Pubblicazioni

Guarino, A., Santoro, D., Grilli, L., Zaccagnino, R. & Balbi, M. (2024). EvoFolio: a portfolio optimization method based on multi-objective evolutionary algorithms. Neural Computing & Applications, doi: 10.1007/s00521-024-09456-w (FASCIA A);

Sgarro, G.A., Grilli, L. & Santoro, D. (2024). Optimal multivariate mixture: a genetic algorithm approach. Annals of Operations Research, doi: 10.1007/s10479-024-06045-x (FASCIA A);

Cappelletti, M. G., Caputo, R., Cariglia, M., Grilli, L., Russo, C., Santoro, D. & Sgarro, G. A. (2023). Harnessing the power of blockchain in the agri-food sector: a meta-analysis of current research and best practices. Applied Mathematical Sciences, 17(10), 477-501, doi: 10.12988/ams.2023.917473;

Guarino, A., Grilli, L., Santoro, D., Messina, F. & Zaccagnino, R. (2023). On the efficacy of "herd behavior" in the commodities market: A neuro-fuzzy agent "herding" on deep learning traders. Applied Stochastic Models in Business and Industry, 1-25. doi: 10.1002/asmb.2793 (FASCIA A);

Cappelletti G.M., Grilli L., Russo C.,  & Santoro, D., (2023). Benchmarking Sustainable Mobility in Higher Education. Sustainability 2023, 15(6), 5190. doi: 10.3390/su15065190;

Di Bari A., Santoro D., Tarrazon-Rodon M. A., & Villani G., (2023). The impact of polarity score on real option valuation for multistage projects. Quality & Quantity. doi: 10.1007/s11135-023-01635-6 (FASCIA A).

Cappelletti, G. M., Grilli, L., Santoro, D., & Russo, C. (2022). Machine learning and sustainable mobility: The case of the university of Foggia (Italy). Applied Sciences, 12(17):8774. doi: 10.3390/app12178774;

Grilli, L., & Santoro, D. (2022). Forecasting Financial Time Series with Boltzmann Entropy through Neural Networks. 19, 665–681. Computational Management Sciences, doi: 10.1007/s10287-022-00430-2 (FASCIA A);

Colasanto, F., Grilli, L., & Santoro, D. (2022). Directional derivatives in non-Hausdorff TVS: topological filter techniques without metric structures. Applied Mathematical Sciences, 16(5), 251-260. doi: 10.12988/ams.2022.916795;

Colasanto, F., Grilli, L., Santoro, D., & Villani, G. (2022). A neural network contribute to reverse cryptographic processes in bitcoin systems: attention on SHA256. Applied Mathematical Sciences, 16(4), 215-232. doi: 10.12988/ams.2022.916778;

Guarino, A., Grilli, L., Santoro, D., Messina, F. & Zaccagnino, R. (2022). To learn or not to learn? evaluating autonomous, adaptive, automated traders in cryptocurrencies financial bubbles. 34, 20715–20756. Neural Computing & Applications, doi: 10.1007/s00521-022-07543-4 (FASCIA A);

Colasanto, F., Grilli, L., Santoro, D., & Villani, G. (2022). Bert’s sentiment score for portfolio optimization: A fine-tuned view in black and litterman model. 34, 17507–17521. Neural Computing & Applications, doi: 10.1007/s00521-022-07403-1 (FASCIA A);

Santoro, D. & Grilli, L. (2022). Generative adversarial network to evaluate quantity of information in financial markets. 34, 17473–17490. Neural Computing & Applications, doi: 10.1007/s00521-022-07401-3 (FASCIA A e finalista Best Paper AMASES 2023);

Colasanto, F., Grilli, L., Santoro, D., & Villani, G. (2022). AlBERTino for stock price prediction: A Gibbs sampling approach. Information Sciences, 597, 341–357. doi: 10.1016/j.ins.2022.03.051 (FASCIA A e vincitore Best Paper DySES2023🥳);

Santoro, D., & Villani, G. (2022). Real R&D options under sentimental information analysis. In M. Corazza (Ed.), MAF 2022, Mathematical and Statistical Methods for Actuarial Sciences and Finance (Chap. 67, pp. 1–6). 10.1007/978-3-030-99638- 3_67. IT: Springer Nature Switzerland AG.

Cappelletti, G. M., Grilli, L., Santoro, D., & Russo, C. (2021). Sustainable mobility in universities: The case of the university of Foggia (italy). Environments, 8, 57. doi: 10.3390/environments8060057;

Grilli, L., & Santoro, D. (2021). A statistical ensemble based approach for entropy in cryptocurrencies markets. Chaotic Modeling and Simulation - CMSIM, ISSN: 2241-0503, 2, 91–103;

Grilli, L., & Santoro, D. (2021). Cryptocurrencies markets and entropy: A statistical ensemble based approach. Applied Mathematical Sciences, 15(7), 297–320. doi: 10.12988/ams.2021.914488;

Casalino, G., Grilli, L., Limone, P., Santoro, D., & Schicchi, D. (2021). Deep learning for knowledge tracing in learning ana- lytics: An overview. In P. Limone (Ed.), Proceedings of the First Workshop on Technology Enhanced Learning Environments for Blended Education - The Italian e-learning Conference 2021 (teleXbe 2021) (Vol. 2817), IT: CEUR Workshop Proceedings - ISSN:1613-0073;

Grilli, L., & Santoro, D. (2021). A statistical ensemble based approach for entropy in cryptocurrencies markets. In Y. D. Christos H. Skiadas (Ed.), Proceedings of the 13th International Chaotic Modeling and Simulation International Conference (p. 1091). ISSN: 2213-8684. GR: Springer International Publishing;

Grilli, L., & Santoro, D. (2021). Machine-deep learning and finance: A review of recent results. In E. Set (Ed.), Proceedings book of the 4th International Conference on Mathematical and Related Sciences: Current Trends and Developments (ICMRS 2021) (p. 4). ISBN: 978-605-70978-1-1, TR.

Grilli, L., & Santoro, D. (2020). Generative adversarial network for market hourly discrimination. In E. Set (Ed.), Proceedings book of the 3rd International Conference on Mathematical and Related Sciences: Current Trends and Developments (ICMRS 2020) (pp. 106– 113). ISBN: 978-625-409-146-9, TR.