Results 111 to 120 of about 36,835 (256)

Online Hyperparameter Tuning in Bayesian Optimization for Material Parameter Identification: An Application in Strain-Hardening Plasticity for Automotive Structural Steel

AppliedMath
Effective identification of strain-hardening parameters is essential for predictive plasticity models used in automotive applications. However, the performance of Bayesian optimization depends strongly on kernel hyperparameters in the Gaussian-process ...
Teng Long, Leyu Wang, Cing-Dao Kan, James D. Lee   +3 more
doaj   +1 more source

Exact Discrete Stochastic Simulation With Deep‐Learning‐Scale Gradient Optimization

Advanced Science, EarlyView.
A 203,796‐parameter gene regulatory network classifies handwritten digits with 98.4% accuracy using exact stochastic dynamics. The framework decouples forward simulation from backward differentiation, making continuous‐time Markov chain models compatible with deep‐learning optimization.
Jose M. G. Vilar, Leonor Saiz
wiley   +1 more source

Cardiovascular disease detection from cardiac arrhythmia ECG signals using artificial intelligence models with hyperparameters tuning methodologies

Heliyon
Cardiovascular disease (CVD) is connected with irregular cardiac electrical activity, which can be seen in ECG alterations. Due to its convenience and non-invasive aspect, the ECG is routinely exploited to identify different arrhythmias and automatic ECG
Gowri Shankar Manivannan, Harikumar Rajaguru, Rajanna S, Satish V. Talawar   +3 more
doaj   +1 more source

Deep Learning Network‐Tailored Microenvironment Matching of 4D Bioprinting Bioactive Scaffolds for Bone Regeneration

Advanced Science, EarlyView.
A DLN dataset was built to analyze MABS composition versus in vitro/in vivo osteogenesis and angiogenesis. An MLP neural network, taking BG morphological parameters as input, extracts bioactive features from these datasets. A rabbit tibial defect model then validates 4D‐printed MABS for adaptability and bone regeneration in critical defects.
Xiongjie Liang, Yuechi Zhang, Weifeng Hu, Shiyan Lv, Fan Jia, Yan Zhang, Feng Wu, Changjiang Yang, Guanqi Zhen, Jinglong Yan, Xu Cui, Wei Zhao, Guanghua Chen   +12 more
wiley   +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   +3 more sources

Optimizing a Hybrid Deep Learning Model for DDoS Detection Using DBSCAN and PSO

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This study proposes a hybrid deep learning approach that combines Gated Recurrent Units (GRUs) and Convolutional Neural Networks (CNNs) for Distributed Denial of Service (DDoS) cyberattack detection.
Indrastanti Ratna Widiasari, Rissal Efendi   +1 more
doaj   +1 more source

Hexagonal Boron Nitride on Liquid and Single‐Crystal Copper: Operando X‐Ray and Atomistic Insights into Growth and Interfacial Structure

Advanced Science, EarlyView.
Liquid metal catalysts are widely assumed to promote high‐quality 2D growth. In this work, operando experiments and machine‐learning‐accelerated simulations show that hBN on Cu breaks this graphene‐derived expectation, where molten Cu promotes multilayer/3D growth, but re‐solidified single‐crystal Cu stabilizes monolayer formation.
Nikoo Ghanadan, Valentina Rein, Hao Gao, Egor Bersenev, Thomas Sarrazin, Manmeet Kaur Sodhi, Bárbara Canto, Irina Snigireva, Nicolas Gauthier, Anastasios C. Manikas, Costas Galiotis, Maciej Jankowski, Gilles Renaud, Athanasios Dimoulas, Karsten Reuter, Oleg Konovalov, Hendrik H. Heenen, Irene M.N. Groot   +17 more
wiley   +1 more source

The Use of Hyperparameter Tuning in Model Classification: A Scientific Work Area Identification

JOIV: International Journal on Informatics Visualization
This research aims to investigate the effectiveness of hyperparameter tuning, particularly using Optuna, in enhancing the classification performance of machine learning models on scientific work reviews. The study focuses on automating the classification
Nadya Alinda Rahmi, Sarjon Defit, - Okfalisa   +2 more
doaj   +1 more source

ORBIT‐AMD: Ordinal Risk, Bilateral Imaging, and Trajectory Learning for Age‐Related Macular Degeneration in Multi‐Cohorts

Advanced Science, EarlyView.
Eligibility flow and real‐world AMD burden in the UKB retinal imaging cohort and TMUEH external‐validation cohort. Overview of the ORBIT‐AMD architecture, integrating retinal representation pretraining, bilateral eye‐graph modeling and concept bottleneck learning to support ordered risk, bilateral context, interpretable lesion concepts, longitudinal ...
Xuehao Cui, Dejia Wen, Patrick Yu‐Wai‐Man, Xiaorong Li   +3 more
wiley   +1 more source

StackingNet: Collective Inference Across Independent AI Foundation Models

Advanced Science, EarlyView.
ABSTRACT Artificial intelligence (AI) built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black‐box foundation models is essential for trustworthy ...
Siyang Li, Chenhao Liu, Dongrui Wu, Zhigang Zeng, Lieyun Ding   +4 more
wiley   +1 more source