Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Deepak Bhaskar Acharya"'
Publikováno v:
IEEE Access, Vol 12, Pp 181282-181302 (2024)
This paper provides a comprehensive survey on the applications, challenges, and future directions of Artificial Intelligence (AI) in the insurance and real estate sectors. We explore key AI-driven solutions, such as advanced risk assessment, predicti
Externí odkaz:
https://doaj.org/article/5f714c7abe9b4653b09fdedb00550982
Publikováno v:
IEEE Access, Vol 12, Pp 154022-154034 (2024)
This paper introduces a framework that integrates fairness and transparency into advanced machine learning models, specifically LightGBM and XGBoost, applied to loan approval and house price prediction datasets. The key contribution is using fairness
Externí odkaz:
https://doaj.org/article/1ac4a2290f3f481ea1a58eb9948d6707
Autor:
Huaming Zhang, Deepak Bhaskar Acharya
Publikováno v:
SN Computer Science. 2
Finding useful patterns in the dataset has been a fascinating topic, and one of the most researched problems in this area is identifying the cluster groups within the dataset. This research paper introduces a “new data clustering method” called D
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b2ba49462697311339422d34630a79f9
https://doi.org/10.1007/s42979-021-00707-4
https://doi.org/10.1007/s42979-021-00707-4
Autor:
Deepak Bhaskar Acharya, Huaming Zhang
Recently, in many systems such as speech recognition and visual processing, deep learning has been widely implemented. In this research, we are exploring the possibility of using deep learning in community detection among the graph datasets. Graphs h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd0b6310a240e3a5ec87a60b38d2e7eb
http://arxiv.org/abs/2005.02372
http://arxiv.org/abs/2005.02372
Autor:
Huaming Zhang, Deepak Bhaskar Acharya
Publikováno v:
ACM Southeast Regional Conference
Graph Neural Networks (GNNs) have been a latest hot research topic in data science, due to the fact that they use the ubiquitous data structure graphs as the underlying elements for constructing and training neural networks. In a GNN, each node has n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3a9321b225a9d2eed4024d01e3be1a8
https://doi.org/10.1145/3374135.3385309
https://doi.org/10.1145/3374135.3385309