Fabrizio De Vita

Orcid: 0000-0002-6709-8001

According to our database1, Fabrizio De Vita authored at least 22 papers between 2018 and 2024.

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

Timeline

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Bibliography

2024
A Numerical Comparison of Deeply Quantized Models for sEMG Hand Gesture Classification on Constrained Devices.
Proceedings of the IEEE International Conference on Smart Computing, 2024

Leveraging Homeostatic Plasticity to Enable Anomaly Detection in Spiking Neural Networks.
Proceedings of the IEEE International Conference on Smart Computing, 2024

2023
A Process Mining-based unsupervised Anomaly Detection technique for the Industrial Internet of Things.
Internet Things, December, 2023

A Novel Echo State Network Autoencoder for Anomaly Detection in Industrial IoT Systems.
IEEE Trans. Ind. Informatics, August, 2023

µ-FF: On-Device Forward-Forward Training Algorithm for Microcontrollers.
Proceedings of the 2023 IEEE International Conference on Smart Computing, 2023

2022
Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks.
Sensors, 2022

A fog-assisted system to defend against Sybils in vehicular crowdsourcing.
Pervasive Mob. Comput., 2022

On-Device Training of Deep Learning Models on Edge Microcontrollers.
Proceedings of the 2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, 2022

Traffic Condition Estimation at the Smart City Edge using Deep Learning: A Ro-Pax Terminal Case Study.
Proceedings of the IEEE International Smart Cities Conference, 2022

2021
Porting deep neural networks on the edge via dynamic K-means compression: A case study of plant disease detection.
Pervasive Mob. Comput., 2021

A Cloud Platform for Collecting and Processing Road Pavement Multi Sensor Data.
Proceedings of the IEEE International Conference on Smart Computing, 2021

A Semi-Supervised Bayesian Anomaly Detection Technique for Diagnosing Faults in Industrial IoT Systems.
Proceedings of the IEEE International Conference on Smart Computing, 2021

A Novel Recruitment Policy to Defend against Sybils in Vehicular Crowdsourcing.
Proceedings of the IEEE International Conference on Smart Computing, 2021

2020
On the use of a full stack hardware/software infrastructure for sensor data fusion and fault prediction in industry 4.0.
Pattern Recognit. Lett., 2020

Leveraging Stack4Things for Federated Learning in Intelligent Cyber Physical Systems.
J. Sens. Actuator Networks, 2020

A deep learning approach for pressure ulcer prevention using wearable computing.
Hum. centric Comput. Inf. Sci., 2020

Quantitative Analysis of Deep Leaf: a Plant Disease Detector on the Smart Edge.
Proceedings of the IEEE International Conference on Smart Computing, 2020

A Novel Data Collection Framework for Telemetry and Anomaly Detection in Industrial IoT Systems.
Proceedings of the Fifth IEEE/ACM International Conference on Internet-of-Things Design and Implementation, 2020

2019
Using Deep Reinforcement Learning for Application Relocation in Multi-Access Edge Computing.
IEEE Commun. Stand. Mag., 2019

On the Use of LSTM Networks for Predictive Maintenance in Smart Industries.
Proceedings of the IEEE International Conference on Smart Computing, 2019

2018
A Deep Learning Approach for Indoor User Localization in Smart Environments.
Proceedings of the 2018 IEEE International Conference on Smart Computing, 2018

A Deep Reinforcement Learning Approach For Data Migration in Multi-Access Edge Computing.
Proceedings of the 2018 ITU Kaleidoscope: Machine Learning for a 5G Future, 2018


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