Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Sahaj A. Patel"'
Publikováno v:
Frontiers in Neuroinformatics, Vol 18 (2024)
Recently, graph theory has become a promising tool for biomedical signal analysis, wherein the signals are transformed into a graph network and represented as either adjacency or Laplacian matrices. However, as the size of the time series increases,
Externí odkaz:
https://doaj.org/article/cd4720b8a53a4e759dc577db74ed6324
Autor:
Sahaj Anilbhai Patel, Abidin Yildirim
Publikováno v:
Frontiers in Neuroinformatics. 17
This paper presents a time-efficient preprocessing framework that converts any given 1D physiological signal recordings into a 2D image representation for training image-based deep learning models. The non-stationary signal is rasterized into the 2D
Autor:
Sahaj Anilbhai Patel, Reepa Saha, S. Abdollah Mirbozorgi, Sabrin Samain, Kaushal Aishwarya, Rouzbeh Nazari, Maryam Karimi
Publikováno v:
2022 20th IEEE Interregional NEWCAS Conference (NEWCAS).
Publikováno v:
2022 IEEE International Symposium on Circuits and Systems (ISCAS).
Publikováno v:
American journal of stem cells. 7(4)
Genetic imprinting is the process of epigenetic labelling or silencing of particular genes, based on the maternal or paternal origin of the gene, in a heritable pattern. The incidence of imprinting disorders has become a growing concern due to the po
Autor:
Sahaj Anilbhai Patel, Abidin Yildirim
Publikováno v:
Signals, Vol 5, Iss 2, Pp 402-416 (2024)
(1) Problem Statement: The development of clustering algorithms for neural recordings has significantly evolved, reaching a mature stage with predominant approaches including partitional, hierarchical, probabilistic, fuzzy logic, density-based, and l
Externí odkaz:
https://doaj.org/article/ad45b9b0a6724930a2cb5d0e4c67e7f5
Autor:
Sahaj Anilbhai Patel, Abidin Yildirim
Publikováno v:
Journal of Imaging, Vol 10, Iss 5, p 121 (2024)
In graph theory, the weighted Laplacian matrix is the most utilized technique to interpret the local and global properties of a complex graph structure within computer vision applications. However, with increasing graph nodes, the Laplacian matrix’
Externí odkaz:
https://doaj.org/article/639a01ea0a4946ee8e527153781d7080