Deep Learning for Multimedia Data in IoT

Hiriyannaiah, Srinidhi; Akanksh, B. S.; Koushik, Abhay; Siddesh, G M; Srinivasa, K. G.

doi:10.1007/978-981-13-8759-3_4

Cited by 9 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even if the learning rate is faster, it is not necessarily possible to really learn useful features. So gradient vanish is more likely to occur in deep neural networks (Høye et al 2021).…”

Section: Gradient Vanishmentioning

confidence: 99%

“…In the field of computer, multimedia is a technology that manages language, text, audio, and video information by computer. In this process, users can constantly rely on a variety of sensory and computer for real-time information exchange (Hiriyannaiah et al 2020). As a way of communication, multimedia communication based on human-computer interaction is particularly important.…”

Section: Multimediamentioning

confidence: 99%

See 1 more Smart Citation

The application of evolutionary computation in generative adversarial networks (GANs): a systematic literature survey

Wang,

Zhang,

Wang

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

As a subfield of deep learning (DL), generative adversarial networks (GANs) have produced impressive generative results by applying deep generative models to create synthetic data and by performing an adversarial training process. Nevertheless, numerous issues related to the instability of training need to be urgently addressed. Evolutionary computation (EC), using the corresponding paradigm of biological evolution, overcomes these problems and improves evolutionary-based GANs’ ability to deal with real-world applications. Therefore, this paper presents a systematic literature survey combining EC and GANs. First, the basic theories of GANs and EC are analyzed and summarized. Second, to provide readers with a comprehensive view, this paper outlines the recent advances in combining EC and GANs after detailed classification and introduces each of them. These classifications include evolutionary GANs and their variants, GANs with evolutionary strategies and differential evolution, GANs combined with neuroevolution, evolutionary GANs related to different optimization problems, and applications of evolutionary GANs. Detailed information on the evaluation metrics, network structures, and comparisons of these models is presented in several tables. Finally, future directions and possible perspectives for further development are discussed.

show abstract

“…Even if the learning rate is faster, it is not necessarily possible to really learn useful features. So gradient vanish is more likely to occur in deep neural networks (Høye et al 2021).…”

Section: Gradient Vanishmentioning

confidence: 99%

Section: Multimediamentioning

confidence: 99%

The application of evolutionary computation in generative adversarial networks (GANs): a systematic literature survey

Wang,

Zhang,

Wang

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…One could also think that the institutional challenges of companies, universities and scientific networks would be to have access to high-performance clusters, specialized software and other computing resources for the development of projects, within an efficient, quality and relevance scheme ( Hiriyannaiah et al., 2020 ). However, the most decisive challenges and resolutions go beyond these practical complications.…”

Section: The Ethical Challenges For Information Sciencesmentioning

confidence: 99%

Towards an ethical framework about Big Data era: metaethical, normative ethical and hermeneutical approaches

Reyes¹

2022

Heliyon

View full text Add to dashboard Cite

“…The explosive growth of the web computing data seriously challenges to traditional path protocols (Li et al , 2020). Traditional path protocols such as OSPF, IS-IS and RIP (Hedrick, 1988; Hiriyannaiah et al , 2020; Ali et al , 2020) are based on the principle of calculating the shortest path (Mahenge et al , 2019) for data transmission, they do not consider the remaining cache size and other information of each router in path selection. When the data volume increases sharply, one or more path devices are simultaneously selected by multiple data transmission tasks, this condition may happen, which will cause network data congestion, reduce network throughput and increase data transmission delay.…”

Section: Introductionmentioning

confidence: 99%

Optimal path strategy for the web computing under deep reinforcement learning

Shengdong

Wang

Zheng-xian

et al. 2020

IJWIS

View full text Add to dashboard Cite

Purpose With the advent of the web computing era, the transmission mode of the Internet of Everything has caused an explosion in data volume, which has brought severe challenges to traditional routing protocols. The limitations of the existing routing protocols under the condition of rapid data growth are elaborated, and the routing problem is remodeled as a Markov decision process. this paper aims to solve the problem of high blocking probability due to the increase in data volume by combining deep reinforcement learning. Finally, the correctness of the proposed algorithm in this paper is verified by simulation. Design/methodology/approach The limitations of the existing routing protocols under the condition of rapid data growth are elaborated and the routing problem is remodeled as a Markov decision process. Based on this, a deep reinforcement learning method is used to select the next-hop router for each data transmission task, thereby minimizing the length of the data transmission path while avoiding data congestion. Findings Simulation results show that the proposed method can significantly reduce the probability of data congestion and increase network throughput. Originality/value This paper proposes an intelligent routing algorithm for the network congestion caused by the explosive growth of data volume in the future of the big data era. With the help of deep reinforcement learning, it is possible to dynamically select the transmission jump router according to the current network state, thereby reducing the probability of congestion and improving network throughput.

show abstract

Deep Learning for Multimedia Data in IoT

Cited by 9 publications

References 8 publications

The application of evolutionary computation in generative adversarial networks (GANs): a systematic literature survey

The application of evolutionary computation in generative adversarial networks (GANs): a systematic literature survey

Towards an ethical framework about Big Data era: metaethical, normative ethical and hermeneutical approaches

Optimal path strategy for the web computing under deep reinforcement learning

Contact Info

Product

Resources

About