Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 17, 2026
Recent Advances in Hyperparameter Tuning for Machine Learning Models
Article Number 4
Number of page(s) 11
DOI https://doi.org/10.1051/smdo/2026002
Published online 17 March 2026
  1. M. Karnan, M. Akila, N. Krishnaraj, Biometric personal authentication using keystroke dynamics: a review, Appl. Soft Comput. 11, 1565–1573 (2011) [Google Scholar]
  2. P.S. Teh, N. Zhang, A.B.J. Teoh, K. Chen, A survey on touch dynamics authentication in mobile devices, Comput. Security, 59, 210–235 (2016) [Google Scholar]
  3. S. Khellat-Kihel, R. Abrishambaf, J.L. Monteiro, M. Benyettou, Multimodal fusion of the finger vein, fingerprint and the finger-knuckle-print using Kernel Fisher analysis, Appl. Soft Comput. 42, 439–447 (2016) [Google Scholar]
  4. C. Galdi, M. Nappi, D. Riccio, H. Wechsler, Eye movement analysis for human authentication: a critical survey, Pattern Recognit. Lett. 84, 272–283 (2016) [Google Scholar]
  5. F. Yazdani, M. Emadi Andani, Verification based on palm vein by estimating wavelet coefficient with autoregressive model, in: 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), 2017, pp. 118–122 [Google Scholar]
  6. M. Emadi Andani, Z. Salehi, An affordable and easy-to-use tool to diagnose knee arthritis using knee sound, Biomed. Signal Process. Control 88, 105685 (2024) [Google Scholar]
  7. S. Dargan, M. Kumar, A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities, Expert Syst. Appl. 143, 113114 (2020) [Google Scholar]
  8. M.U. Khan, Z.A. Choudry, S. Aziz, S.Z.H. Naqvi, A. Aymin, M.A. Imtiaz, Biometric authentication based on EMG signals of speech, in: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), IEEE, 2020, pp. 1–5 [Google Scholar]
  9. S. Minaee, A. Abdolrashidi, H. Su, M. Bennamoun, D. Zhang, Biometrics recognition using deep learning: a survey, Artif. Intell. Rev. 1–49 (2023). https://doi.org/10.48550/arXiv.1912.00271 [Google Scholar]
  10. M. Mailah, B.H. Lim, Biometric signature verification using pen position, time, velocity and pressure parameters, J. Teknol. (2012), https://doi.org/10.11113/jt.v48.218 [Google Scholar]
  11. B.K. Jaisawal, Y. Perwej, S.K. Singh, S. Kumar, J.P. Dixit, N.K. Singh, An empirical investigation of human identity verification methods, Int. J. Sci. Res. Sci. Eng. Technol. (IJSRSET), 10, 16–38 (2023) [Google Scholar]
  12. M.H. Yaacob, S.Z.S. Idrus, W.Z.W. Ali, W.A. Mustafa, M.F. Jamlos, M.H.A. Wahab, A review on feature extraction in keystroke dynamics, J. Phys. (2020). https://doi.org/10.1088/1742-6596/1529/2/022088 [Google Scholar]
  13. C.R.P. Siahaan, A. Chowanda, Spoofing keystroke dynamics authentication through synthetic typing pattern extracted from screen-recorded video, J. Big Data, 9 (2022). https://doi.org/10.1186/s40537-022-00662-8 [Google Scholar]
  14. E. Maiorana, H. Kalita, P. Campisi, Mobile keystroke dynamics for biometric recognition: an overview, IET Biometrics, 10, 1–23 (2021) [Google Scholar]
  15. H. Nonaka, M. Kurihara, Sensing pressure for authentication system using keystroke dynamics. Zenodo (CERN European Organization for Nuclear Research). (2005). https://doi.org/10.5281/zenodo.1058297 [Google Scholar]
  16. C.S. Leberknight, G.R. Widmeyer, M. Recce, An investigation into the efficacy of keystroke analysis for perimeter defense and facility access, in: IEEE International Conference on Technologies for Homeland Security, 2008, https://doi.org/10.1109/ths.2008.4534475 [Google Scholar]
  17. H. Saevanee, P. Bhattarakosol, Authenticating user using keystroke dynamics and finger pressure, in: Consumer Communications and Networking Conference, 2009. https://doi.org/10.1109/ccnc.2009.4784783 [Google Scholar]
  18. M. Trojahn, F. Ortmeier, Toward mobile authentication with keystroke dynamics on mobile phones and tablets, Adv. Inf. Netw. Appl. (2013). https://doi.org/10.1109/waina.2013.36 [Google Scholar]
  19. A.E. Sulavko, A.S. Eremenko, A.K. Fedotov, Users’ identification through keystroke dynamics based on vibration parameters and keyboard pressure, Dyn. Syst. Mech. Mach. (2017). https://doi.org/10.1109/dynamics.2017.8239514 [Google Scholar]
  20. S. Krishnamoorthy, L. Rueda, S. Saad, H. Elmiligi, Identification of user behavioral biometrics for authentication using keystroke dynamics and machine learning, in: Proceedings of the 2018 2nd International Conference on Biometric Engineering and Applications, 2018. https://doi.org/10.1145/3230820.3230829 [Google Scholar]
  21. H. Lee, J. Hwang, D. Kim, S. Lee, S. Lee, J.H. Shin, Understanding keystroke dynamics for smartphone users authentication and keystroke dynamics on smartphones built-in motion sensors, Secur. Commun. Netw. 2018, 1–10 (2018) [Google Scholar]
  22. C. Wu, W. Ding, R. Liu, J. Wang, A.C. Wang, J.J. Wang, S. Li, Y. Zi, Z.L. Wang, Keystroke dynamics enabled authentication and identification using triboelectric nanogenerator array, Mater. Today, 21, 216–222 (2018) [Google Scholar]
  23. H. Lee, J. Hwang, S. Lee, D. Kim, S. Lee, J. Lee, J.H. Shin, A parameterized model to select discriminating features on keystroke dynamics authentication on smartphones, Pervasive Mob. Comput. 54, 45–57 (2019) [Google Scholar]
  24. S.A. Alsuhibany, A.S. Almuqbil, Analyzing the effectiveness of touch keystroke dynamic authentication for the arabic language, Wirel. Commun. Mob. Comput. 2021, 1–15 (2021) [Google Scholar]
  25. A.B. López, Deep learning in biometrics: a survey, ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. 8, 19–32 (2019) [Google Scholar]
  26. P. Terrier, Gait recognition via deep learning of the center-of-pressure trajectory, Appl. Sci. 10, 774 (2020). [Google Scholar]
  27. J. Moon, N.H. Minaya, N.A. Le, H.C. Park, S.I. Choi, Can ensemble deep learning identify people by their gait using data collected from multi-modal sensors in their insole? Sensors 20, 4001 (2020) [Google Scholar]
  28. D. Deb, A. Ross, A.K. Jain, K. Prakah-Asante, K.V. Prasad, Actions speak louder than (pass) words: passive authentication of smartphone users via deep temporal features, in: 2019 international conference on biometrics (ICB), IEEE, 2019, June, pp. 1–8. https://doi.org/10.1109/ICB45273.2019.8987433 [Google Scholar]
  29. Y. Sun, Q. Gao, X. Du, Z. Gu, Smartphone user authentication based on holding position and touch-typing biometrics, Comput. Mater. Continua, 61, (2019). https://doi.org/10.32604/cmc.2019.06294 [Google Scholar]
  30. G. Stragapede, R. Vera-Rodriguez, R. Tolosana, A. Morales, A. Acien, G. Le Lan, Mobile behavioral biometrics for passive authentication, Pattern Recognit. Lett. 157, 35–41 (2022) [Google Scholar]
  31. G. Stragapede, R. Vera-Rodriguez, R. Tolosana, A. Morales, BehavePassDB: benchmarking mobile behavioral biometrics, 2022. arXiv preprint arXiv:2206.02502. https://doi.org/10.48550/arXiv.2206.02502 [Google Scholar]
  32. G. Stragapede, P. Delgado-Santos, R. Tolosana, R. Vera-Rodriguez, R. Guest, A. Morales, Mobile keystroke biometrics using transformers, in: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), IEEE, 2023, January, pp. 1–6 [Google Scholar]
  33. M. Fiorio, M. Emadi Andani, A. Marotta, J. Classen, M. Tinazzi, Placebo-induced changes in excitatory and inhibitory corticospinal circuits during motor performance, J. Neurosci. 34, 3993–4005 (2014) [Google Scholar]
  34. M. Emadi Andani, M. Tinazzi, N. Corsi, M. Fiorio, Modulation of inhibitory corticospinal circuits induced by a nocebo procedure in motor performance, PLOS ONE, 10, e0125223 (2015) [Google Scholar]
  35. N. Corsi, M. Emadi Andani, M. Tinazzi, M. Fiorio, Changes in perception of treatment efficacy are associated to the magnitude of the nocebo effect and to personality traits, Sci. Rep. 6, (2016). https://doi.org/10.1038/srep30671 [Google Scholar]
  36. G. Rossettini, M. Emadi Andani, F. Dalla Negra, M. Testa, M. Tinazzi, M. Fiorio, The placebo effect in the motor domain is differently modulated by the external and internal focus of attention, Sci. Rep. 8, (2018). https://doi.org/10.1038/s41598-018-30228-9 [Google Scholar]
  37. B. Villa-Sánchez, M. Emadi Andani, M. Fiorio, The role of the dorsolateral prefrontal cortex in the motor placebo effect, Eur. J. Neurosci. 48, 3410–3425 (2018) [Google Scholar]
  38. Y. Sun, F.P. Lo, B. Lo, EEG-based user identification system using 1D-convolutional long short-term memory neural networks, Exp. Syst. Appl. 125, 259–267 (2019) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.