Hybrid Machine Learning Attack for Feed-Forward Arbiter PUF and Its Evaluation

2019 ◽  
Vol 139 (6) ◽  
pp. 692-700
Author(s):  
Yusuke Nozaki ◽  
Masaya Yoshikawa
Keyword(s):  
Machine Learning ◽  
Feed Forward ◽  
Arbiter Puf ◽  
Hybrid Machine

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

scholarly journals Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1241
Author(s):  
Ming-Hsi Lee ◽  
Yenming J. Chen
Keyword(s):  
Machine Learning ◽  
Markov Chain ◽  
Random Field ◽  
Long Range ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Prediction Algorithm ◽  
Learning Method ◽  
Multi Scale ◽  
Hybrid Machine

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.

Design of coaxial coils using hybrid machine learning

2021 ◽  
Vol 92 (4) ◽  
pp. 045103
Author(s):  
Jun Chen ◽  
Zeliang Wu ◽  
Guzhi Bao ◽  
L. Q. Chen ◽  
Weiping Zhang
Keyword(s):  
Machine Learning ◽  
Hybrid Machine

scholarly journals An Efficient Hybrid Machine Learning Classifier for Rainfall Prediction

2021 ◽  
Vol 1770 (1) ◽  
pp. 012012
Author(s):  
P. Asha ◽  
A. Jesudoss ◽  
S. Prince Mary ◽  
K. V. Sai Sandeep ◽  
K. Harsha Vardhan
Keyword(s):  
Machine Learning ◽  
Rainfall Prediction ◽  
Learning Classifier ◽  
Hybrid Machine
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