Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Geetabai S. Hukkeri"'
Autor:
Dhananjaya. G. M., R. H. Goudar, Anjanabhargavi A. Kulkarni, Vijayalaxmi N. Rathod, Geetabai S. Hukkeri
Publikováno v:
IEEE Access, Vol 12, Pp 34019-34041 (2024)
This review delves into using e-learning technology and personalized recommendation systems in education. It examines 60 articles from prominent databases and identifies the different methods used in recommendation systems, such as collaborative and
Externí odkaz:
https://doaj.org/article/0ee318eb0d934267a4e9a81ad4a213da
Autor:
Vijayalaxmi N. Rathod, R. H. Goudar, Anjanabhargavi Kulkarni, Dhananjaya G M, Geetabai S. Hukkeri
Publikováno v:
IEEE Access, Vol 12, Pp 11723-11732 (2024)
Autism, also known as Autism Spectrum Disorder (ASD) or Asperger’s syndrome, has an impact on cognition, social relationships, and behavior. Based on these characteristics and their learning interests, e-learning (Electronic Learning) recommendatio
Externí odkaz:
https://doaj.org/article/fbe0ce72a2cb4edc8089d65d814c38b3
Autor:
Geetabai S. Hukkeri, Sujay Raghavendra Naganna, Dayananda Pruthviraja, Nagaraj Bhat, R. H. Goudar
Publikováno v:
IEEE Access, Vol 11, Pp 141375-141393 (2023)
Depending on the severity and spatial-temporal variability, droughts can have a wide range of impacts such as crop failure, water shortages, and food insecurity. Accurate and timely forecasting is necessary to mitigate the hazards of extreme weather
Externí odkaz:
https://doaj.org/article/c0caeccdb260459c940bbf6ad3a969db
New approaches in federated learning and split learning have the potential to significantly improve ubiquitous intelligence in internet of things (IoT) applications. In split federated learning, the machine learning model is divided into smaller netw
Publikováno v:
2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT).
Nowadays, the enormous information has pulled in considerations from an ever-increasing number of specialists. The enormous information is characterized as the data set whose estimate is past the handling capacity of run-of-the-mill database. It may
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dd54610165e13ca3cf6fc02a5e1c3c2e
https://doi.org/10.1109/rteict46194.2019.9016846
https://doi.org/10.1109/rteict46194.2019.9016846