Transfer learning for wireless networks: A comprehensive survey
With outstanding features, machine learning (ML) has become the backbone of numerous
applications in wireless networks. However, the conventional ML approaches face many …
applications in wireless networks. However, the conventional ML approaches face many …
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
Transfer learning for future wireless networks: A comprehensive survey
With outstanding features, Machine Learning (ML) has been the backbone of numerous
applications in wireless networks. However, the conventional ML approaches have been …
applications in wireless networks. However, the conventional ML approaches have been …
Flight delay prediction based on aviation big data and machine learning
Accurate flight delay prediction is fundamental to establish the more efficient airline
business. Recent studies have been focused on applying machine learning methods to …
business. Recent studies have been focused on applying machine learning methods to …
Breast cancer–detection system using PCA, multilayer perceptron, transfer learning, and support vector machine
This study proposed a new processing method to predict breast cancer on the basis of nine
individual attributes, including age, body mass index, glucose, insulin, and a homeostasis …
individual attributes, including age, body mass index, glucose, insulin, and a homeostasis …
Spectrum sensing in cognitive radio: A deep learning based model
Spectrum sensing is an efficient technology for addressing the shortage of spectrum
resources. Widely used methods usually employ model‐based features as the test statistics …
resources. Widely used methods usually employ model‐based features as the test statistics …
Transfer learning for radio frequency machine learning: a taxonomy and survey
Transfer learning is a pervasive technology in computer vision and natural language
processing fields, yielding exponential performance improvements by leveraging prior …
processing fields, yielding exponential performance improvements by leveraging prior …
When machine learning meets spectrum sharing security: Methodologies and challenges
The exponential growth of Internet connected systems has generated numerous challenges,
such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions …
such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions …
Deep learning for spectrum sensing in cognitive radio
The detection of primary user signals is essential for optimum utilization of a spectrum by
secondary users in cognitive radio (CR). The conventional spectrum sensing schemes have …
secondary users in cognitive radio (CR). The conventional spectrum sensing schemes have …
Distributed unsupervised learning for interference management in integrated sensing and communication systems
Nowadays, the multi-access interference problem in the ISAC systems can not be ignored.
The study on interference management in ISAC has been envisioned as one of key …
The study on interference management in ISAC has been envisioned as one of key …