We are excited to share that the Journal of Scientific Computing has published our comprehensive review paper on “Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next”. 

The paper focuses on Physics-Informed Neural Networks (PINN), a novel approach where neural networks are used to solve complex mathematical equations, including Partial Differential Equations (PDEs). The review summarizes the literature on PINNs and their advantages and disadvantages.

We hope this review paper will be beneficial to researchers in the field of scientific machine learning and inspire future work on this promising approach.

Find out more at: https://link.springer.com/article/10.1007/s10915-022-01939-z 

We are excited to welcome MariaPia de Rosa and Stefano Izzo as new PhD students to our MODAL laboratory! With their expertise and skills, we’re looking forward to the exciting contributions they will bring to our ongoing projects. Stay tuned for more updates as we continue to push the boundaries of cutting-edge research in DL and AI!  

AWARD – We are proud to announce that our article titled A survey on deep learning in medicine: Why, how and when? has been awarded as Highly Cited Paper in 2021 by Web of Science, Clarivative. Here the Link

As of July/August 2021, this Highly cited received enough citations to place it in the top 1% of the academic field of Computer Science based on a highly cited threshold for the field and publication year.

We are excited to welcome our new PhD students, Diletta Chiaro from Federico II and Pian Qi from China, to our research group. Pian will be joining us in MODAL and staying with us for the next 4 years, and we are excited to see the valuable contributions she will bring to MODAL. Congratulations and best wishes for your academic journey! 🎓👩‍🎓👨‍🎓

NEW PUBLICATION – First research results of the BIOCHIP (Intelligent BIOsensors based on CHImeric Proteins) project have been published by M.O.D.A.L. on the top-ranking journal Biosensors and Bioelectronics (Elsevier).

The project “Intelligent biosensors based on chimeric proteins” (BIOCHIP) will synergically integrate principles and potentialities derived from biochemistry, chemistry, and informatics, to create intelligent biosensors endowed with high sensitivity, specificity, reliability, and ability to work in a wide range of matrices.

Link to the publication:

https://www.sciencedirect.com/science/article/pii/S0956566321007338

Link to the UNINA news:

http://www.unina.it/-/27081936-progetto-di-ricerca-biochip-pubblicati-i-primi-risultati-su-prestigiosa-rivista

We are proud to announce that the paper “Predictive Medicine for Salivary Gland Tumours Identification Through Deep Learning” has been published in the Journal of Biomedical and Health Informatics. 🎉  This research provides a promising application of AI in predictive medicine and can pave the way for the development of more accurate and personalized medical treatments. 💻📈
Check out more at https://ieeexplore.ieee.org/abstract/document/9573315?casa_token=J1rtYgM-ugcAAAAA:U19s6LI6xNl9zoIUoXDTOm7ytwudAUGDk8_XmHodORg0fcRzc_gd1MtEDR4xhyyP_hiV1hQ

This research work presents and discusses a Deep Learning-based framework for automatic segmentation and classification of salivary gland tumours. Furthermore, we propose an explainable segmentation learning approach supporting the effectiveness of the proposed framework through a per-epoch learning process analysis and the attention map mechanism. The proposed framework was evaluated with a collected CT dataset of patients with salivary gland tumours. Experimental results show that our methodology achieves significant scores on both segmentation and classification tasks.

Link to Publication:

https://ieeexplore.ieee.org/abstract/document/9573315

Welcome Dr. Victor Rodriguez to our research community! We are excited to have you as a visiting researcher. Your expertise and contributions will undoubtedly be valuable to our team. We hope that your time here will be productive, enriching, and enjoyable!

NEW PUBLICATION –  Predictive Analytics for Smart Parking: A Deep Learning Approach in Forecasting of IoT Data is the title of the article published by M.O.D.A.L. on ACM Transactions on Internet Technology journal.

In this article, we present and discuss an innovative Deep Learning-based ensemble technique in forecasting the parking space occupancy to reduce the search time for parking and to optimize the flow of cars in particularly congested areas, with an overall positive impact on traffic in urban centres.

Link to the publication:

https://dl.acm.org/doi/abs/10.1145/3412842

NEW PUBLICATION – The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic is the title of the article published by M.O.D.A.L. on Information Systems Frontiers, Springer.

Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.

Link to the publication:

https://link.springer.com/article/10.1007/s10796-021-10131-x

NEW PUBLICATION – Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion is the title of the article published by M.O.D.A.L. on Information Fusion, Elsevier.

This paper presents and discusses a multi-source time series fusion and forecasting framework relying on Deep Learning. By combining weather, air-quality and medical bookings time series through a feature compression stage which preserves temporal patterns, the prediction is provided through a flexible ensemble technique based on machine learning models and a hybrid neural network. The proposed system is able to predict the number of bookings related to a specific medical examination for a 7-days horizon period. To assess the proposed approach’s effectiveness, we rely on time series extracted from a real dataset of administrative e-health records provided by the Campania Region health department, in Italy.

Link to Publication:

https://www.sciencedirect.com/science/article/pii/S1566253521000592