Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Siddhaling Urolagin"'
Publikováno v:
IEEE Access, Vol 12, Pp 117627-117649 (2024)
Histopathological image classification stands as a cornerstone in the pathological diagnosis workflow, yet it remains challenging due to the inherent complexity of histopathological images. Recently, transformers and convolutional neural network (CNN
Externí odkaz:
https://doaj.org/article/d057b722880342d4b00943ec84fe834a
Publikováno v:
International Journal of Information Management Data Insights, Vol 3, Iss 1, Pp 100145- (2023)
The Users regularly share their views on services through internet reviews. Digital tourism allows travelers to plan and manage their trips ahead of time using the internet. The proposed system will enable users to create recommender systems that can
Externí odkaz:
https://doaj.org/article/d9c061dd462f4b7ea6e8e701f832282d
Publikováno v:
IEEE Access, Vol 10, Pp 132455-132471 (2022)
Social media contains a plethora of information in the form of text, images, videos, and other data. Users across the globe are increasingly sharing their data on various social media platforms. Sentiment analysis of data, such as text, images, and v
Externí odkaz:
https://doaj.org/article/665c0122d42c4a4b95242746fe69d8ce
Publikováno v:
Journal of Imaging, Vol 9, Iss 4, p 78 (2023)
Histopathology image analysis is considered as a gold standard for the early diagnosis of serious diseases such as cancer. The advancements in the field of computer-aided diagnosis (CAD) have led to the development of several algorithms for accuratel
Externí odkaz:
https://doaj.org/article/8c72d7c9a2704b02a9d63f064ecea5e9
Autor:
Shubhangi A. Joshi, Anupkumar M. Bongale, P. Olof Olsson, Siddhaling Urolagin, Deepak Dharrao, Arunkumar Bongale
Publikováno v:
Computation, Vol 11, Iss 3, p 59 (2023)
Early detection and timely breast cancer treatment improve survival rates and patients’ quality of life. Hence, many computer-assisted techniques based on artificial intelligence are being introduced into the traditional diagnostic workflow. This i
Externí odkaz:
https://doaj.org/article/f53068be01174f9a9fc62da1d333d354
Autor:
Ram Krishn Mishra, Siddhaling Urolagin, J. Angel Arul Jothi, Ashwin Sanjay Neogi, Nishad Nawaz
Publikováno v:
Frontiers in Computer Science, Vol 3 (2021)
The Covid-19 pandemic has disrupted the world economy and significantly influenced the tourism industry. Millions of people have shared their emotions, views, facts, and circumstances on numerous social media platforms, which has resulted in a massiv
Externí odkaz:
https://doaj.org/article/da63d87333934d989a352c3a04a1c2d7
Publikováno v:
International Journal of Information Management Data Insights, Vol 1, Iss 2, Pp 100036- (2021)
With the fast improvement and development in deep learning and computer vision, the interaction between humans and computers is becoming immense. This research work aims at recognizing hand gestures performed in TEDx videos and analyse the relationsh
Externí odkaz:
https://doaj.org/article/e80a82e6b3af4e9a828b94e29ef1615d
Publikováno v:
Applied Sciences, Vol 12, Iss 16, p 8105 (2022)
The tourism industry has experienced fast and sustainable growth over the years in the economic sector. The data available online on the ever-growing tourism sector must be given importance as it provides crucial economic insights, which can be helpf
Externí odkaz:
https://doaj.org/article/d3b5397ca18a4465b52011e6644a5ace
Publikováno v:
PLoS ONE, Vol 10, Iss 2, p e0117380 (2015)
More reliable and faster prediction methods are needed to interpret enormous amounts of data generated by sequencing and genome projects. We have developed a new computational tool, PON-P2, for classification of amino acid substitutions in human prot
Externí odkaz:
https://doaj.org/article/bcacedc019de410aa31c57a547d7dd06
Autor:
Siddhaling Urolagin, Saifali Patel
Publikováno v:
IEEE Transactions on Computational Social Systems. :1-16
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::974a13ad35fb8f139a5e2a27f3bd1b8d
https://doi.org/10.1109/tcss.2023.3239523
https://doi.org/10.1109/tcss.2023.3239523