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Toplam kayıt 18, listelenen: 1-10
Compressed spectrum sensing using sparse recovery convergence patterns through machine learning classification
(Institute of Electrical and Electronics Engineers Inc., 2019)
Despite the well-known success of sub-Nyquist sampling in reducing the hardware and computational costs of spectrum sensing, it still has the shortcoming of requiring a pre-determined spectrum sparsity level. This paper ...
Selection of waveform parameters using machine learning for 5G and beyond
(Institute of Electrical and Electronics Engineers Inc., 2019)
Flexibility is one of the essential requirements for future cellular communications technologies. Providing customized communications solutions for each user and service type cannot be possible without the flexibility in ...
Advances in quantitative analysis of astrocytes using machine learning
(Wolters Kluwer Medknow Publications, 2023)
Astrocytes, a subtype of glial cells, are starshaped cells that are involved in the homeostasis and blood flow control of the central nervous system (CNS). They are known to provide structural and functional support to ...
Predicting the effects of repetitive transcranial magnetic stimulation on cognitive functions in patients with alzheimer's disease by automated EEG analysis
(Frontiers Media SA, 2022)
Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that generally affects the elderly. Today, after the limited benefit of the pharmacological treatment strategies, numerous noninvasive brain ...
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 ...
Fuzzy classification methods based diagnosis of Parkinson’s disease from speech test cases
(Bentham Science Publishers, 2019)
Background: Together with the Alzheimer’s disease, Parkinson’s disease is considered as one of the two serious known neurodegenerative diseases. Physicians find it hard to predict whether a given patient has already developed ...
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 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 ...
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 ...