Results 151 to 160 of about 3,042,897 (294)

Image Denoising Using Quantum Deep Convolutional Generative Adversarial Network for Medical Images

International Journal of Computational Intelligence Systems
A significant role is played by medical images in diagnosing diseases and planning the course of treatment. Noise can potentially degrade the quality of images which can lead to misdiagnosis.
Priyanka Nandal, Sudesh Pahal, Govind Murari Upadhyay   +2 more
doaj   +1 more source

Advancing student outcome predictions through generative adversarial networks

Computers and Education: Artificial Intelligence
Predicting student outcomes is essential in educational analytics for creating personalised learning experiences. The effectiveness of these predictive models relies on having access to sufficient and accurate data. However, privacy concerns and the lack
Helia Farhood, Ibrahim Joudah, Amin Beheshti, Samuel Muller   +3 more
doaj   +1 more source

Font Creation Using Class Discriminative Deep Convolutional Generative Adversarial Networks

, 2017
In this research, we attempt to generate fonts automatically using a modification of a Deep Convolutional Generative Adversarial Network (DCGAN) by introducing class consideration. DCGANs are the application of generative adversarial networks (GAN) which
Seiichi Uchida, Brian Kenji Iwana, Iwana, B. K., Viktor Gosta Holmer, Abe, K., Uchida, S., Holmer, Viktor Gösta,, Kotaro Abe   +7 more
core   +1 more source

Machine Learning‐Enabled Field Prediction and Inverse Design of Functionally Graded Materials Inspired by Tendon–Bone Enthesis

Advanced Intelligent Systems, EarlyView.
A mechanics‐informed machine learning framework enables mechanics field prediction and inverse design of tendon–bone enthesis‐inspired functionally graded materials that reduce stress concentration and failure risk. By coupling multiscale finite element modeling, convolutional field prediction, and kernel‐based inverse design, the study uncovers ...
Zhangke Yang, Zhaoxu Meng
wiley   +1 more source

RETRACTED: Sankar et al. Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology. BioMedInformatics 2024, 4, 1059–1070

BioMedInformatics
The journal retracts the article, “Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology” [...]
Aravinthan Sankar, Kunal Chaturvedi, Al-Akhir Nayan, Mohammad Hesam Hesamian, Ali Braytee, Mukesh Prasad   +5 more
doaj   +1 more source

DyCoDiff: Dynamic Collaborative Diffusion for Text‐Image‐Audio‐to‐Video

Advanced Intelligent Systems, EarlyView.
DyCoDiff enables text, image, and audio conditions to collaborate before temporal video generation. A multimodal condition fusion layer injects gated text‐centric fusion into the temporal branch, while collaborative losses align generated videos with semantic, visual, and dynamic audio cues for rhythm‐sensitive text‐image‐audio‐to‐video synthesis. Text‐
Youming Zhang, Wenmin Wang, Zhongheng Chen, Zhengyu Peng   +3 more
wiley   +1 more source

From Atoms to Reactors: A Critical Review of a Decade of Machine Learning in Heterogeneous Catalysis (2017–2026)

Angewandte Chemie, EarlyView.
Machine learning has digitalized heterogeneous catalysis from the top of the length scale downward: prediction at the atom and the material is now mature, yet the reactor and the process remain the open frontier. This critical review traces a decade of progress across the full catalytic value chain and asks where the next decade of impact must be won ...
Rafael Larraz, Avelino Corma
wiley   +2 more sources

Applying generative adversarial networks to generate synthetic train trip data for train delay prediction

APPLYING GENERATIVE ADVERSARIAL NETWORKS TO GENERATE SYNTHETIC TRAIN TRIP DATA FOR TRAIN DELAY PREDICTION Applying generative adversarial networks to generate synthetic train trip data for train delay prediction / Hauck, Florian (Rights reserved) (-
Hauck, Florian, Güth, Albrecht, Kliewer, Natalia, Rößler-von Saß, David Christian Daniel   +3 more
core  

AI‐Powered Smart Displays and AR Glasses: Perspectives and Challenges

Advanced Intelligent Systems, EarlyView.
This perspective examines how AI is reshaping smart displays and AR glasses. The latest in image‐quality enhancement, sensor‐integrated display optimization, and physiological sensing in display systems, followed by AI‐assisted AR technologies including waveguide optimization, image compensation, foveated rendering, and multimodal sensing, is discussed.
Luke Benoit, Yizhou Qian, Yi‐Chien Chen, Ziyang Shen, Po‐Sheng Chiu, Youngeon Lee, Cheng‐Han Ke, Chih‐Lung Lin, Shin‐Tson Wu   +8 more
wiley   +1 more source

Vertical NAND Flash Memory‐Based Bayesian Neural Networks With Intrinsic Gaussian Weight Sampling

Advanced Intelligent Systems, EarlyView.
Commercial vertical NAND flash memory supports Bayesian neural networks by using paired cells to produce bell‐shaped weight distributions. Read voltage controls their spread without additional memory cells or changes to the array structure. Simulations based on device measurements demonstrate improved uncertainty estimation and accuracy gains of up to ...
Sung‐Ho Park, Mingyun Oh, Dongbeen Shin, Jaehyeon Kim, Jonghyun Ko, Jiseong Im, Yeongheon Yang, Gyuweon Jung, Jong‐Ho Lee   +8 more
wiley   +1 more source