Performance Comparison of Machine Learning Techniques in Identifying Dementia from Open Access Clinical Datasets

2020 ◽  
pp. 79-89 ◽  
Author(s):  
Yunus Miah ◽  
Chowdhury Nazia Enam Prima ◽  
Sharmeen Jahan Seema ◽  
Mufti Mahmud ◽  
M Shamim Kaiser
Keyword(s):  
Machine Learning ◽  
Open Access ◽  
Performance Comparison ◽  
Machine Learning Techniques ◽  
Learning Techniques

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.

scholarly journals A Performance Comparison of Statistical and Machine Learning Techniques in Learning Time Series Data

2015 ◽  
Vol 21 (10) ◽  
pp. 3037-3041 ◽  
Author(s):  
. Haviluddin ◽  
Rayner Alfred ◽  
Joe Henry Obit ◽  
Mohd Hanafi Ahmad Hijazi ◽  
Ag Asri Ag Ibrahim
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Performance Comparison ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Learning Time ◽  
Learning Techniques ◽  
A Performance

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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