Results 151 to 160 of about 98,022 (269)

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

A hybrid LSTM-GRU framework for lung cancer classification using GWO-WOA algorithm for hyperparameter tuning and BPSO for feature selection. [PDF]

Sci Rep
Amrir MMS, Ayid YM, Elshewey AM, Fouad Y.   +3 more
europepmc   +1 more source

Hyperparameter Tuning and Pipeline Optimization via Grid Search Method and Tree-Based AutoML in Breast Cancer Prediction. [PDF]

J Pers Med, 2021
Radzi SFM, Karim MKA, Saripan MI, Rahman MAA, Isa INC, Ibahim MJ.   +5 more
europepmc   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

Semi-supervised GAN with hybrid regularization and evolutionary hyperparameter tuning for accurate melanoma detection. [PDF]

Sci Rep
Golkarieh A, Razmara P, Lagzian A, Dolatabadi A, Mousavirad SJ.   +4 more
europepmc   +1 more source

Practical Bayesian optimisation for hyperparameter tuning

, 2020
Advances in machine learning have had, and continue to have, a profound effect on scientific research and industrial activities. We are able to uncover insights contained within large troves of data and develop models to solve problems that seemed infeasible until recently.
openaire   +2 more sources

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

Advanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source

Minimizing unnecessary tax audits using multi-objective hyperparameter tuning of XGBoost with focal loss. [PDF]

Front Artif Intell
Malashin IP, Masich IS, Tynchenko VS, Gantimurov AP, Nelyub VA, Borodulin AS.   +5 more
europepmc   +1 more source

Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson's disease.

Sci Rep, 2023
Lilhore UK, Dalal S, Faujdar N, Margala M, Chakrabarti P, Chakrabarti T, Simaiya S, Kumar P, Thangaraju P, Velmurugan H.   +9 more
europepmc   +1 more source

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

Advanced Intelligent Discovery, EarlyView.
This paper presents a computer vision (deep learning) pipeline integrating YOLOv8 and YOLOv9 for automated detection, segmentation, and analysis of rosette cellulose synthase complexes in freeze‐fracture electron microscopy images. The study explores curated dataset expansion for model improvement and highlights pipeline accuracy, speed ...
Siri Mudunuri, Leala Carbonneau, Eric M. Roberts, Alison W. Roberts, Candace H. Haigler, Alexey V. Gulyuk, Yaroslava G. Yingling   +6 more
wiley   +1 more source