scholarly journals Robust Classification of High-Dimensional Spectroscopy Data Using Deep Learning and Data Synthesis

2020 ◽  
Vol 60 (4) ◽  
pp. 1936-1954 ◽  
Author(s):  
James Houston ◽  
Frank G. Glavin ◽  
Michael G. Madden
Keyword(s):  
Deep Learning ◽  
High Dimensional ◽  
Spectroscopy Data ◽  
Data Synthesis ◽  
Robust Classification

scholarly journals Iot Device Classification in Cloud Organizations Using Deep Belief Networks

2021 ◽  
Author(s):  
H. Azath H ◽  
M. NAGESWARA GUPTHA M ◽  
L. SHAKKEERA L ◽  
M.R.M. VEERA MANICKAM M.R.M ◽  
B. LANITHA B ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection Rate ◽  
High Dimensional ◽  
Deep Belief Networks ◽  
Malicious Nodes ◽  
Learning Classifier ◽  
Device Classification ◽  
Iot Devices ◽  
Computational Resources

Abstract With the rapid increase in the usage of IoT devices, the cyber threats are increasing among the communication between the IoT devices. The challenges related to security surmounts with increasing number of IoT devices due to its functionality and heterogeneity. In recent times, deep learning algorithms are offered to resolve the constraints associated with detection of malicious devices among the networks. In this paper, we utilize deep belief network (DBN) to resolve the problems associated with identification, detection of anomaly IoT devices. Several features are extracted initially to find the malicious devices in the IoT device network that includes storage, computational resources and high dimensional features. These features extracted from the network traffic assists in achieving the classification of devices by DBN. The simulation is performed to test the accuracy and detection rate of the proposed deep learning classifier. The results show that the proposed method is effective in implementing the detection of malicious nodes in the network than existing methods.

scholarly journals Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data

2010 ◽  
Vol 24 (11-12) ◽  
pp. 719-727 ◽  
Author(s):  
Julien Jacques ◽  
Charles Bouveyron ◽  
Stéphane Girard ◽  
Olivier Devos ◽  
Ludovic Duponchel ◽  
...  
Keyword(s):  
Mixture Models ◽  
Vibrational Spectroscopy ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Spectroscopy Data

Application of Deep Learning for EEG

2020 ◽  
pp. 106-123
Author(s):  
Angana Saikia ◽  
Sudip Paul
Keyword(s):  
Deep Learning ◽  
Extraction Methods ◽  
Physiological Signals ◽  
High Dimensional ◽  
Support Vector ◽  
Learning Techniques ◽  
Eeg Data ◽  
Learning Architectures ◽  
Subject Differences

Deep learning is a relatively new branch of machine learning, which has been used in a variety of biomedical applications. It has been used to analyze different physiological signals and gain better understanding of human physiology for automated diagnosis of abnormal conditions. It is used in the classification of electroencephalography signals. Most of the present research has continued to use manual feature extraction methods followed by a traditional classifier, such as support vector machine or logistic regression. This is largely due to the low number of samples per experiment, high-dimensional nature of the data, and the difficulty in finding appropriate deep learning architectures for classification of EEG signals. One of the challenges in modeling cognitive events from EEG data is finding representations that are invariant to inter- and intra-subject differences as well as the inherent noise associated with EEG data collection. Herein, the authors explore the capabilities of the recent deep learning techniques for modeling cognitive events from EEG data.

Classification of Customer to Upgrade Profits in Retail Market with Deep Learning Methodology

2019 ◽  
Vol 7 (5) ◽  
pp. 188-191
Author(s):  
I.Gayathri Devi ◽  
G. Surya Kala Eswari ◽  
G. Kumari
Keyword(s):  
Deep Learning ◽  
Retail Market
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