Daniel L. Marino

Orcid: 0000-0002-8686-4752

According to our database1, Daniel L. Marino authored at least 28 papers between 2016 and 2023.

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

Timeline

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Bibliography

2023
RX-ADS: Interpretable Anomaly Detection Using Adversarial ML for Electric Vehicle CAN Data.
IEEE Trans. Intell. Transp. Syst., December, 2023

Editorial: Explainable artificial intelligence.
Frontiers Comput. Sci., 2023

Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting.
IEEE Access, 2023

Informed Deep Learning for Anomaly Detection in Cyber-Physical Systems.
Proceedings of the IEEE International Conference on Industrial Technology, 2023

2022
An Artificial Intelligence Approach for Real-Time Tuning of Weighting Factors in FCS-MPC for Power Converters.
IEEE Trans. Ind. Electron., 2022

Self-Supervised and Interpretable Anomaly Detection using Network Transformers.
CoRR, 2022

Anomaly Detection in Critical-Infrastructures using Autoencoders: A Survey.
Proceedings of the IECON 2022, 2022

2021
ResNet Autoencoders for Unsupervised Feature Learning From High-Dimensional Data: Deep Models Resistant to Performance Degradation.
IEEE Access, 2021

Explainable Unsupervised Machine Learning for Cyber-Physical Systems.
IEEE Access, 2021

Data-Driven Correlation of Cyber and Physical Anomalies for Holistic System Health Monitoring.
IEEE Access, 2021

The Virtualized Cyber-Physical Testbed for Machine Learning Anomaly Detection: A Wind Powered Grid Case Study.
IEEE Access, 2021

Deep Embedded Clustering with ResNets.
Proceedings of the 14th International Conference on Human System Interaction, 2021

2020
Trustworthy AI Development Guidelines for Human System Interaction.
Proceedings of the 13th International Conference on Human System Interaction, 2020

AI Augmentation for Trustworthy AI: Augmented Robot Teleoperation.
Proceedings of the 13th International Conference on Human System Interaction, 2020

2019
Modeling and Planning Under Uncertainty Using Deep Neural Networks.
IEEE Trans. Ind. Informatics, 2019

Combining Physics-Based Domain Knowledge and Machine Learning using Variational Gaussian Processes with Explicit Linear Prior.
CoRR, 2019

Data-driven Stochastic Anomaly Detection on Smart-Grid communications using Mixture Poisson Distributions.
Proceedings of the IECON 2019, 2019

Data Driven Hourly Taxi Drop-offs Prediction using TLC Trip Record Data.
Proceedings of the 12th International Conference on Human System Interaction, 2019

Intelligent Driver System for Improving Fuel Efficiency in Vehicle Fleets.
Proceedings of the 12th International Conference on Human System Interaction, 2019

Machine Learning for Deep Brain Stimulation Efficacy using Dense Array EEG.
Proceedings of the 12th International Conference on Human System Interaction, 2019

2018
Generalization of Deep Learning for Cyber-Physical System Security: A Survey.
Proceedings of the IECON 2018, 2018

An Adversarial Approach for Explainable AI in Intrusion Detection Systems.
Proceedings of the IECON 2018, 2018

Deep Self-Organizing Maps for Visual Data Mining.
Proceedings of the 11th International Conference on Human System Interaction, 2018

Interpretable Data-Driven Modeling in Biomass Preprocessing.
Proceedings of the 11th International Conference on Human System Interaction, 2018

2017
Deep neural networks for energy load forecasting.
Proceedings of the 26th IEEE International Symposium on Industrial Electronics, 2017

2016
Simultaneous generation-classification using LSTM.
Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, 2016

Fast trajectory simplification algorithm for natural user interfaces in Robot programming by demonstration.
Proceedings of the 25th IEEE International Symposium on Industrial Electronics, 2016

Building energy load forecasting using Deep Neural Networks.
Proceedings of the IECON 2016, 2016


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