In medicine, image registration is vital in image-guided interventions and other clinical applications.
In this way, a criterion is introduced that is used together with accuracy and FPR criteria for malware analysis in IoT environment.
This description includes some explanation about algorithms and also algorithms that are being implemented by Cyrus team members.
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.
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.
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.
We test our experiments in a different type of features: API, intent, and permission features on these three datasets.
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.