Anahid N. Jalali

Affiliations:
  • Austrian Institute of Technology, Vienna, Austria


According to our database1, Anahid N. Jalali authored at least 18 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
MobilityDL: A Review of Deep Learning From Trajectory Data.
CoRR, 2024

2023
A Pathway to Combat Climate Change with Human-Centred XAI.
ERCIM News, 2023

Decoding the Unknown: Unveiling Industrial Time Series Classification with Counterfactuals.
ERCIM News, 2023

Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis.
CoRR, 2023

Towards eXplainable AI for Mobility Data Science.
CoRR, 2023

Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms.
CoRR, 2023

Deep Learning From Trajectory Data: a Review of Deep Neural Networks and the Trajectory Data Representations to Train Them.
Proceedings of the Workshops of the EDBT/ICDT 2023 Joint Conference, 2023

2022
Ethical AI: Why and How?
ERCIM News, 2022

Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context.
CoRR, 2022

Low-complexity deep learning frameworks for acoustic scene classification.
CoRR, 2022

Machine Learning Methods for Health-Index Prediction in Coating Chambers.
CoRR, 2022

2021
Minimal-Configuration Anomaly Detection for IIoT Sensors.
CoRR, 2021

A Low-Compexity Deep Learning Framework For Acoustic Scene Classification.
CoRR, 2021

Transfer Learning Strategies for Anomaly Detection in IoT Vibration Data.
Proceedings of the IECON 2021, 2021

2020
Multi-modal video forensic platform for investigating post-terrorist attack scenarios.
Proceedings of the 11th ACM Multimedia Systems Conference, 2020

Comparison of PSG signals and Respiratory Movement Signal via 3D Camera in Detecting Sleep Respiratory Events by LSTM Models.
Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2020

2019
Understandable Deep Neural Networks for Predictive Maintenance in the Manufacturing Industry.
ERCIM News, 2019

Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning.
Proceedings of the 2019 IEEE International Conference on Prognostics and Health Management, 2019


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