Deep Learning for Imbalanced Multimedia Data Classification

2015 ◽  
Author(s):  
Yilin Yan ◽  
Min Chen ◽  
Mei-Ling Shyu ◽  
Shu-Ching Chen
Keyword(s):  
Deep Learning ◽  
Data Classification ◽  
Multimedia Data

Feature extraction-based image steganalysis using deep learning

2021 ◽  
pp. 188-198
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Secure Communication ◽  
Information Technologies ◽  
Network Models ◽  
Error Rates ◽  
Multimedia Data ◽  
Secret Message ◽  
Neural Network Models

The innovations in advanced information technologies has led to rapid delivery and sharing of multimedia data like images and videos. The digital steganography offers ability to secure communication and imperative for internet. The image steganography is essential to preserve confidential information of security applications. The secret image is embedded within pixels. The embedding of secret message is done by applied with S-UNIWARD and WOW steganography. Hidden messages are reveled using steganalysis. The exploration of research interests focused on conventional fields and recent technological fields of steganalysis. This paper devises Convolutional neural network models for steganalysis. Convolutional neural network (CNN) is one of the most frequently used deep learning techniques. The Convolutional neural network is used to extract spatio-temporal information or features and classification. We have compared steganalysis outcome with AlexNet and SRNeT with same dataset. The stegnalytic error rates are compared with different payloads.

scholarly journals Optimal Deep Learning based Data Classification Model for Type-2 Diabetes Mellitus Diagnosis and Prediction System

2020 ◽  
Vol 9 (3) ◽  
pp. 1596-1604
Keyword(s):  
Deep Learning ◽  
Data Classification ◽  
Research Area ◽  
Classification Model ◽  
Prediction System ◽  
Pima Indians ◽  
Significant Research ◽  
Diabetes Mellitus Diagnosis ◽  
Simulation Results

In recent days, deep learning models become a significant research area because of its applicability in diverse domains. In this paper, we employ an optimal deep neural network (DNN) based model for classifying diabetes disease. The DNN is employed for diagnosing the patient diseases effectively with better performance. To further improve the classifier efficiency, multilayer perceptron (MLP) is employed to remove the misclassified instance in the dataset. Then, the processed data is again provided as input to the DNN based classification model. The use of MLP significantly helps to remove the misclassified instances. The presented optimal data classification model is experimented on the PIMA Indians Diabetes dataset which holds the medical details of 768 patients under the presence of 8 attributes for every record. The obtained simulation results verified the superior nature of the presented model over the compared methods.

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