Zobrazeno 1 - 10
of 1 283
pro vyhledávání: '"Ranjan Rajiv"'
Publikováno v:
BIO Web of Conferences, Vol 86, p 01107 (2024)
The average age of the participants in this research, which evaluated the effects of public display advertising in smart cities, was found to be 31.2 years, with a gender distribution that is balanced. When compared to a prior review, exposure and me
Externí odkaz:
https://doaj.org/article/0022831d38f347ea9a6127a78125ab76
Publikováno v:
BIO Web of Conferences, Vol 86, p 01067 (2024)
This extensive experimental research provides strong empirical proof of the revolutionary power of deep learning algorithms when integrated into Industry 5.0. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Generative Adversarial
Externí odkaz:
https://doaj.org/article/70c42be59a744fd2a91509e97ad2bb72
Autor:
Orlov Alexandr K., Saxena Archana, Mittal Aman, Ranjan Rajiv, Singh Bhagat, Yellanki V. Sahithi
Publikováno v:
BIO Web of Conferences, Vol 86, p 01087 (2024)
Using a mixed-methods approach, we examine the complex link between user happiness and technology adoption in the context of smart homes. Our tests show that user happiness and adoption are highly influenced by the versions of smart home technologies
Externí odkaz:
https://doaj.org/article/9a65e1b3071c47738f0f30532a481221
This study investigates the relationship between sugarcane yield and cane height derived under different water and nitrogen conditions from pre-harvest Digital Surface Model (DSM) obtained via Unmanned Aerial Vehicle (UAV) flights over a sugarcane te
Externí odkaz:
http://arxiv.org/abs/2410.20880
Autor:
Shi, Xiufang, Zhang, Wei, Wu, Mincheng, Liu, Guangyi, Wen, Zhenyu, He, Shibo, Shah, Tejal, Ranjan, Rajiv
In federated learning, the heterogeneity of client data has a great impact on the performance of model training. Many heterogeneity issues in this process are raised by non-independently and identically distributed (Non-IID) data. This study focuses
Externí odkaz:
http://arxiv.org/abs/2409.17517
Publikováno v:
Managing Internet of Things Applications across Edge and Cloud Data Centres, IET, 2024
Since SLAs specify the contractual terms that are formally used between consumers and providers, there is a need to aggregate QoS requirements from the perspectives of Clouds, networks, and devices to deliver the promised IoT functionalities. Therefo
Externí odkaz:
http://arxiv.org/abs/2408.15013
Autor:
Liu, Zhenyu, Duan, Haoran, Liang, Huizhi, Long, Yang, Snasel, Vaclav, Nicosia, Guiseppe, Ranjan, Rajiv, Ojha, Varun
Publikováno v:
31st International Conference on Neural Information Processing (ICONIP), 2024
Adversarial training is one of the most effective methods for enhancing model robustness. Recent approaches incorporate adversarial distillation in adversarial training architectures. However, we notice two scenarios of defense methods that limit the
Externí odkaz:
http://arxiv.org/abs/2408.13102
Autor:
Duan, Haoran, Wang, Shidong, Ojha, Varun, Wang, Shizheng, Huang, Yawen, Long, Yang, Ranjan, Rajiv, Zheng, Yefeng
While traditional feature engineering for Human Activity Recognition (HAR) involves a trial-anderror process, deep learning has emerged as a preferred method for high-level representations of sensor-based human activities. However, most deep learning
Externí odkaz:
http://arxiv.org/abs/2405.15962
Text-to-3D content creation is a rapidly evolving research area. Given the scarcity of 3D data, current approaches often adapt pre-trained 2D diffusion models for 3D synthesis. Among these approaches, Score Distillation Sampling (SDS) has been widely
Externí odkaz:
http://arxiv.org/abs/2405.15914
We introduce a rehearsal-free federated domain incremental learning framework, RefFiL, based on a global prompt-sharing paradigm to alleviate catastrophic forgetting challenges in federated domain-incremental learning, where unseen domains are contin
Externí odkaz:
http://arxiv.org/abs/2405.13900