An Artificial Intelligence‐Driven Agent for Real‐Time Head‐and‐Neck IMRT Plan Generation using Conditional Generative Adversarial Network (cGAN)

2021 ◽  
Author(s):  
Xinyi Li ◽  
Chunhao Wang ◽  
Yang Sheng ◽  
Jiahan Zhang ◽  
Wentao Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Head And Neck ◽  
Real Time ◽  
Imrt Plan ◽  
Generative Adversarial Network ◽  
Plan Generation ◽  
Adversarial Network

scholarly journals An Artificial Intelligence-Driven Agent for Rapid Head-and-Neck IMRT Plan Generation

2020 ◽  
Vol 108 (3) ◽  
pp. e263
Author(s):  
C. Wang ◽  
X. Li ◽  
J. Zhang ◽  
Y. Sheng ◽  
F.F. Yin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Head And Neck ◽  
Imrt Plan ◽  
Plan Generation

scholarly journals GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 245
Author(s):  
Konstantinos G. Liakos ◽  
Georgios K. Georgakilas ◽  
Fotis C. Plessas ◽  
Paris Kitsos
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Power Analysis ◽  
Public Libraries ◽  
Data Sets ◽  
Hardware Trojan ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Encrypted Data ◽  
Adversarial Network

A significant problem in the field of hardware security consists of hardware trojan (HT) viruses. The insertion of HTs into a circuit can be applied for each phase of the circuit chain of production. HTs degrade the infected circuit, destroy it or leak encrypted data. Nowadays, efforts are being made to address HTs through machine learning (ML) techniques, mainly for the gate-level netlist (GLN) phase, but there are some restrictions. Specifically, the number and variety of normal and infected circuits that exist through the free public libraries, such as Trust-HUB, are based on the few samples of benchmarks that have been created from circuits large in size. Thus, it is difficult, based on these data, to develop robust ML-based models against HTs. In this paper, we propose a new deep learning (DL) tool named Generative Artificial Intelligence Netlists SynthesIS (GAINESIS). GAINESIS is based on the Wasserstein Conditional Generative Adversarial Network (WCGAN) algorithm and area–power analysis features from the GLN phase and synthesizes new normal and infected circuit samples for this phase. Based on our GAINESIS tool, we synthesized new data sets, different in size, and developed and compared seven ML classifiers. The results demonstrate that our new generated data sets significantly enhance the performance of ML classifiers compared with the initial data set of Trust-HUB.

scholarly journals AI detection of M. Tuberculosis pathogens using Generative Adversarial Network (GAN) analyses

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
M Dedicatoria ◽  
S Klaus ◽  
R Case ◽  
S Na ◽  
E Ludwick ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Mobile Computing ◽  
Initial Step ◽  
Rapid Identification ◽  
Training Data ◽  
Outbreak Detection ◽  
Outbreak Response ◽  
Generative Adversarial Network ◽  
Adversarial Network

Abstract Background Rapid identification of pathogens is critical to outbreak detection and sentinel surveillance; however most diagnoses are made in laboratory settings. Advancements in artificial intelligence (AI) and computer vision offer unprecedented opportunities to facilitate detection and reduce response time in field settings. An initial step is the creation of analysis algorithms for offline mobile computing applications. Methods AI models to identify objects using computer vision are typically “trained” on previously labeled images. The scarcity of labeled image-libraries creates a bottleneck, requiring thousands of labor hours to annotate images by hand to create “training data.” We describe the applicability of Generative Adversarial Network (GAN) methods to amass sufficient training data with minimal manual input. Results Our AI models are built with a performance score of 0.84-0.93 for M. Tuberculosis, a measure of the AI model's accuracy using precision and recall. Our results demonstrate that our GAN pipeline boosts model robustness and learnability of sparse open source data. Conclusions The use of labeled training data to identify M. Tuberculosis developed using our GAN pipeline techniques demonstrates the potential for rapid identification of known pathogens in field settings. Our work paves the way for the development of offline mobile computing applications to identify pathogens outside of a laboratory setting. Advancements in artificial intelligence (AI) and computer vision offer unprecedented opportunities to decrease detection time in field settings by combining these technologies. Further development of these capabilities can improve time-to-detection and outbreak response significantly. Key messages Rapidly deploy AI detectors to aid in disease outbreak and surveillance. Our concept aligns with deploying responsive alerting capabilities to address dynamic threats in low resource, offline computing environs.

scholarly journals floodGAN: Using Deep Adversarial Learning to Predict Pluvial Flooding in Real Time

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2255
Author(s):  
Julian Hofmann ◽  
Holger Schüttrumpf
Keyword(s):  
Real Time ◽  
Hydrodynamic Model ◽  
Early Warning Systems ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Flood Prediction ◽  
Adversarial Network ◽  
Pluvial Flooding ◽  
Physically Based ◽  
Fully Connected

Using machine learning for pluvial flood prediction tasks has gained growing attention in the past years. In particular, data-driven models using artificial neuronal networks show promising results, shortening the computation times of physically based simulations. However, recent approaches have used mainly conventional fully connected neural networks which were (a) restricted to spatially uniform precipitation events and (b) limited to a small amount of input data. In this work, a deep convolutional generative adversarial network has been developed to predict pluvial flooding caused by nonlinear spatial heterogeny rainfall events. The model developed, floodGAN, is based on an image-to-image translation approach whereby the model learns to generate 2D inundation predictions conditioned by heterogenous rainfall distributions—through the minimax game of two adversarial networks. The training data for the floodGAN model was generated using a physically based hydrodynamic model. To evaluate the performance and accuracy of the floodGAN, model multiple tests were conducted using both synthetic events and a historic rainfall event. The results demonstrate that the proposed floodGAN model is up to 106 times faster than the hydrodynamic model and promising in terms of accuracy and generalizability. Therefore, it bridges the gap between detailed flood modelling and real-time applications such as end-to-end early warning systems.

scholarly journals Computational analysis of transition temperatures (Tts) of proteins fused to elastin-like polypeptide (ELP): deep fake evaluation of proteins, linkers, and trailers features

2021 ◽  
Author(s):  
Mohammad Davoud Ghafari ◽  
Iraj Rasooli ◽  
Khosro Khajeh ◽  
Bahareh Dabirmanesh ◽  
Mohammadreza Ghafari ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Phase Transition ◽  
Transition Temperature ◽  
Computational Analysis ◽  
Large Data ◽  
High Accuracy ◽  
Sequence Length ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Protein Feature

The phase transition temperature (Tt) prediction of the Elastin-like polypeptides (ELPs) is not trivial because it is related to complex sets of variables such as composition, sequence length, hydrophobic characterization, hydrophilic characterization, the sequence order in the fused proteins, linkers and trailer constructs. In this paper, two unique quantitative models are presented for the prediction of the Tt of a family of ELPs that could be fused to different proteins, linkers, and trailers. The lack of need to use multiple software, peptide information, such as PDB file, as well as knowing the second and third structures of proteins are the advantages of this model besides its high accuracy and speed. One of our models could predict the Tt values of the fused ELPs by entering the protein, linker, and trailer features with R2=99%. Also, another model is able to predict the Tt value by entering the fused protein feature with R2=96%. For more reliability, our method is enriched by Artificial Intelligence (AI) to generate similar proteins. In this regard, Generative Adversarial Network (GAN) is our AI method to create fake proteins and similar values. The experimental results show that our strategy for prediction of Tt is reliable in large data.

scholarly journals Upsampling Real-Time, Low-Resolution CCTV Videos Using Generative Adversarial Networks

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1312
Author(s):  
Debapriya Hazra ◽  
Yung-Cheol Byun
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Super Resolution ◽  
Poor Quality ◽  
Generative Adversarial Networks ◽  
Temporal Consistency ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Benchmark Datasets

Video super-resolution has become an emerging topic in the field of machine learning. The generative adversarial network is a framework that is widely used to develop solutions for low-resolution videos. Video surveillance using closed-circuit television (CCTV) is significant in every field, all over the world. A common problem with CCTV videos is sudden video loss or poor quality. In this paper, we propose a generative adversarial network that implements spatio-temporal generators and discriminators to enhance real-time low-resolution CCTV videos to high-resolution. The proposed model considers both foreground and background motion of a CCTV video and effectively models the spatial and temporal consistency from low-resolution video frames to generate high-resolution videos. Quantitative and qualitative experiments on benchmark datasets, including Kinetics-700, UCF101, HMDB51 and IITH_Helmet2, showed that our model outperforms the existing GAN models for video super-resolution.

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