scholarly journals Utilizing ensemble learning for performance and power modeling and improvement of parallel cancer deep learning CANDLE benchmarks

2021 ◽  
Author(s):  
Xingfu Wu ◽  
Valerie Taylor
Keyword(s):  
Deep Learning ◽  
Ensemble Learning ◽  
Power Modeling

scholarly journals Machine Learning-Based Malicious X.509 Certificates’ Detection

2021 ◽  
Vol 11 (5) ◽  
pp. 2164
Author(s):  
Jiaxin Li ◽  
Zhaoxin Zhang ◽  
Changyong Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ensemble Learning ◽  
Traffic Analysis ◽  
Learning Models ◽  
Detection Model ◽  
Analysis Tools ◽  
Average Accuracy ◽  
Machine Learning Models

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.

A hybrid deep learning and ensemble learning mechanism for damaged power line detection in smart grids

2021 ◽  
Author(s):  
Yangyang Tian ◽  
Qi Wang ◽  
Zhimin Guo ◽  
Huitong Zhao ◽  
Sulaiman Khan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ensemble Learning ◽  
Smart Grids ◽  
Power Line ◽  
Line Detection ◽  
Learning Mechanism

Identifying High Risk of Atherosclerosis Using Deep Learning and Ensemble Learning

2021 ◽  
Author(s):  
Hedieh Hashem Olhosseiny ◽  
Mohammadsalar Mirzaloo ◽  
Miodrag Bolic ◽  
Hilmi R. Dajani ◽  
Voicu Groza ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Risk ◽  
Ensemble Learning

scholarly journals Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 83 ◽  
Author(s):  
Giannis Haralabopoulos ◽  
Ioannis Anagnostopoulos ◽  
Derek McAuley
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Ensemble Learning ◽  
Affective Computing ◽  
Binary Classification ◽  
User Generated Content ◽  
Emotional Information ◽  
Multiple Classifiers ◽  
Different Levels

Sentiment analysis usually refers to the analysis of human-generated content via a polarity filter. Affective computing deals with the exact emotions conveyed through information. Emotional information most frequently cannot be accurately described by a single emotion class. Multilabel classifiers can categorize human-generated content in multiple emotional classes. Ensemble learning can improve the statistical, computational and representation aspects of such classifiers. We present a baseline stacked ensemble and propose a weighted ensemble. Our proposed weighted ensemble can use multiple classifiers to improve classification results without hyperparameter tuning or data overfitting. We evaluate our ensemble models with two datasets. The first dataset is from Semeval2018-Task 1 and contains almost 7000 Tweets, labeled with 11 sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments, labeled with six different levels of abuse or harassment. Our results suggest that ensemble learning improves classification results by 1.5 % to 5.4 % .

Ensemble learning versus deep learning for Hypoxia detection in CTG signal

2019 ◽  
Author(s):  
P Riskyana Dewi Intan ◽  
M. Anwar Ma'sum ◽  
Noverina Alfiany ◽  
Wisnu Jatmiko ◽  
Aria Kekalih ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ensemble Learning

scholarly journals Efficient Regularization Framework for Histopathological Image Classification Using Convolutional Neural Networks.

2020 ◽  
Vol 14 (4) ◽  
pp. 62-81
Author(s):  
Nassima Dif ◽  
Zakaria Elberrichi
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Ensemble Learning ◽  
Data Augmentation ◽  
Data Representation ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Histopathological Image ◽  
Histopathological Image Classification ◽  
Small Models

Deep learning methods are characterized by their capacity to learn data representation compared to the traditional machine learning algorithms. However, these methods are prone to overfitting on small volumes of data. The objective of this research is to overcome this limitation by improving the generalization in the proposed deep learning framework based on various techniques: data augmentation, small models, optimizer selection, and ensemble learning. For ensembling, the authors used selected models from different checkpoints and both voting and unweighted average methods for combination. The experimental study on the lymphomas histopathological dataset highlights the efficiency of the MobileNet2 network combined with the stochastic gradient descent (SGD) optimizer in terms of generalization. The best results have been achieved by the combination of the best three checkpoint models (98.67% of accuracy). These findings provide important insights into the efficiency of the checkpoint ensemble learning method for histopathological image classification.

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