Machine Learning Enhanced Channel Selection for Unlicensed LTE

2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) ◽

10.1109/dyspan.2019.8935859 ◽

2019 ◽

Author(s):

Matthew Tonnemacher ◽

Chance Tarver ◽

Joseph Cavallar ◽

Joseph Camp

Keyword(s):

Machine Learning ◽

Channel Selection ◽

Selection For

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Machine Learning based Side Channel Selection for Time-Driven Cache Attacks on AES

2019 4th International Conference on Computer Science and Engineering (UBMK) ◽

10.1109/ubmk.2019.8907211 ◽

2019 ◽

Author(s):

Burcu Sonmez ◽

Ahmet Ali Sarikaya ◽

Serif Bahtiyar

Keyword(s):

Machine Learning ◽

Channel Selection ◽

Side Channel ◽

Selection For ◽

Cache Attacks

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Channel selection for epilepsy seizure prediction method based on machine learning

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/embc.2012.6347156 ◽

2012 ◽

Author(s):

Nai-Fu Chang ◽

Tung-Chien Chen ◽

Cheng-Yi Chiang ◽

Liang-Gee Chen

Keyword(s):

Machine Learning ◽

Prediction Method ◽

Channel Selection ◽

Seizure Prediction ◽

Selection For

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Optimal sensor channel selection for resource-efficient deep activity recognition

Proceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021) ◽

10.1145/3412382.3458278 ◽

2021 ◽

Author(s):

Clayton Frederick Souza Leite ◽

Yu Xiao

Keyword(s):

Activity Recognition ◽

Channel Selection ◽

Sensor Channel ◽

Selection For ◽

Deep Activity

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Optimal feature selection for machine learning based intrusion detection system by exploiting attribute dependence

Materials Today Proceedings ◽

10.1016/j.matpr.2021.04.643 ◽

2021 ◽

Author(s):

Ghanshyam Prasad Dubey ◽

Dr. Rakesh Kumar Bhujade

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Optimal Feature Selection ◽

Selection For ◽

Optimal Feature

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Feature Selection for Unsupervised Machine Learning of Accelerometer Data Physical Activity Clusters – A Systematic Review

Gait & Posture ◽

10.1016/j.gaitpost.2021.08.007 ◽

2021 ◽

Author(s):

Petra J. Jones ◽

Mike Catt ◽

Melanie J. Davies ◽

Charlotte L. Edwardson ◽

Evgeny M. Mirkes ◽

...

Keyword(s):

Physical Activity ◽

Machine Learning ◽

Systematic Review ◽

Feature Selection ◽

Accelerometer Data ◽

Unsupervised Machine Learning ◽

Selection For

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Optimum machine learning algorithm selection for forecasting vegetation indices: MODIS NDVI & EVI

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2021.100582 ◽

2021 ◽

Vol 23 ◽

pp. 100582

Author(s):

Bishal Roy

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Vegetation Indices ◽

Machine Learning Algorithm ◽

Algorithm Selection ◽

Modis Ndvi ◽

Selection For

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Q-Learning Based Adaptive Channel Selection for Underwater Sensor Networks

2018 Fourth Underwater Communications and Networking Conference (UComms) ◽

10.1109/ucomms.2018.8493219 ◽

2018 ◽

Author(s):

Antony Pottier ◽

Paul D. Mitchell ◽

Francois-Xavier Socheleau ◽

Christophe Laot

Keyword(s):

Sensor Networks ◽

Channel Selection ◽

Underwater Sensor Networks ◽

Q Learning ◽

Selection For

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Channel Selection for Spectrum Sharing in Wireless Networks

ETRI Journal ◽

10.4218/etrij.16.2616.0007 ◽

2016 ◽

Vol 38 (5) ◽

pp. 952-961

Author(s):

Jae Cheol Park ◽

Kyu-Min Kang ◽

Seungkeun Park

Keyword(s):

Wireless Networks ◽

Spectrum Sharing ◽

Channel Selection ◽

Selection For

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Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory

The 2010 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2010.5596450 ◽

2010 ◽

Cited By ~ 19

Author(s):

Davide Anguita ◽

Alessandro Ghio ◽

Noemi Greco ◽

Luca Oneto ◽

Sandro Ridella

Keyword(s):

Machine Learning ◽

Support Vector Machines ◽

Model Selection ◽

Learning Theory ◽

Support Vector ◽

Advantages And Disadvantages ◽

Vector Machines ◽

Selection For

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Adaptive Frequency Band and Channel Selection for Simultaneous Receiving and Sending in Multiband Communication

IEEE Wireless Communications Letters ◽

10.1109/lwc.2018.2876258 ◽

2019 ◽

Vol 8 (2) ◽

pp. 460-463 ◽

Cited By ~ 4

Author(s):

Ayaka Hanyu ◽

Yuichi Kawamoto ◽

Hiroki Nishiyama ◽

Nei Kato ◽

Naoto Egashira ◽

...

Keyword(s):

Frequency Band ◽

Channel Selection ◽

Selection For

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