A Semi-Supervised Generative Adversarial Network for Prediction of Genetic Disease Outcomes

Davi, Caio; Braga-Neto, Ulisses

doi:10.1109/mlsp52302.2021.9596351

Cited by 2 publications

(1 citation statement)

References 18 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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An Overview of Deep Generative Models in Functional and Evolutionary Genomics

Yelmen

Jay

2023

Annu. Rev. Biomed. Data Sci.

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Following the widespread use of deep learning for genomics, deep generative modeling is also becoming a viable methodology for the broad field. Deep generative models (DGMs) can learn the complex structure of genomic data and allow researchers to generate novel genomic instances that retain the real characteristics of the original dataset. Aside from data generation, DGMs can also be used for dimensionality reduction by mapping the data space to a latent space, as well as for prediction tasks via exploitation of this learned mapping or supervised/semisupervised DGM designs. In this review, we briefly introduce generative modeling and two currently prevailing architectures, we present conceptual applications along with notable examples in functional and evolutionary genomics, and we provide our perspective on potential challenges and future directions. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 6 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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A Semi-Supervised Generative Adversarial Network for Prediction of Genetic Disease Outcomes

Cited by 2 publications

References 18 publications

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

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

An Overview of Deep Generative Models in Functional and Evolutionary Genomics

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