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Found 10 papers, 0 papers with code
MobilityDL: A Review of Deep Learning From Trajectory Data
Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior.
Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis
Furthermore, we find that counterfactual explanations and misclassifications can significantly increase the users understanding of how a machine learning model is making decisions.
Towards eXplainable AI for Mobility Data Science
This paper presents our ongoing work towards XAI for Mobility Data Science applications, focusing on explainable models that can learn from dense trajectory data, such as GPS tracks of vehicles and vessels using temporal graph neural networks (GNNs) and counterfactuals.
Explainable Artificial Intelligence (XAI)
Explainable Models
Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms
In the second phase, the student network, which presents a low complexity model, is trained with the embeddings extracted from the teacher.
Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context
In this paper, we present a comprehensive analysis of Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature.
Low-complexity deep learning frameworks for acoustic scene classification
In this report, we presents low-complexity deep learning frameworks for acoustic scene classification (ASC).
Machine Learning Methods for Health-Index Prediction in Coating Chambers
Our overall goal is to predict the future condition of the coating chamber to allow cost and quality optimized maintenance of the equipment.
Minimal-Configuration Anomaly Detection for IIoT Sensors
The increasing deployment of low-cost IoT sensor platforms in industry boosts the demand for anomaly detection solutions that fulfill two key requirements: minimal configuration effort and easy transferability across equipment.
Multi-Modal Video Forensic Platform for Investigating Post-Terrorist Attack Scenarios
Specifically, Audio Event Detection is applied to index the content according to attack-specific acoustic concepts.
Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning
Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry.
