Results 171 to 180 of about 319,994 (331)

A dynamic stimulus distribution methodology for efficient large-scale pairwise relational data collection. [PDF]

MethodsX
Chuyin Z, Lipeng L, Tsuchiya N.
europepmc   +1 more source

Determinants of Knowledge and Usage of Generative Artificial Intelligence in Agricultural Extension: Evidence From Tennessee Extension Personnel

Agribusiness, EarlyView.
ABSTRACT This paper examines the determinants of generative AI (GenAI) knowledge and usage among agricultural extension professionals. Drawing on survey data from agricultural extension personnel in Tennessee, we employ regression analyses and latent Dirichlet allocation (LDA) for topic modeling of open‐ended responses to study the knowledge and usage ...
Abdelaziz Lawani, Jason de Koff, James Muriuki, Anna‐Liisa Ihuhwa   +3 more
wiley   +1 more source

How Complex is a GitHub Issue?

Anonymous
openalex   +1 more source

GitHub Copilot and AI Coding Tools in Practice [PDF]

Nick Wienholt
openalex   +1 more source

Replication Package for Paper "How Early Participation Determines Long-Term Sustained Activity in GitHub Projects"

, 2023
­ Anonymous
openalex   +1 more source

Beyond blacklists: a critical assessment of exclusion set generation strategies and alternative approaches. [PDF]

Bioinformatics
Wall BPG, Ogata JD, Nguyen M, Olex AL, Floros KV, Faber AC, McClay JL, Harrell JC, Dozmorov MG.   +8 more
europepmc   +1 more source

Automatic Determination of Quasicrystalline Patterns from Microscopy Images

Advanced Intelligent Discovery, EarlyView.
This work introduces a user‐friendly machine learning tool to automatically extract and visualize quasicrystalline tiling patterns from atomically resolved microscopy images. It uses feature clustering, nearest‐neighbor analysis, and support vector machines. The method is broadly applicable to various quasicrystalline systems and is released as part of
Tano Kim Kender, Marco Corrias, Cesare Franchini   +2 more
wiley   +1 more source

Development and Evolution of Xtext-based DSLs on GitHub: An Empirical Investigation [PDF]

Weixing Zhang, Daniel Strüber, Regina Hebig   +2 more
openalex   +1 more source

pam: An R Package for Fast and Efficient Processing of Pulse-Amplitude Modulation Data. [PDF]

Ecol Evol
Böhm J, Schrag P.
europepmc   +1 more source

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source