7 research outputs found
Evidence combination for incremental decision-making processes
The establishment of a medical diagnosis is an incremental process highly fraught with uncertainty. At each step of this painstaking process, it may be beneficial to be able to quantify the uncertainty linked to the diagnosis and steadily update the uncertainty estimation using available sources of information, for example user feedback, as they become available. Using the example of medical data in general and EEG data in particular, we show what types of evidence can affect discrete variables such as a medical diagnosis and build a simple and computationally efficient evidence combination model based on the Dempster-Shafer theory
A baseline for unsupervised advanced persistent threat detection in system-level provenance
Advanced persistent threats (APT) are stealthy, sophisticated, and
unpredictable cyberattacks that can steal intellectual property, damage
critical infrastructure, or cause millions of dollars in damage. Detecting APTs
by monitoring system-level activity is difficult because manually inspecting
the high volume of normal system activity is overwhelming for security
analysts. We evaluate the effectiveness of unsupervised batch and streaming
anomaly detection algorithms over multiple gigabytes of provenance traces
recorded on four different operating systems to determine whether they can
detect realistic APT-like attacks reliably and efficiently. This report is the
first detailed study of the effectiveness of generic unsupervised anomaly
detection techniques in this setting
Deep Learning for Epilepsy monitoring: A survey
Diagnosis of epilepsy can be expensive, time-consuming, and often inaccurate. The gold standard diagnostic monitoring is continuous video-electroencephalography (EEG), which ideally captures all epileptic events and dis-charges. Automated monitoring of seizures and epileptic activity from EEG would save time and resources, it is the focus of much EEG-based epilepsy research. The purpose of this paper is to provide a survey in order to understand, classify and benchmark the key parameters of deep learning-based approaches that were applied in the processing of EEG signals for epilepsy monitoring. This survey identifies the availability of data and the black-box nature of DL as the main challenges hindering the clinical acceptance of EEG analysis systems based on Deep Learning and suggests the use of Explainable Artificial Intelligence (XAI) and Transfer Learning to overcome these issues. It also underlines the need for more research to recognize the full potential of big data, Computing Edge, IoT to implement wearable devices that can assist epileptic patients and improve their quality of life