Performance Comparison of Machine Learning Techniques for Epilepsy Classification and Detection in EEG Signal

2019 ◽  
pp. 425-438 ◽  
Author(s):  
Rekh Ram Janghel ◽  
Archana Verma ◽  
Yogesh Kumar Rathore
Keyword(s):  
Machine Learning ◽  
Performance Comparison ◽  
Machine Learning Techniques ◽  
Eeg Signal ◽  
Learning Techniques ◽  
Epilepsy Classification

scholarly journals Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2509 ◽  
Author(s):  
Kamran Shaukat ◽  
Suhuai Luo ◽  
Vijay Varadharajan ◽  
Ibrahim A. Hameed ◽  
Shan Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Comparison ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Modern World ◽  
The Internet ◽  
Cyber Threats ◽  
Risk Detection ◽  
Learning Techniques ◽  
Made In

Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

scholarly journals A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1216
Author(s):  
Alberto Guillén ◽  
José Martínez ◽  
Juan Miguel Carceller ◽  
Luis Javier Herrera
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Comparative Analysis ◽  
Performance Comparison ◽  
Machine Learning Techniques ◽  
Model Complexity ◽  
Air Showers ◽  
Particle Detectors ◽  
Learning Techniques ◽  
Selection Stage

The main goal of this work is to adapt a Physics problem to the Machine Learning (ML) domain and to compare several techniques to solve it. The problem consists of how to perform muon count from the signal registered by particle detectors which record a mix of electromagnetic and muonic signals. Finding a good solution could be a building block on future experiments. After proposing an approach to solve the problem, the experiments show a performance comparison of some popular ML models using two different hadronic models for the test data. The results show that the problem is suitable to be solved using ML as well as how critical the feature selection stage is regarding precision and model complexity.

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