Results 161 to 170 of about 9,834 (275)

Scalable Computation of Topological Abstractions for Scalar Data

Computer Graphics Forum, EarlyView.
Abstract Topological data analysis has become an important tool for large scale scalar data analysis and visualization, efficiently extracting the inherent structure and features of interest of the data. However, with growing dataset sizes and complexity, it is increasingly becoming infeasible to compute topological abstractions of interest in serial ...
M. Will, J. Lukasczyk, J. Tierny, A. Gyulassy, G. H. Weber, H. Carr, C. Garth   +6 more
wiley   +1 more source

Breast Tumor Diagnosis Based on Molecular Learning Vector Quantization Neural Networks. [PDF]

Adv Sci (Weinh)
Huang C, Shao J, Peng B, Guo Q, Li P, Sun J, Wang Y.   +6 more
europepmc   +1 more source

Rejection strategies for learning vector quantization

, 2014
Fischer L, Hammer B, Wersing H. Rejection strategies for learning vector quantization. In: Verleysen M, ed. ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
Hammer, Barbara ; https://orcid.org/, Wersing, Heiko, Fischer, Lydia, Verleysen, Michel   +3 more
core  

STMIIT—Symbol Tags for Massive Insects Identification and Tracking

Insect Science, EarlyView.
Ruigang Wang, Tailin Liu, Xinyi Jiang, Kun Dong, Hong Yuan, Hongmu Zhao, Zhengwei Wang, Wenzheng Zhao   +7 more
wiley   +1 more source

Survey on Visualization of Information Diffusion over Networks

Computer Graphics Forum, EarlyView.
Abstract Information Diffusion (ID) describes how a value (e.g., a pathogen, a rumor, a packet) spreads through an underlying “medium” network of elements (e.g., a social or computer network). Understanding the information diffusion process is essential to predicting trends, controlling misinformation, and enhancing decision‐making as well as ...
T. Baumgartl, M. Sondag, V. Filipov, M. Tuscher, S. Rajendran, S. Miksch, D. Archambault, A. Arleo, T. von Landesberger   +8 more
wiley   +1 more source

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang, Daeun Choi, Damian Adams, Jaehyun Ahn, Kelly Balmant, Raquel Dias, Carlos D. Messina, Rafael Muñoz‐Carpena, Vance M. Whitaker, Haipeng Yu, Ziwen Yu, Chang Zhao, Changying Li   +12 more
wiley   +1 more source

Relevance determination in learning vector quantization

, 2001
Bojer T, Hammer B, Schunk D, Tluk von Toschanowitz K. Relevance determination in learning vector quantization. In: Verleysen M, ed. ESANN'2001.
Verleysen, M., Hammer, Barbara ; https://orcid.org/, Daniel Schunk, Katharina Tluk Von Toschanowitz, Bojer, T., Barbara Hammer, Tluk von Toschanowitz, Katharina, Schunk, D., Thorsten Bojer   +8 more
core  

Terrestrial Ecosystem Detection Based on Deep Learning Framework and Satellite Image Geographical Information

Geoscience Data Journal, Volume 13, Issue 3, July 2026.
The proposed deep learning framework integrates ResNet‐50 and LSTM models to detect and classify terrestrial ecosystems from satellite imagery. The workflow begins with image preprocessing using bilateral, guided, and median filters to enhance image quality and preserve edges.
Liang Dong, Fan Lei, Dongliang Huang, Yuye Gong, Jide Wei, Wenjing She   +5 more
wiley   +1 more source

A combined method of optimized learning vector quantization and neuro-fuzzy techniques for predicting unified Parkinson's disease rating scale using vocal features. [PDF]

MethodsX
Zogaan WA, Nilashi M, Ahmadi H, Abumalloh RA, Alrizq M, Abosaq H, Alghamdi A.   +6 more
europepmc   +1 more source

Speeding up Generalized Learning Vector Quantization

, 2007
This paper first discusses the performance evaluation between Generalized Learning Vector Quantization (GLVQ) and other clustering algorithms (Hard C-means: HCM, Fuzzy C-means: FCM, and Learning Vector Quantization: LVQ) using wine recognition data. GLVQ,
Seok-joo Kang, Jang-Hee Yoo, Byoung-Ho Kang   +2 more
core