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Toplam kayıt 42, listelenen: 11-20
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 ...
How to engage consumers through effective social media use-guidelines for consumer goods companies from an emerging market
(Universidad de Talca, 2021)
This study aims to establish actionable guidelines and provide strategic insights as a means of increasing the social media effectiveness of consumer brands. Post-related factors in addition to the contextual and temporal ...
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 ...
A machine learning approach to predict creatine kinase test results
(Ital Publication, 2020)
Most of the research done in the literature are based on statistical approaches and used for deriving reference limits based on lab results. As more data are available to the researchers, ML methods are more effectively ...
Data analysis in health and big data: A machine learning medical diagnosis model based on patients’ complaints
(Taylor and Francis Inc., 2021)
The emergence of big data made it possible to make better predictions and discover hidden patterns which contain a load of useful information. Like other domains, health discipline is also enjoying this new data science ...
A review of computational drug repositioning: Strategies, approaches, opportunities, challenges, and directions
(BMC, 2020)
Drug repositioning is the process of identifying novel therapeutic potentials for existing drugs and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an important role in optimizing the ...
An early warning system using machine learning for the detection of intracranial hematomas in the emergency trauma setting
(Turkish Neurosurgical Society, 2022)
AIM: To present an early warning system (EWS) that employs a supervised machine learning algorithm for the rapid detection of extra-axial hematomas (EAHs) in an emergency trauma setting. MATERIAL and METHODS: A total of ...
Primary user emulation and jamming attack detection in cognitive radio via sparse coding
(Springer, 2020)
Cognitive radio is an intelligent and adaptive radio that improves the utilization of the spectrum by its opportunistic sharing. However, it is inherently vulnerable to primary user emulation and jamming attacks that degrade ...
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 ...