Data recovery algorithm based on generative adversarial networks in crowd sensing Internet of Things

Shi, Yushi; Zhang, Xiaoqi; Hu, Qiaohong; Cheng, Hongju

doi:10.1007/s00779-020-01428-w

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…The GAN networks are found to be highly capable of producing plausible results across a wider range of diverse domain applications. The applications such as security [54,55], baggage inspection [56], infected leaf identification [57], covid-19 prediction [58], agriculture [59], business process monitoring [60], Brain MRI synthesis [61], flood visualization [62], estimating the standards of gold [63], ECG wave synthesis [64], Internet of Things (IoT) [65] and Dengue Fever sampling [66].…”

Section: Table 1: Comparison Of Existing Sl Recognition Frameworkmentioning

confidence: 99%

Development of an End-to-End Deep Learning Framework for Sign Language Recognition, Translation, and Video Generation

et al. 2022

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The recent developments in deep learning techniques evolved to new heights in various domains and applications. The recognition, translation, and video generation of Sign Language (SL) still face huge challenges from the development perspective. Although numerous advancements have been made in earlier approaches, the model performance still lacks recognition accuracy and visual quality. In this paper, we introduce novel approaches for developing the complete framework for handling SL recognition, translation, and production tasks in real-time cases. To achieve higher recognition accuracy, we use the MediaPipe library and a hybrid Convolutional Neural Network + Bi-directional Long Short Term Memory (CNN + Bi-LSTM) model for pose details extraction and text generation. On the other hand, the production of sign gesture videos for given spoken sentences is implemented using a hybrid Neural Machine Translation (NMT) + MediaPipe + Dynamic Generative Adversarial Network (GAN) model. The proposed model addresses the various complexities present in the existing approaches and achieves above 95% classification accuracy. In addition to that, the model performance is tested in various phases of development, and the evaluation metrics show noticeable improvements in our model. The model has been experimented with using different multilingual benchmark sign corpus and produces greater results in terms of recognition accuracy and visual quality. The proposed model has secured a 38.06 average Bilingual Evaluation Understudy (BLEU) score, remarkable human evaluation scores, 3.46 average Fréchet Inception Distance to videos (FID2vid) score, 0.921 average Structural Similarity Index Measure (SSIM) values, 8.4 average Inception Score, 29.73 average Peak Signal-to-Noise Ratio (PSNR) score, 14.06 average Fréchet Inception Distance (FID) score, and an average 0.715 Temporal Consistency Metric (TCM) Score which is evidence of the proposed work.

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Section: Table 1: Comparison Of Existing Sl Recognition Frameworkmentioning

confidence: 99%

Development of an End-to-End Deep Learning Framework for Sign Language Recognition, Translation, and Video Generation

et al. 2022

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“…In [20] a convolution neural network has been utilized for generating data recovery algorithms. For restoring the data which mainly includes two steps, all sensed collected for the training process to the networks and data recovery has been initiated with the help of a trained generator.An redundant residue number system algorithm [21], Max-flow algorithm fault tolerant [22], Device pairing algorithm [23], Finding Least connected points algorithm [24], Least connected neighbour algorithm [25] are the few fault-tolerant algorithms have been studied.…”

Section: Related Workmentioning

confidence: 99%

Data Recovery Approach for Fault-Tolerant IoT Node

Vedavalli¹,

Ch²

2022

IJACSA

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Internet of Things (IoT) has a wide range of applications in many sectors like industries, health care, homes, militarily, and agriculture. Especially IoT-based safety and critical applications must be more securable and reliable. Such type of applications needs to be operated continuously even in the presence of errors and faults. In safety and critical IoT applications maintaining data reliability and security is the critical task. IoT suffers from node failures due to limited resources and the nature of deployment which results in data loss consequently. This paper proposes a Data Recovery Approach for Fault Tolerant (DRAFT) IoT node algorithm, which is fully distributed, data replication and recovery implemented through redundant local database storage of other nodes in the network. DRAFT ensures high data availability even in the presence of node failures to preserve the data. When an IoT node fails in any cluster in the network data can be retrieved through redundant storage with the help of neighbor nodes in the cluster. The proposed algorithm is simulated for 100-150 IoT nodes which enhances 5% of network lifetime, and throughput. The performance metrics such as Mean Time to Data Loss (MTTDL), throughput, Network lifetime, and reliability are computed and results are found to be improved.

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“…In the data utilizing phase, Shi et al [7] introduced a data recovery algorithm based on generative adversarial networks. The convolution neural network is used as the basic model of this algorithm.…”

Section: Accepted Articlesmentioning

confidence: 99%