Results 81 to 90 of about 572,458 (282)

Multivariate temporal dictionary learning for EEG [PDF]

Journal of Neuroscience Methods, 2013
This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary.
Barthélemy, Quentin, Gouy-Pailler, Cédric, Isaac, Yoann, Souloumiac, Antoine, Larue, Anthony, Mars, Jérôme I.   +5 more
openaire   +5 more sources

ChatCFD: A Large Language Model‐Driven Agent for End‐to‐End Computational Fluid Dynamics Automation with Structured Knowledge and Reasoning

Advanced Intelligent Discovery, EarlyView.
Chat computational fluid dynamics (CFD) introduces an large language model (LLM)‐driven agent that automates OpenFOAM simulations end‐to‐end, attaining 82.1% execution success and 68.12% physical fidelity across 315 benchmarks—far surpassing prior systems.
E Fan, Kang Hu, Zhuowen Wu, Jiangyang Ge, Jiawei Miao, Yuzhi Zhang, He Sun, Weizong Wang, Tianhan Zhang   +8 more
wiley   +1 more source

Compressed Data Collection Method for Wireless Sensor Networks Based on Optimized Dictionary Updating Learning

IEEE Access, 2020
Wireless sensor networks (WSNs) is composed of a large number of tiny sensors. These energy-constrained sensors are deployed in a variety of environments to collect data such as temperature, humidity, and light intensity.
Junying Chen, Fuqiang Zhou, Zhanshe Guo, Jiangwen Wan   +3 more
doaj   +1 more source

An Incidence Geometry approach to Dictionary Learning [PDF]

, 2015
We study the Dictionary Learning (aka Sparse Coding) problem of obtaining a sparse representation of data points, by learning \emph{dictionary vectors} upon which the data points can be written as sparse linear combinations.
Sitharam, Meera, Tarifi, Mohamad, Wang, Menghan   +2 more
core  

A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
A novel hybrid transfer learning approach for brain tumor classification achieves 99.47% accuracy using magnetic resonance imaging (MRI) images. By combining image preprocessing, ensemble deep learning, and explainable artificial intelligence (XAI) techniques like gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP), the ...
Sadia Islam Tonni, Md. Alif Sheakh, Mst. Sazia Tahosin, Md. Zahid Hasan, Taslima Ferdaus Shuva, Touhid Bhuiyan, Muhammad Ali Abdullah Almoyad, Nabil Anan Orka, Md. Tanvir Rahman, Risala Tasin Khan, M. Shamim Kaiser, Mohammad Ali Moni   +11 more
wiley   +1 more source

Analysis of fast structured dictionary learning [PDF]

Information and Inference: A Journal of the IMA, 2019
Abstract Sparsity-based models and techniques have been exploited in many signal processing and imaging applications. Data-driven methods based on dictionary and sparsifying transform learning enable learning rich image features from data and can outperform analytical models.
Ravishankar, Saiprasad, Ma, Anna, Needell, Deanna   +2 more
openaire   +3 more sources

Robust Dysarthric Speech Recognition with GAN Enhancement and LLM Correction

Advanced Intelligent Systems, EarlyView.
This study tackles dysarthric speech recognition by combining generative adversarial network (GAN)‐generated synthetic data with large language model (LLM)‐based error correction. The approach integrates three key elements: an improved CycleGAN to generate synthetic dysarthric speech for data augmentation, a multimodal automatic speech recognition core
Yibo He, Kah Phooi Seng, Chee Shen Lim, Li Minn Ang   +3 more
wiley   +1 more source

BDL.NET: Bayesian dictionary learning in Infer.NET [PDF]

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016
We introduce and analyse a flexible and efficient implementation of Bayesian dictionary learning for sparse coding. By placing Gaussian-inverse-Gamma hierarchical priors on the coefficients, the model can automatically determine the required sparsity level for good reconstructions, whilst also automatically learning the noise level in the data ...
Diethe, Tom, Twomey, Niall, Flach, Peter
openaire   +3 more sources

Trainlets: Dictionary Learning in High Dimensions

, 2016
Sparse representations has shown to be a very powerful model for real world signals, and has enabled the development of applications with notable performance.
Elad, Michael, Ophir, Boaz, Sulam, Jeremias, Zibulevsky, Michael   +3 more
core   +1 more source

SciLitMiner: An Intelligent System for Scientific Literature Mining and Knowledge Discovery

Advanced Intelligent Systems, EarlyView.
SciLitMiner is an intelligent system that federately ingests scientific literature, filters it using advanced information retrieval methods, and applies retrieval‐augmented generation tailored to scientific domains. Demonstrated on creep deformation in γ‐TiAl alloys, SciLitMiner provides a controlled workflow for systematic knowledge discovery and ...
Vipul Gupta, Jonathan David Heaton Paul, Ingo Schmitt, Florian Pyczak   +3 more
wiley   +1 more source