017)Increase in popularity of deep convolutional neural networks in many different areas leads to increase in the use of these networks in reinforcement learning. Training a huge deep neural network structure by using simple ... -
Position resolution study at high energies of a sampling electromagnetic calorimeter whose active material is a scintillator with Peroxide-cured polysiloxane base
(Elsevier, 2020)This study is based on the simulation for the position resolution performances of a sampling electromagnetic calorimeter with a Peroxide-cured polysiloxane based scintillator as an active material. Various algorithms and ... -
Region contrastive camera localization
(Elsevier, 2023)Visual camera localization is a well-studied computer vision problem and has many applications. Recently, deep convolutional neural networks have begun to be utilized to solve six-degree-of-freedom (6-DoF) camera pose ... -
SecureDeepNet-IoT: A deep learning application for invasion detection in industrial Internet of Things sensing systems
(Wiley, 2021)Deep learning (DL) is a special field of artificial intelligence that has increased its use in various fields and has proved its effectiveness in classification. The feasibility of using many hidden layers and many neurons ... -
Transfer learning to detect neonatal seizure from electroencephalography signals
(Springer, 2021)This paper offers a solution to the problem of detecting neonatal seizures via a transfer learning technique that judiciously reconstructs pre-trained deep convolution neural networks (p-DCNN), including alexnet, resnet18, ...