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Your search keyword '"Carlo Sanghez"' showing total 3 results
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3 results on '"Carlo Sanghez"'

2. Machine Learning-aided Automatic Calibration of Smart Thermal Cameras for Health Monitoring Applications

Author

Fiammetta Marulli, Lelio Campanile, Gianfranco Palmiero, Carlo Sanghez, Michele Mastroianni, Campanile, L., Marulli, F., Mastroianni, M., Palmiero, G., Sanghez, C., Campanile, L., Marulli, F., Mastroianni, M., Palmiero, G., and Sanghez, C.

Subjects
Computer science, Calibration (statistics), Smart Sensor Networks, Internet of Things, Real-time computing, Clinical Evaluation, Mass Screening Infection, Health Monitoring, Machine Learning, Deep Learning, Covid-19 Disease, Thermal, Internet of Thing
Abstract

In this paper, we introduce a solution aiming to improve the accuracy of the surface temperature detection in an outdoor environment. The temperature sensing subsystem relies on Mobotix thermal camera without the black body, the automatic compensation subsystem relies on Raspberry Pi with Node-RED and TensorFlow 2.x. The final results showed that it is possible to automatically calibrate the camera using machine learning and that it is possible to use thermal imaging cameras even in critical conditions such as outdoors. Future development is to improve performance using computer vision techniques to rule out irrelevant measurements.

Published
2021
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3. Dataset Anonimyzation for Machine Learning: An ISP Case Study

Author

Carlo Sanghez, Gianfranco Palmiero, Fiammetta Marulli, Fabio Forgione, Lelio Campanile, Campanile, L., Forgione, F., Marulli, F., Palmiero, G., Sanghez, C., Campanile, L., Forgione, F., Marulli, F., Palmiero, G., and Sanghez, C.

Subjects
WISP, Hash, Database, Computer science, business.industry, Character masking, Hash function, Pseudo-anonymization, Logistic regression, Cryptography, Customer Premise Equipment, computer.software_genre, Random forest, Internet service provider, Attribute suppression, Technical support, Order (business), Data generalization, ISP, Raw data, business, computer
Abstract

Internet Service Providers technical support needs personal data to predict potential anomalies. In this paper, we performed a comparative study of forecasting performance using raw data and anonymized data, in order to assess how much performance may vary, when plain personal data are replaced by anonymized personal data.

Published
2021
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