Securing e-Health application of cloud computing using hyperchaotic image encryption framework

Automated ischemic acute infarction detection using pre-trained CNN models' deep features

In the recent time, enviromental sound classification has received much popularity. This area of research comes under domain of non-speech audio classification. In this work, we have proposed a dilated Convolutional Neural Network approch to classify urban sound. We have carried out feature extraction, data augmentation techniques to carry out our experimental strategy smoothly. We also found out the activation maps of each layers of dilated convolution neural network. An increamental dilation rate has exploited Overall we achieved 84.16% of accuracy from the proposed dilated convolutional method. The gradual increaments of dilation rate has exploited the worse effect of grindding and has lowered down the computational cost. Also, overall classification performance, precision, recall,overall truth and kappa value have been obtained from our proposed method. We have considered 10 fold cross validation for the implementation of the dilated CNN model.

Deep convolutional neural network for environmental sound classification via dilation

Fire calamity is one of the worst adversarial events that can happen to the human race. Fire disaster can happen as a manmade disaster or even naturally, and it may cause environmental, social, and financial damages as well. In order to minimalize the unwanted fire calamity, early detection of fire eruptions coupled with immediate and effective response is extremely vital to disaster management systems. The classification of forest fire and non fire images using deep learning techniques has recently received popularity. Detection and prevention of forest fire have lot of significance from the perspective of the forest fire department, specially for the fire and arson investigators. There are shortcomings in the current mechanisms of forest fire detection in terms of accuracy. Hence, we propose a fire detection model using LeNet5 convolutional neural networks (CNN), which can spot fire in outdoor environments by classifying fire and non fire images. L2 regularization is critical technique that manipulates the complexity of the convolutional neural network model. In our work fire images have certain features that decide if the image is fire or non fire.A weight is assigned to every feature. Regularization used to help to reduce the over fitting that used to caused by plenty of weights. Our proposed provides the directiontowards developing a system that detects the early stages of forest fire.This model can further be utilized to prevent the damage caused by the fire. A CNN is a deep learning method, which has been adopted in order to detect the images of fire and non-fire. With the non sparse solution of L2 regularization we have obtained around 87% of train accuracy, 71% of validation accuracy and 70% of test accuracy after running 10 epochs. 

L2 regularized deep convolutional neural networks for fire detection

Coronary artery disease (CAD) has become a significant cause of heart attack, especially among those 40 years old or younger. There is a need to develop new technologies and methods to deal with this disease. Many researchers have proposed image processing-based solutions for CAD diagnosis, but achieving highly accurate results for angiogram segmentation is still a challenge. Several different types of angiograms are adopted for CAD diagnosis. This paper proposes an approach for image segmentation using Convolution Neural Networks (CNN) for diagnosing coronary artery disease to achieve state-of-the-art results. We have collected the 2D X-ray images from the hospital, and the proposed model has been applied to them. Image augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s size. Also, the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high accuracy. As the output, different settings of the network architecture undoubtedly have achieved different accuracy, among which the highest accuracy of the model is 97.61%. Compared with the other models, these results have proven to be superior to this proposed method in achieving state-of-the-art results. 

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Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

A computed tomography scan is a process that uses computers and X-ray machines, which rotate to create cross-sectional images of the body. The images provided by the CT scans are more accurate and detailed in comparison to standard X-ray images. The detail in the images can be increased by administering a chemical dye called contrast. A medical practitioner can administer the dye with prior permission from the patient as there is a risk of having side effects such as cancerous disease and headaches. Our study aims at providing a classification model using a CNN learning algorithm to classify images based on the age of a person and if contrast material is used. A practitioner hence can get prior knowledge about trends in CT scan images and if administering contrast material would affect the image in any way. Our proposed model provides an accuracy of 89.99% on any test CT image.

Classifying CT scan images based on contrast material and age of a person: ConvNets approach

This paper describes an algorithm of encrypting wave files in image format with a randomly generated orthogonal key decrypting them after being passed through a channel with white Gaussian noise. Gaussian noise is then removed and then matrix manipulations are done, to get the original audio back. In this age of information, audio encryption can be a secure way to communicate between individuals with assurance of privacy of the data.

Soumik Mitra

Papers

Classifying CT scan images based on contrast material and age of a person: ConvNets approach

Audio encryption and decryption algorithm in image format for secured communication