Autors: Ekonomou, L., Christodoulou, C. A., Mladenov, V. M.
Title: An artificial neural network software tool for the assessment of the electric field around metal oxide surge arresters
Keywords: Artificial neural networks, Electric field, Measurements Sur

References

    Issue

    Neural Computing and Applications, Springer-Verlag London Ltd., vol. 27, issue 5, pp. 1143-1148, 2016, United Kingdom, Springer-Verlag London Ltd, DOI 10.1007/s00521-015-1969-x

    Цитирания (Citation/s):
    1. • Ramchoun, H., Ettaouil, M. New prior distribution for Bayesian neural network and learning via Hamiltonian Monte Carlo., Springer Science and Business Media Deutschland GmbH, Journal Evolving Systems, 11(4), DOI: 10.1007/s12530-019-09288-3, pp. 661-671 - 2020 - в издания, индексирани в Scopus или Web of Science
    2. Hoang, T.T., Cho, M.Y., Alam, M.N. and Vu, Q.T., 2018. A novel differential particle swarm optimization for parameter selection of support vector machines for monitoring metal-oxide surge arrester conditions. Swarm and Evolutionary Computation, 38, pp.120-126. - 2018 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    3. Cheng, Min-Yuan, and Nhat-Duc Hoang. "Estimating construction duration of diaphragm wall using firefly-tuned least squares support vector machine." Neural Computing and Applications 30, no. 8 (2018): 2489-2497. - 2018 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    4. Herrera, José Elías Cancino, Ricardo Rodríguez Jorge, Osslan Osiris Vergara Villegas, Vianey Guadalupe Cruz Sánchez, Jiri Bila, Manuel de Jesús Nandayapa Alfaro, Israel U. Ponce, Ángel Israel Soto Marrufo, and Ángel Flores Abad. "Monitoring of cardiac arrhythmia patterns by adaptive analysis." In International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 885-894. Springer, Cham, 2016. - 2016 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    5. Dhar, J., Chatterjee, B., Maur, S., Biswas, S. and Dalai, S., 2023, November. Condition Assessment of Metal Oxide Surge Arrester Using Machine Learning Techniques. In 2023 IEEE 3rd Applied Signal Processing Conference (ASPCON) (pp. 166-170). IEEE. doi: 10.1109/ASPCON59071.2023.10396141 (Google Scholar) - 2023 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science