Ara
Toplam kayıt 11, listelenen: 1-10
PL-GAN: Path loss prediction using generative adversarial networks
(IEEE-Institute of Electrical and Electronics Engineers Inc., 2022)
Accurate prediction of path loss is essential for the design and optimization of wireless communication networks. Existing path loss prediction methods typically suffer from the trade-off between accuracy and computational ...
Predicting path loss distribution of an area from satellite ımages using deep learning
(IEEE - Institute of Electrical and Electronics Engineers, Inc., 2020)
Path loss prediction is essential for network planning in any wireless communication system. For cellular networks, it is usually achieved through extensive received signal power measurements in the target area. When the ...
SNF-CVAE: Computational method to predict drug-disease interactions using similarity network fusion and collective variational autoencoder
(Elsevier, 2021)
Drug repositioning is an emerging approach to identify novel therapeutic potentials for approved drugs and discover therapies for previously untreatable diseases. Drug repositioning has also attracted considerable attention ...
Proposing a CNN method for primary and permanent tooth detection and enumeration on pediatric dental radiographs
(NLM (Medline), 2022)
OBJECTIVE: In this paper, we aimed to evaluate the performance of a deep learning system for automated tooth detection and numbering on pediatric panoramic radiographs. STUDY DESIGN: YOLO V4, a CNN (Convolutional Neural ...
An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain
(Wiley-Hindawi, 2019)
Demand forecasting is one of the main issues of supply chains. It aimed to optimize stocks, reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical data can be analyzed to improve demand ...
Regression of large-scale path loss parameters using deep neural networks
(IEEE-Institute of Electrical and Electronics Engineers Inc., 2022)
Path loss exponent and shadowing factor are among important wireless channel parameters. These parameters can be estimated using field measurements or ray-tracing simulations, which are costly and time-consuming. In this ...
Modeling traders' behavior with deep learning and machine learning methods: Evidence from BIST 100 index
(Wiley-Hindawi, 2020)
Although the vast majority of fundamental analysts believe that technical analysts' estimates and technical indicators used in these analyses are unresponsive, recent research has revealed that both professionals and ...
SNF-NN: Computational method to predict drug-disease interactions using similarity network fusion and neural networks
(BioMed Central Ltd., 2021)
Background: Drug repositioning is an emerging approach in pharmaceutical research for identifying novel therapeutic potentials for approved drugs and discover therapies for untreated diseases. Due to its time and cost ...
A comparative study of different pre-trained deeplearning models and custom CNN for pancreatic tumor detection
(Zarka Private University, 2023)
Artificial Intelligence and its sub-branches like MachineLearning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging is briefly making ...
Exploring deep learning for adaptive energy detection threshold determination: A multistage approach
(Multidisciplinary Digital Publishing Institute (MDPI), 2023)
The concept of spectrum sensing has emerged as a fundamental solution to address the growing demand for accessing the limited resources of wireless communications networks. This paper introduces a straightforward yet ...