Zobrazeno 1 - 10
of 83
pro vyhledávání: '"Devarani Devi"'
Publikováno v:
ETRI Journal, Vol 45, Iss 6, Pp 1007-1021 (2023)
Stroke is the leading cause of permanent disability in adults, and it can cause permanent brain damage. According to the World Health Organization, 795,000 Americans experience a new or recurrent stroke each year. Early detection of medical disorders
Externí odkaz:
https://doaj.org/article/37fa72ae74ba4745af3a49512402d62e
Autor:
Radhe Shyam, Deepak Negi, Komal Shekhawat, Fouran Singh, Devarani Devi, Pargam Vashishtha, Govind Gupta, Subingya Pandey, Pamu Dobbidi, Srinivasa Rao Nelamarri
Publikováno v:
Results in Physics, Vol 47, Iss , Pp 106330- (2023)
The present study demonstrates the tuning of structural, topography, and luminescence properties of 30 keV Li-ion implanted (K,Na)NbO3 thin films synthesized using RF sputtering. The Li-ion implantation of KNN films was carried out at different ion f
Externí odkaz:
https://doaj.org/article/fcaa7aebd5fd4496a29ae7cf39ce44e6
Autor:
Byunghyun Yoo, Devarani Devi Ningombam, Sungwon Yi, Hyun Woo Kim, Euisok Chung, Ran Han, Hwa Jeon Song
Publikováno v:
IEEE Access, Vol 10, Pp 47741-47753 (2022)
Although recent years witnessed notable success for a cooperative setting in multi-agent reinforcement learning (MARL), efficient explorations are still challenging primarily due to the complex dynamics of inter-agent interactions constituting the hi
Externí odkaz:
https://doaj.org/article/2c52b19cd0694122b162961de7740579
Publikováno v:
IEEE Access, Vol 10, Pp 87254-87265 (2022)
In this paper, we propose a novel curiosity-based learning algorithm for Multi-agent Reinforcement Learning (MARL) to attain efficient and effective decision-making. We employ the centralized training with decentralized execution framework (CTDE) and
Externí odkaz:
https://doaj.org/article/ea341c98ba52493b8fe43c46f77bd8a6
Autor:
Hafiz Muhammad Raza Ur Rehman, Byung-Won On, Devarani Devi Ningombam, Sungwon Yi, Gyu Sang Choi
Publikováno v:
IEEE Access, Vol 9, Pp 129728-129741 (2021)
When individuals interact with one another to accomplish specific goals, they learn from others’ experiences to achieve the tasks at hand. The same holds for learning in virtual environments, such as video games. Deep multiagent reinforcement learn
Externí odkaz:
https://doaj.org/article/68f0b78038fa4f6abfdf228735c49cfa
Autor:
Devarani Devi Ningombam, Seokjoo Shin
Publikováno v:
Sensors, Vol 20, Iss 4, p 1128 (2020)
In the last few years, multicast device-to-device (D2D) cellular networks has become a highly attractive area of research. However, a particularly challenging class of issues in this area is data traffic, which increases due to increase in video and
Externí odkaz:
https://doaj.org/article/f2d9e422f9a041e6851ddd0f174fd82e
Autor:
Devarani Devi Ningombam, Seokjoo Shin
Publikováno v:
Sensors, Vol 19, Iss 2, p 251 (2019)
Device-to-device (D2D) communications can be adopted as a promising solution to attain high quality of service (QoS) for a network. However, D2D communications generates harmful interference when available resources are shared with traditional cellul
Externí odkaz:
https://doaj.org/article/fd3ca4fd71cb43a888395acae23e2c37
Autor:
Devarani Devi Ningombam, Seokjoo Shin
Publikováno v:
Computers, Vol 7, Iss 4, p 50 (2018)
Device-to-device (D2D) communication is affirmed as one of the dynamic techniques in improving the network throughput and capacity and reducing traffic load to the evolved Node B (eNB). In this paper, we propose a resource allocation and power contro
Externí odkaz:
https://doaj.org/article/16e54bfd4fc549f4ad7ddad784044b1b
Publikováno v:
In Thin Solid Films 15 September 2024 804
Publikováno v:
In Materials Today Communications August 2024 40