Update research.md

Author: MPiangerelli

research.md

@@ -20,38 +20,29 @@ I am also interested in more purely educational aspects and I participated in th
 
 #### Selected Publications ####
 1. Abdullahu, E., Wache, H., Piangerelli, M. (2025). Secure and Decentralized Hybrid
-Multi-Face Recognition for IoT Applications. Sensors, 25, 5880.
-https://doi.org/10.3390/s25185880
+Multi-Face Recognition for IoT Applications. Sensors, 25, 5880. https://doi.org/10.3390/s25185880
 2. Cruciata, L., Contino, S., Ciccarelli, M., Pirrone, R., Mostarda, L., Papetti, A.,
 Piangerelli, M. (2025). Lightweight Vision Transformer for Frame-Level Ergonomic
-Posture Classification in Industrial Workflows. Sensors, 25, 4750.
-https://doi.org/10.3390/s25154750
+Posture Classification in Industrial Workflows. Sensors, 25, 4750. https://doi.org/10.3390/s25154750
 3. Corradini, F., Leonesi, M., Piangerelli, M. (2025). State of the Art and Future Directions
-of Small Language Models: A Systematic Review. Big Data and Cognitive Computing, 9,
-189.https://doi.org/10.3390/bdcc9070189
+of Small Language Models: A Systematic Review. Big Data and Cognitive Computing, 9, 189.https://doi.org/10.3390/bdcc9070189
 4. Pelosi, D., Cacciagrano, D., Piangerelli, M. (2025). Explainability and Interpretability in
-Concept and Data Drift: A Systematic Literature Review. Algorithms, 18, 443.
-https://doi.org/10.3390/a18070443
+Concept and Data Drift: A Systematic Literature Review. Algorithms, 18, 443. https://doi.org/10.3390/a18070443
 5. Corradini, F., Mozzoni, L., Piangerelli, M., Re, B., Rossi, L. (2025). A Framework for
-Rapidly Prototyping Data Mining Pipelines. Big Data and Cognitive Computing, 9, 150.
-https://doi.org/10.3390/bdcc9060150
+Rapidly Prototyping Data Mining Pipelines. Big Data and Cognitive Computing, 9, 150. https://doi.org/10.3390/bdcc9060150
 6. Corradini, F., Nucci, V., Piangerelli, M., Re, B. (2025). Online Clustering with
 Interpretable Drift Adaptation to Mixed Features. Intelligent Systems with Applications,
 200510. https://doi.org/10.1016/j.iswa.2025.200510
 7. Assefa, R., Mamuye, A., Piangerelli, M. (2025). COVID-19 Severity Classification Using
 Hybrid Feature Extraction: Integrating Persistent Homology, Convolutional Neural
-Networks and Vision Transformers. Big Data and Cognitive Computing, 9, 83.
-https://doi.org/10.3390/bdcc9040083
+Networks and Vision Transformers. Big Data and Cognitive Computing, 9, 83. https://doi.org/10.3390/bdcc9040083
 8. Ahmed, U., Alexopoulos, C., Piangerelli, M., Polini, A. (2024). BRYT: Automated
-Keyword Extraction for Open Datasets. Intelligent Systems with Applications, 23,
-200421. https://doi.org/10.1016/j.iswa.2024.200421
+Keyword Extraction for Open Datasets. Intelligent Systems with Applications, 23,200421. https://doi.org/10.1016/j.iswa.2024.200421
 9. Ciccarelli, M., Corradini, F., Germani, M., Menchi, G., Mostarda, L., Papetti, A.,
 Piangerelli, M. (2022). SPECTRE: A Deep Learning Network for Posture Recognition in
-Manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-
-021-01853-2
+Manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01853-2
 10. De Simone, A., Piangerelli, M. (2020). A Bayesian Approach for Monitoring Epidemics in
-Presence of Undetected Cases. Chaos, Solitons and Fractals, 140, 110167.
-https://doi.org/10.1016/j.chaos.2020.110167
+Presence of Undetected Cases. Chaos, Solitons and Fractals, 140, 110167.https://doi.org/10.1016/j.chaos.2020.110167