Zobrazeno 1 - 10
of 278
pro vyhledávání: '"Mathukumalli Vidyasagar"'
Autor:
Mathukumalli Vidyasagar
Publikováno v:
SICE Journal of Control, Measurement, and System Integration, Vol 16, Iss 1, Pp 172-191 (2023)
In this paper, we present a brief survey of reinforcement learning, with particular emphasis on stochastic approximation (SA) as a unifying theme. The scope of the paper includes Markov reward processes, Markov decision processes, SA algorithms, and
Externí odkaz:
https://doaj.org/article/8e4ee9a0515949188c327645dab0c284
Autor:
Richa Bansal, Sathiakar Collison, Lakshmi Krishnan, Bharat Aggarwal, Mathukumalli Vidyasagar, Siva Teja Kakileti, Geetha Manjunath
Publikováno v:
Frontiers in Artificial Intelligence, Vol 5 (2023)
ObjectiveArtificial intelligence-enhanced breast thermography is being evaluated as an ancillary modality in the evaluation of breast disease. The objective of this study was to evaluate the clinical performance of Thermalytix, a CE-marked, AI-based
Externí odkaz:
https://doaj.org/article/e50c5fbf4f2848b2a8120ae24bca8099
A Review on the Challenges in Indian Genomics Research for Variant Identification and Interpretation
Autor:
Sandhya Kiran Pemmasani, Rasika Raman, Rajkishore Mohapatra, Mathukumalli Vidyasagar, Anuradha Acharya
Publikováno v:
Frontiers in Genetics, Vol 11 (2020)
Today, genomic data holds great potential to improve healthcare strategies across various dimensions – be it disease prevention, enhanced diagnosis, or optimized treatment. The biggest hurdle faced by the medical and research community in India is
Externí odkaz:
https://doaj.org/article/688d62e3f6bd4b698a56b60a355eb9b6
Autor:
Mehmet Eren Ahsen, Todd P. Boren, Nitin K. Singh, Burook Misganaw, David G. Mutch, Kathleen N. Moore, Floor J. Backes, Carolyn K. McCourt, Jayanthi S. Lea, David S. Miller, Michael A. White, Mathukumalli Vidyasagar
Publikováno v:
BMC Genomics, Vol 18, Iss S3, Pp 1-12 (2017)
Abstract Background Metastasis via pelvic and/or para-aortic lymph nodes is a major risk factor for endometrial cancer. Lymph-node resection ameliorates risk but is associated with significant co-morbidities. Incidence in patients with stage I diseas
Externí odkaz:
https://doaj.org/article/5685153e23b84181b171b36625789c9b
Publikováno v:
Genomics & Informatics, Vol 11, Iss 1, Pp 55-57 (2013)
Growing numbers of studies employ cell line-based systematic short interfering RNA (siRNA) screens to study gene functions and to identify drug targets. As multiple sources of variations that are unique to siRNA screens exist, there is a growing dema
Externí odkaz:
https://doaj.org/article/e1c93eef5eff4aa1b8c044523fb35628
Publikováno v:
IEEE Control Systems Letters. 6:1916-1921
Autor:
Siva Teja Kakileti, Raghav Shrivastava, Geetha Manjunath, Mathukumalli Vidyasagar, Axel Graewingholt
Publikováno v:
Journal of medical imaging (Bellingham, Wash.). 9(4)
Autor:
Mathukumalli Vidyasagar, Mahsa Lotfi
Publikováno v:
IEEE Transactions on Signal Processing. 68:3008-3021
In this paper, we study the problem of compressed sensing using binary measurement matrices and $\ell_1$-norm minimization (basis pursuit) as the recovery algorithm. We derive new upper and lower bounds on the number of measurements to achieve robust
Publikováno v:
IFAC-Papers
The SARS-Cov-2 is a type of coronavirus that has caused the COVID-19 pandemic. In traditional epidemiological models such as SEIR (Susceptible, Exposed, Infected, Removed), the exposed group E does not infect the susceptible group S. A distinguishing
Publikováno v:
IEEE Transactions on Signal Processing. 68:3834-3848
In this paper we study the matrix completion problem: Suppose $X \in {\mathbb R}^{n_r \times n_c}$ is unknown except for a known upper bound $r$ on its rank. By measuring a small number $m \ll n_r n_c$ of elements of $X$, is it possible to recover $X