Results 171 to 180 of about 43,961 (294)

Super-Resolution Reconstruction of Sonograms Using Residual Dense Conditional Generative Adversarial Network. [PDF]

Sensors (Basel)
Xu Z, Wei Y.
europepmc   +1 more source

Enhanced Generative Adversarial Networks for Isa Furnace Matte Grade Prediction under Limited Data [PDF]

Huaibo Ma, Zhuorui Li, Bo Shu, Bin Yu, Jun Ma   +4 more
openalex   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source

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

Predicting multiphase flow behavior of methane in shallow unconfined aquifers using conditional deep convolutional generative adversarial network

Reza Ershadnia, Farzad Moeini, Seyyed A. Hosseini, Zhenxue Dai, Mohamad Reza Soltanian   +4 more
openalex   +1 more source

Neural Network Models for Solar Irradiance Forecasting in Polluted Areas: A Comparative Study

Energy Science &Engineering, EarlyView.
Pollution‐aware hybrid ensemble model is proposed to forecast solar irradiance across eight diverse cities. The model integrates MLP, RNN, and NARX to handle varying atmospheric pollution levels. The model outperforms state‐of‐the‐art methods with enhanced accuracy and interpretability on standard solar irradiance data set.
Mujtaba Ali, Muhammad Yaqoob Javed, Aamer Bilal Asghar, Khurram Hashmi, Abbas Javed, Basem Alamri, Krzysztof Ejsmont   +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

Performance Monitoring of Photovoltaic Modules Using Machine‐Learning‐Based Solutions: A Survey of Current Trends

Energy Science &Engineering, EarlyView.
The graphical abstract presents the concept of applying machine‐learning algorithms to assess the performance of photovoltaic modules. Data from solar panels are fed to surrogates of intelligent models, to assess the following performance metrics: identifying faults, quantifying energy production and trend degradation over time. The combination of data
Nangamso Nathaniel Nyangiwe, Abraham Dimitri Kapim Kenfack, Nicolas Thantsha, Mandla Msimanga   +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

Machine learning-based urban densification: extending roof ridge lines for sustainable housing extension using generative adversarial networks [PDF]

Yangzhi Li, Jingwei Li, Qiwei Song
openalex   +1 more source