Discovering varying patterns of Normal and interleaved ADLs in smart homes. - Mahsa Raeiszadeh; Hooman Tahayori; Andrea Visconti (Appl. Intell., 2019)

A Deep Learning Approach for Real-Time Application-Level Anomaly Detection in IoT Data Streaming. - Mahsa Raeiszadeh; Ahsan Saleem; Amin Ebrahimzadeh; Roch H. Glitho; Johan Eker; Raquel A. F. Mini (CCNC, 2023)

Real-Time Anomaly Detection Using Distributed Tracing in Microservice Cloud Applications. - Mahsa Raeiszadeh; Amin Ebrahimzadeh; Ahsan Saleem; Roch H. Glitho; Johan Eker; Raquel A. F. Mini (CloudNet, 2023)

A Deep Learning Approach for Root Cause Analysis in Real-Time IIoT Edge Networks. - Ahsan Saleem; Mahsa Raeiszadeh; Amin Ebrahimzadeh; Roch H. Glitho; Johan Eker; Raquel A. F. Mini (NOMS, 2023)

A Data-Driven Approach for Adaptive Real-Time Log Parsing in Cloud Environments. - Mahsa Raeiszadeh; Felipe Estrada Solano; Roch H. Glitho; Johan Eker; Raquel A. F. Mini (MeditCom, 2024)

PECMS: modeling a personalized energy and comfort management system based on residents' behavior anticipation in smart home. - Mahsa Raeiszadeh; Hooman Tahayori; Ali Bahadori-Jahromi (J. Reliab. Intell. Environ., 2024)

Real-Time Adaptive Anomaly Detection in Industrial IoT Environments. - Mahsa Raeiszadeh; Amin Ebrahimzadeh; Roch H. Glitho; Johan Eker; Raquel A. F. Mini (IEEE Trans. Netw. Serv. Manag., 2024)