A Brief Survey of Memory Analysis Tools

This paper covers five major tools used for memory forensics that would be helpful to the scientific community and forensics researchers in determining which tools are best according to their requirement. From memory forensic analysis, it is very easy to judge about malware presence and behavior. This paper shows a brief survey of the tool’s attributes and their supported platforms. We have mainly focused to mention results on the basis of running process, DLL's, drivers, registry data, event logs, web activity, services, Malware IOC(Indicators of compromise) analysis, network information, size of the tool, address translation etc. Investigators may choose one of the tools according to their requirements

Automated benchmarking of surgical skills using machine learning

Surgical trainees are required to acquire specific skills during the course of their residency before performing real surgeries. Surgical training involves constant practice of skills and seeking feedback from supervising surgeons, who generally have a packed schedule. The process of manual assessment makes the whole training cycle extremely cumbersome and inefficient. Having automated assessment systems for surgical training can be of great value to medical schools and teaching hospitals. The aim of this PhD research is to develop machine learning based methods for assessment of surgical skills from basic tasks to complex robot-assisted procedures. Specifically, this thesis will cover details of (1) developing novel motion based features for basic surgical skills assessment in open and robotic surgical training, (2) developing unsupervised and supervised methods for recognizing individual steps of complex robot-assisted (RA) surgical procedures, (3) generating automated score reports for RA surgical procedures, and (4) producing video highlights to indicate which parts of the surgical task most effected the final surgical skill score. Positive results from experiments conducted confirms the feasibility of providing automated skill based feedback to surgeons.Ph.D

Automated Assessment of Surgical Skills Using Frequency Analysis

© Springer International Publishing Switzerland 2015. The original publication is available at www.springerlink.comDOI: 10.1007/978-3-319-24553-9_53We present an automated framework for visual assessment of the expertise level of surgeons using the OSATS (Objective Structured Assessment of Technical Skills) criteria. Video analysis techniques for extracting motion quality via frequency coefficients are introduced. The framework is tested on videos of medical students with different expertise levels performing basic surgical tasks in a surgical training lab setting. We demonstrate that transforming the sequential time data into frequency components effectively extracts the useful information differentiating between different skill levels of the surgeons. The results show significant performance improvements using DFT and DCT coefficients over known state-of-the-art techniques