Zobrazeno 1 - 10
of 29
pro vyhledávání: '"S. Thejas"'
Autor:
Kavitha Dhanushkodi, S. Thejas
Publikováno v:
IEEE Access, Vol 12, Pp 173127-173136 (2024)
This comprehensive review examines the role of artificial intelligence (AI) in enhancing threat detection and cybersecurity, focusing on recent advancements and ongoing challenges in this dynamic field. The ability to identify and counteract cybersec
Externí odkaz:
https://doaj.org/article/6aaf54ea1040494db0a7fb20ead71f2c
Autor:
G. S. Thejas, Rameshwar Garg, S. S. Iyengar, N. R. Sunitha, Prajwal Badrinath, Shasank Chennupati
Publikováno v:
IEEE Access, Vol 9, Pp 128687-128701 (2021)
Feature selection has emerged as a craft, using which we boost the performance of our learning model. Feature or Attribute Selection is a data preprocessing technique, where only the most informative features are considered and given to the predictor
Externí odkaz:
https://doaj.org/article/1628312443244aec8c20351d0e866fe0
Publikováno v:
IEEE Access, Vol 7, Pp 116875-116885 (2019)
Feature Selection has been a significant preprocessing procedure for classification in the area of Supervised Machine Learning. It is mostly applied when the attribute set is very large. The large set of attributes often tend to misguide the classifi
Externí odkaz:
https://doaj.org/article/766b667e61104e6c8500828434e9c06e
Publikováno v:
International Journal of Environment and Climate Change. 13:123-131
A field experiment was conducted during Zaid 2022 at Crop Research Farm, Department of Agronomy, SHUATS, Prayagraj (U.P) to determine the “Influence of micronutrients and naphthalene acetic acid on growth, yield and economics of summer greengram (V
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::005d0080cd11afe7a3aa759917560c38
https://doi.org/10.9734/ijecc/2023/v13i51753
https://doi.org/10.9734/ijecc/2023/v13i51753
Publikováno v:
International Journal of Environment and Climate Change. 13:114-122
A field experiment was conducted during Zaid 2022 at Crop Research Farm, Department of Agronomy, SHUATS, Prayagraj (U.P) to determine the “Effect of different levels of potassium and zinc on growth, yield and economics of summer groundnut (Arachis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::49f2e26dcebc0254f47b6121e5fe2598
https://doi.org/10.9734/ijecc/2023/v13i51752
https://doi.org/10.9734/ijecc/2023/v13i51752
Autor:
Kavitha, D., S., Thejas
Publikováno v:
Grenze International Journal of Engineering & Technology (GIJET); Jan2024, Vol. 10 Issue 1, Part 3, p2641-2648, 8p
Publikováno v:
Journal of Electronic Materials. 51:938-952
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::04075b1e17d369af4bf858daa090b27d
https://doi.org/10.1007/s11664-021-09346-0
https://doi.org/10.1007/s11664-021-09346-0
Publikováno v:
Ferroelectrics. 582:46-62
Lead free piezo materials are becoming popular as the world is conscious about toxicity of PbO present in PZT. As a result, lot of R&D efforts are made in last two decades to find out suitable lead...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::66b9134731e435fca237fa439fd1e9d2
https://doi.org/10.1080/00150193.2021.1951034
https://doi.org/10.1080/00150193.2021.1951034
Autor:
Prajwal Badrinath, N. R. Sunitha, Rameshwar Garg, G S Thejas, Shasank Chennupati, S. Sitharama Iyengar
Publikováno v:
IEEE Access, Vol 9, Pp 128687-128701 (2021)
Feature selection has emerged as a craft, using which we boost the performance of our learning model. Feature or Attribute Selection is a data preprocessing technique, where only the most informative features are considered and given to the predictor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fadcb9628fbf5620548d91f6dbb76879
https://ieeexplore.ieee.org/document/9536707/
https://ieeexplore.ieee.org/document/9536707/
Publikováno v:
IEEE Access, Vol 7, Pp 116875-116885 (2019)
Feature Selection has been a significant preprocessing procedure for classification in the area of Supervised Machine Learning. It is mostly applied when the attribute set is very large. The large set of attributes often tend to misguide the classifi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8cf23f2f26d4ae7e9efec06dcb970048
https://ieeexplore.ieee.org/document/8805315/
https://ieeexplore.ieee.org/document/8805315/