Dr Giovanna Sannino - Researcher, National Research Council of Italy

Contact

Researcher at Institute of High Performance Computing and Networking (ICAR)- National Research Council of Italy

E: giovanna dot sannino at icar dot cnr dot it

Biography

Giovanna Sannino is a Researcher at the Institute for High-Performance Computing and Networking (ICAR) of the National Research Council of Italy (CNR). During her career, she has matured skills regarding biomechanical and physiological parameters extraction and analysis, statistical analysis, using of machine learning/artificial intelligence techniques for eHealth applications, design and validation of novel ICT-based detection and monitoring solutions, especially for chronic disease (cardiovascular diseases) by using wearable devices (for example ECG sensors, accelerometer sensors, etc), experimental protocols and experimentation in real environments involving real end-users. She has been involved as the responsible and/or participant in several projects, especially regarding to mHealth and Artificial Intelligence applications. She has also been part of other national and international working groups and laboratories, such as Cognitive System Lab, IEEE 11073 Personal Health Device Working Group, and World Federation on Soft Computing (WFSC).

Giovanna Sannino is an Associate Editor for Applied Soft Computing Journal and Biomedical Signal Processing and Control journal. She has also been a guest editor of several special issues in prestigious international journals, for example, IEEE Journal of Biomedical and Health Informatics and Journal of Network and Computer Applications. As of today, she is the author or co-author of more than 80 publications in international journals, proceedings of international conferences, and international books.

Recent Publications

(the updated list of publications can be seen on Google Scholar )

1. De Falco, I., De Pietro, G., & Sannino, G. (2022). Classification of Covid-19 chest X-ray images by means of an interpretable evolutionary rule-based approach. Neural Computing and Applications, 1-11.
2. Sannino, G., De Pietro, G., & De Falco, I. (2021, December). Automatic Extraction of Interpretable Knowledge to Predict the Survival of Patients with Heart Failure. In 2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) (pp. 166-173). IEEE.
3. Celesti, A., De Falco, I., Pecchia, L., & Sannino, G. (2021). Guest Editorial Enabling Technologies for Next Generation Telehealthcare. IEEE Journal of Biomedical and Health Informatics, 25(12), 4240-4242.
4. Muhammad, K., Ullah, A., Imran, A. S., Sajjad, M., Kiran, M. S., Sannino, G., & de Albuquerque, V. H. C. (2021). Human action recognition using attention based LSTM network with dilated CNN features. Future Generation Computer Systems, 125, 820-830.
5. Verde, L., De Pietro, G., Ghoneim, A., Alrashoud, M., Al-Mutib, K.N., & Sannino, G. (2021). Exploring the use of Artificial Intelligence techniques to detect the presence of Coronavirus Covid-19 through speech and voice analysis. IEEE Access, 9, 65750-65757.
6. Sannino, G., & Falco, I. D. (2021). Use of Machine Learning Algorithms to Identify Sleep Phases Starting from ECG Signals. In Hybrid Artificial Intelligence and IoT in Healthcare (pp. 273-290). Springer, Singapore.
7. Sannino, G., De Falco, I., & De Pietro, G. (2020). Non-invasive risk stratification of hypertension: A systematic comparison of machine learning algorithms. Journal of Sensor and Actuator Networks, 9(3), 34.