scholarly journals Deep convolutional neural network based image spam classification

2020 ◽  
Author(s):  
Sriram Srinivasan ◽  
vinayakumar R ◽  
Sowmya V ◽  
Moez Krichen ◽  
Dhouha Ben Noureddine ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
False Positive Rate ◽  
Texture Features ◽  
Deep Convolutional Neural Network ◽  
Learning Approach ◽  
Learning Approaches ◽  
Spam Filters ◽  
Positive Rate ◽  
Image Spam

With the tremendous growth of the internet, cyberspace is facing several threats from the attackers. Threats like spam emails account for 55\% of total emails according to the Symantec monthly threat report. Over time, the attackers moved on to image spam to evade the text-based spam filters. To deal with this, the researchers have several machine learning and deep learning approaches that use various features like metadata, color, shape, texture features. But the Deep Convolutional Neural Network (DCNN) and transfer learning-based pre-trained CNN models are not explored much for Image spam classification. Therefore, in this work, 2 DCNN models along with few pre-trained ImageNet architectures like VGG19, Xception are trained on 3 different datasets. The effect of employing a Cost-sensitive learning approach to handle data imbalance is also studied. Some of the proposed models in this work achieves an accuracy up to 99\% with zero false positive rate in best case.

scholarly journals Deep convolutional neural network based image spam classification

2020 ◽  
Author(s):  
Sriram Srinivasan ◽  
vinayakumar R ◽  
Sowmya V ◽  
Moez Krichen ◽  
Dhouha Ben Noureddine ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
False Positive Rate ◽  
Texture Features ◽  
Deep Convolutional Neural Network ◽  
Learning Approach ◽  
Learning Approaches ◽  
Spam Filters ◽  
Positive Rate ◽  
Image Spam

With the tremendous growth of the internet, cyberspace is facing several threats from the attackers. Threats like spam emails account for 55\% of total emails according to the Symantec monthly threat report. Over time, the attackers moved on to image spam to evade the text-based spam filters. To deal with this, the researchers have several machine learning and deep learning approaches that use various features like metadata, color, shape, texture features. But the Deep Convolutional Neural Network (DCNN) and transfer learning-based pre-trained CNN models are not explored much for Image spam classification. Therefore, in this work, 2 DCNN models along with few pre-trained ImageNet architectures like VGG19, Xception are trained on 3 different datasets. The effect of employing a Cost-sensitive learning approach to handle data imbalance is also studied. Some of the proposed models in this work achieves an accuracy up to 99\% with zero false positive rate in best case.

Detection of Alzheimer’s Disease Using Deep Convolutional Neural Network

2021 ◽  
pp. 2140012
Author(s):  
Swapandeep Kaur ◽  
Sheifali Gupta ◽  
Swati Singh ◽  
Isha Gupta
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
False Positive Rate ◽  
Classification Error ◽  
Classification Error Rate ◽  
Positive Rate ◽  
Inability To Work

Alzheimer’s disease (AD) is a disease that gradually develops and causes degeneration of the cells of the brain. The leading cause of AD is dementia that results in a person’s inability to work independently. In the early stages of AD, a person forgets recent conversations or the occurrence of an event. In the later stages, there could be severe loss of memory such that the person is not able to even perform everyday tasks. The medicines currently available for AD may improve its symptoms on a temporary basis in the early stage of the disease. Since no treatment is available for curing AD, its detection becomes extremely important. As the clinical treatments are very expensive, the need for automated diagnosis of AD is of critical importance. In this paper, a deep learning model based on a convolutional neural network has been used and applied to four classes of images of AD that is very mild demented, mild demented, average demented, and non-demented. It was found that the moderate demented class had the highest accuracy of 98.9%, a classification error rate of 0.01, and a specificity of 0.992. Also, the lowest false positive rate of 0.007 was obtained.

scholarly journals INVARIANT DESCRIPTOR LEARNING USING A SIAMESE CONVOLUTIONAL NEURAL NETWORK

2016 ◽  
Vol III-3 ◽  
pp. 11-18 ◽  
Author(s):  
L. Chen ◽  
F. Rottensteiner ◽  
C. Heipke
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Moving Average ◽  
False Positive Rate ◽  
Feature Space ◽  
Recall Rate ◽  
L2 Norm ◽  
Positive Rate ◽  
Benchmark Datasets ◽  
Input Module

In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95 % recall rate on standard benchmark datasets.

A deep learning approach to identify association of disease–gene using information of disease symptoms and protein sequences

2020 ◽  
Vol 12 (15) ◽  
pp. 2016-2026
Author(s):  
Xingyu Chen ◽  
Qixing Huang ◽  
Yang Wang ◽  
Jinlong Li ◽  
Haiyan Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Disease Gene ◽  
Protein Sequences ◽  
Deep Convolutional Neural Network ◽  
Learning Approach ◽  
Disease Symptoms ◽  
Gene Association

Prediction of disease–gene association based on a deep convolutional neural network.

Deep Convolutional Neural Network Based Image Spam Classification

2020 ◽  
Author(s):  
Sriram Srinivasan ◽  
Vinayakumar Ravi ◽  
Sowmya V. ◽  
Moez Krichen ◽  
Dhouha Ben Noureddine ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Image Spam

scholarly journals A Deep Convolutional Neural Network Framework for Lung Cancer Detection

2019 ◽  
Vol 8 (4) ◽  
pp. 10105-10109
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sensitivity And Specificity ◽  
Data Science ◽  
Binary Classification ◽  
False Positive Rate ◽  
Pulmonary Nodules ◽  
High Sensitivity ◽  
Deep Convolutional Neural Network ◽  
High Level

Exact identification of pulmonary nodules with high sensitivity and specificity is basic for programmed lung malignancy analysis from CT scans. In fact, many deep learning-based algorithms gain incredible ground for improving the exactness of nodule recognition; the high false positive rate is yet a difficult issue which restricted the programmed determination. We propose a novel customized Deep Convolutional Neural Network (DCNN) architecture for learning high-level image representation to achieve high classification accuracy with low variance in medical image binary classification tasks. Moreover, a High Sensitivity and Specificity system is introduced to eliminate the erroneously recognized nodule competitors by following the appearance changes in consistent CT slices of every nodule. The proposed structure is assessed on the open Kaggle Data Science Bowl (KDSB17) challenge dataset. Our strategy can precisely distinguish lung nodules at high sensitivity and specificity and accomplishes 95 % sensitivity.

scholarly journals INVARIANT DESCRIPTOR LEARNING USING A SIAMESE CONVOLUTIONAL NEURAL NETWORK

2016 ◽  
Vol III-3 ◽  
pp. 11-18
Author(s):  
L. Chen ◽  
F. Rottensteiner ◽  
C. Heipke
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Moving Average ◽  
False Positive Rate ◽  
Feature Space ◽  
Recall Rate ◽  
L2 Norm ◽  
Positive Rate ◽  
Benchmark Datasets ◽  
Input Module

In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95 % recall rate on standard benchmark datasets.

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