Anahid N. Jalali

According to our database, Anahid N. Jalali authored at least 19 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

MobilityDL: a review of deep learning from trajectory data.

Anita Graser, Anahid N. Jalali, Jasmin Lampert, Axel Weißenfeld, Krzysztof Janowicz

GeoInformatica, January, 2025

2023

A Pathway to Combat Climate Change with Human-Centred XAI.

Anahid N. Jalali, Alexander Schindler, Anita Zolles

ERCIM News, 2023

Decoding the Unknown: Unveiling Industrial Time Series Classification with Counterfactuals.

Anahid N. Jalali, Andreas Rauber, Jasmin Lampert

ERCIM News, 2023

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

Anahid N. Jalali, Bernhard Haslhofer, Simone Kriglstein, Andreas Rauber

CoRR, 2023

Towards eXplainable AI for Mobility Data Science.

Anahid N. Jalali, Anita Graser, Clemens Heistracher

CoRR, 2023

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

Lam Pham, Dat Ngo, Cam Le, Anahid N. Jalali, Alexander Schindler

CoRR, 2023

Light-Weight Deep Learning Models for Acoustic Scene Classification Using Teacher-Student Scheme and Multiple Spectrograms.

Lam Pham, Tin Nguyen, Phat Lam, Dat Ngo, Anahid N. Jalali, Alexander Schindler

Proceedings of the 2023 4th International Symposium on the Internet of Sounds, 2023

Deep Learning From Trajectory Data: a Review of Deep Neural Networks and the Trajectory Data Representations to Train Them.

Anita Graser, Anahid N. Jalali, Jasmin Lampert, Axel Weißenfeld, Krzysztof Janowicz

Proceedings of the Workshops of the EDBT/ICDT 2023 Joint Conference, 2023

2022

Ethical AI: Why and How?

Peter Biegelbauer, Anahid N. Jalali, Sven Schlarb, Michela Vignoli

ERCIM News, 2022

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

Lam Pham, Dusan Salovic, Anahid N. Jalali, Alexander Schindler, Khoa Tran, Hai Canh Vu, Phu X. Nguyen

CoRR, 2022

Low-complexity deep learning frameworks for acoustic scene classification.

Lam Pham, Dat Ngo, Anahid N. Jalali, Alexander Schindler

CoRR, 2022

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

Clemens Heistracher, Anahid N. Jalali, Jürgen Schneeweiss, Klaudia Kovacs, Catherine Laflamme, Bernhard Haslhofer

CoRR, 2022

2021

Minimal-Configuration Anomaly Detection for IIoT Sensors.

Clemens Heistracher, Anahid N. Jalali, Axel Suendermann, Sebastian Meixner, Daniel Schall, Bernhard Haslhofer, Jana Kemnitz

CoRR, 2021

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

Lam Pham, Hieu Tang, Anahid N. Jalali, Alexander Schindler, Ross King

CoRR, 2021

Transfer Learning Strategies for Anomaly Detection in IoT Vibration Data.

Clemens Heistracher, Anahid N. Jalali, Indu Strobl, Axel Suendermann, Sebastian Meixner, Stephanie Holly, Daniel Schall, Bernhard Haslhofer, Jana Kemnitz

Proceedings of the IECON 2021, 2021

2020

Multi-modal video forensic platform for investigating post-terrorist attack scenarios.

Alexander Schindler, Andrew Lindley, Anahid N. Jalali, Martin Boyer, Sergiu Gordea, Ross King

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.

Carmina Coronel, Christoph Wiesmeyr, Heinrich Garn, Bernhard Kohn, Anahid Naghibzadeh-Jalali, Alexander Schindler, Markus Wimmer, Magdalena Mandl, Martin Glos, Thomas Penzel, Gerhard Kloesch, Andrijana Stefanic-Kejik, Marion Böck, Eugenijus Kaniusas, Stefan Seidel

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.

Anahid N. Jalali, Alexander Schindler, Bernhard Haslhofer

ERCIM News, 2019

Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning.

Anahid N. Jalali, Clemens Heistracher, Alexander Schindler, Bernhard Haslhofer, Tanja Nemeth, Robert Glawar, Wilfried Sihn, Peter De Boer

Proceedings of the 2019 IEEE International Conference on Prognostics and Health Management, 2019