Results 201 to 210 of about 222,396 (351)

Conditional generative adversarial network technology for OFDM system receiver signal detection. [PDF]

PLoS One
Liu Y, Liu P, Shi Y, Hao X.
europepmc   +1 more source

Generating Realistic Training Images Based on Tonality-Alignment Generative Adversarial Networks for Hand Pose Estimation [PDF]

, 2018
Liangjian Chen, Shih-Yao Lin, Yusheng Xie, Hui Tang, Yufan Xue, Xiaohui Xie, Yen‐Yu Lin, Wei Fan   +7 more
openalex   +1 more source

Optimized Generative Adversarial Networks for Adversarial Sample Generation

Computers, Materials & Continua, 2022
Daniyal M. Alghazzawi, Syed Hamid Hasan, Surbhi Bhatia   +2 more
openaire   +1 more source

Appraisal of Gene Expression‐Based Classifiers for Neuropsychiatric Disorders: A Meta‐Regression

American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, EarlyView.
ABSTRACT A substantial body of research examines the potential of gene‐expression‐based biomarkers for diagnosing and selecting treatments for neuropsychiatric disorders, yet no clear consensus has been reached regarding the influence of controllable factors such as study design and model selection on the performance of gene‐expression‐based ...
Ali Razavi, Brittany Arensman, Eric J. Barnett, Leo A. Lee, Stephen V. Faraone, Stephen J. Glatt, Jonathan L. Hess   +6 more
wiley   +1 more source

Clinical Application of Using Diffusion-Based Wasserstein Generative Adversarial Network for Morphologic Analysis of Blood Cells. [PDF]

J Clin Lab Anal
Kim HY, Ngasa EE, Kim HJ, Kim B, Lim G, Park CH, Kwon HJ, Jang MA, Woo J.   +8 more
europepmc   +1 more source

Harnessing Machine Learning and Deep Learning Approaches for Laser‐Induced Breakdown Spectroscopy Data Analysis: A Comprehensive Review

Analysis &Sensing, EarlyView.
Laser‐induced breakdown spectroscopy (LIBS), an atomic emission technique, is widely applied in fields like geology and biology. This rapid elemental analysis method leverages computational tools to boost precision and speed up data processing. This review explores machine learning and deep learning methods for analyzing LIBS spectral data, tackling ...
Pegah Dehbozorgi, Ludovic Duponchel, Vincent Motto‐Ros, Thomas Bocklitz   +3 more
wiley   +1 more source

Optimized generative adversarial network for efficient resolution enhancement of 3D segmented rock tomography. [PDF]

Sci Rep
Ugolkov E, He X, Kwak H, Hoteit H.
europepmc   +1 more source

Mining Chemical Space with Generative Models for Battery Materials

Batteries &Supercaps, EarlyView.
Revolutionizing Li‐ion battery material discovery with MatterGen, a foundational generative AI model for crystal structure inverse design. Explored stable, unique, and novel compositions and their analysis with respect to the state‐of‐the‐art databases, followed by DFT validation, provides a new direction for accelerating materials discovery ...
Chiku Parida, Diptendu Roy, Juan Maria García Lastra, Arghya Bhowmik   +3 more
wiley   +1 more source

A Multi-Property Optimizing Generative Adversarial Network for de novo Antimicrobial Peptide Design. [PDF]

Adv Sci (Weinh)
Liu J, Cui T, Wang T, Lin L, Zeng X, Lu D, Jiao S, Wang J, Li X, Xiao S, Xie D, Wang X, Wang Y, Shang X, Niu Y, Wei Z, Peng J.   +16 more
europepmc   +1 more source

Generative Deep Learning for Advanced Battery Materials

Batteries &Supercaps, EarlyView.
This review explores the role of generative deep learning (DL) in battery materials analysis and highlights the fundamental principles of generative DL and its applications in designing battery materials. The importance of using multimodal data is underscored to effectively address the challenges faced during the development of battery materials across
Deepalaxmi Rajagopal, Adrian Cierpka, Britta Nestler, Arnd Koeppe   +3 more
wiley   +1 more source