Sökning: "Outlier detection"
Visar resultat 1 - 5 av 18 avhandlingar innehållade orden Outlier detection.
1. Data Modeling for Outlier Detection
Sammanfattning : This thesis explores the data modeling for outlier detection techniques in three different application domains: maritime surveillance, district heating, and online media and sequence datasets. The proposed models are evaluated and validated under different experimental scenarios, taking into account specific characteristics and setups of the different domains. LÄS MER
2. Data Mining Approaches for Outlier Detection Analysis
Sammanfattning : Outlier detection is studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. LÄS MER
3. Urban Change Detection Using Multitemporal SAR Images
Sammanfattning : Multitemporal SAR images have been increasingly used for the detection of different types of environmental changes. The detection of urban changes using SAR images is complicated due to the complex mixture of the urban environment and the special characteristics of SAR images, for example, the existence of speckle. LÄS MER
4. Conformal anomaly detection : Detecting abnormal trajectories in surveillance applications
Sammanfattning : Human operators of modern surveillance systems are confronted with an increasing amount of trajectory data from moving objects, such as people, vehicles, vessels, and aircraft. A large majority of these trajectories reflect routine traffic and are uninteresting. LÄS MER
5. Outlier Detection as a Safety Measure for Safety Critical Deep Learning
Sammanfattning : Context: Deep learning (DL) has proven to be a valuable component in object detection and semantic segmentation tasks, as the techniques have shown significant performance gains compared to hand-made image processing algorithms. DL refers to an optimization process where a model learns properties and parameters itself through in iterative process running on labeled data. LÄS MER