Search Results for author:
Found 8 papers, 0 papers with code
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach
We also test our methods using various classifier algorithms and compare them with the state-of-the-art data poisoning method using the Jacobian matrix.
On Defending Against Label Flipping Attacks on Malware Detection Systems
Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19\% accuracy when compared with the state-of-the-art method in the literature.
Similarity-based Android Malware Detection Using Hamming Distance of Static Binary Features
We test our experiments in a different type of features: API, intent, and permission features on these three datasets.
Medical Image Registration Using Deep Neural Networks: A Comprehensive Review
Image-guided interventions are saving the lives of a large number of patients where the image registration problem should indeed be considered as the most complex and complicated issue to be tackled.
A New Approach to Image Compression in Industrial Internet of Things
To do this, a new adaptive lossy compression technique to compact sensor-generated images in IIoT by using K- Means++ and Intelligent Embedded Coding (IEC) as our novel approach, is presented.
Cyrus 2D Simulation Team Description Paper 2016
This description includes some explanation about algorithms and also algorithms that are being implemented by Cyrus team members.
Deep Image: A precious image based deep learning method for online malware detection in IoT Environment
In this way, a criterion is introduced that is used together with accuracy and FPR criteria for malware analysis in IoT environment.
Medical image registration using unsupervised deep neural network: A scoping literature review
In medicine, image registration is vital in image-guided interventions and other clinical applications.
