Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Guru Prasad M S"'
Publikováno v:
IET Image Processing, Vol 18, Iss 9, Pp 2449-2460 (2024)
Abstract In automatic facial expression recognition (AFER) systems, modelling the spatio‐temporal feature information in a specific manner, coalescing, and its effective utilization is challenging. The state‐of‐the‐art studies have examined i
Externí odkaz:
https://doaj.org/article/d73b44dc45a34806946616cc0c38025e
Publikováno v:
IET Image Processing, Vol 17, Iss 4, Pp 1111-1125 (2023)
Abstract Facial expression is one form of communication which being non‐verbal in nature precedes verbal communication in both origin and conception. Most of the existing methods for Automatic Facial Expression Recognition (AFER) are mainly focused
Externí odkaz:
https://doaj.org/article/8243da17466a47638558ee52232e48aa
Autor:
Chandrappa, S., Guru Prasad, M. S., Naveen Kumar, H. N., Pachnanda, Shubham, Sharath, K. R., Yeole, Ashwini Niteen
Publikováno v:
Journal of Nano- & Electronic Physics; 2024, Vol. 16 Issue 4, p1-5, 5p
Publikováno v:
IET Image Processing. 17:1111-1125
Publikováno v:
International Journal of Systems Assurance Engineering & Management; Jan2024, Vol. 15 Issue 1, p564-576, 13p
Publikováno v:
2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence).
Publikováno v:
2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence).
Publikováno v:
2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT).
Publikováno v:
2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N).
Frequent item mining is a process wherein we extract or mine frequent itemsets from a given input dataset. Apriori algorithms and FP-growth algorithms are two types of common pattern mining algorithms. Traditional implementations of such frequent ite
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb4b57660facca6811837f57ecb5125f
https://doi.org/10.21203/rs.3.rs-2314436/v1
https://doi.org/10.21203/rs.3.rs-2314436/v1