Results 191 to 200 of about 5,001,817 (246)

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

From Top to Bottom: Manufacturing Process‐Context Aware Resolution of Energy Device Electrodes Through a 3D Diffusion Generative Model

Advanced Energy Materials, EarlyView.
The application of a generative diffusion model, enhanced with a training data augmentation pipeline retaining the manufacturing process context of electrode microstructures, leads to improved fidelity of the through‐plane tortuosity factor in the AI generated samples.
Victor Ramirez‐Camacho, Siwar Ben Hadj Ali, Maxence Desnoyers, Francisco Fernandez, Nassima Benammar, Alejandro A. Franco   +5 more
wiley   +1 more source

How Video‐Based Information Affects Farmers' Willingness to Pay for Drone Services

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT Professional service for digital technology like agricultural drones lowers transaction costs and scope thresholds for smallholders. Meanwhile, perceptual adoption barriers remain underexplored. We conduct a two‐stage choice experiment with a randomized video‐based information treatment among 384 Chinese crop farmers to measure its effect on ...
Hua Zhang, Lena Kuhn, Hang Xiong, Zhanli Sun, Thomas Glauben   +4 more
wiley   +1 more source

Some of the next articles are maybe not open access.

Related searches:

A new hyperparameters optimization method for convolutional neural networks

Pattern Recognition Letters, 2019
The use of convolutional neural networks involves hyperparameters optimization. Gaussian process based Bayesian optimization (GPEI) has proven to be an effective algorithm to optimize several hyperparameters.
Hua Cui, J. Bai
exaly   +2 more sources

Improving Harris hawks optimization algorithm for hyperparameters estimation and feature selection in v‐support vector regression based on opposition‐based learning

Journal of Chemometrics, 2020
Many real problems have been solved by support vector regression, especially v‐support vector regression (v‐SVR), but there are hyperparameters that usually needed to tune. In addition, v‐SVR cannot perform feature selection.
Zakariya Algamal, Omar Saber Qasim
exaly   +2 more sources

Efficient hyperparameters optimization through model-based reinforcement learning with experience exploiting and meta-learning

Soft Computing, 2023
Jia Wu
exaly   +2 more sources

Automatic tuning of hyperparameters using Bayesian optimization

Evolving Systems, 2020
G Maragatham
exaly   +2 more sources

Reproducible Hyperparameter Optimization

Journal of Computational and Graphical Statistics, 2021
A key issue in machine learning research is the lack of reproducibility. We illustrate what role hyperparameter search plays in this problem and how regular hyperparameter search methods can lead t...
Lars Hertel, Pierre Baldi, Daniel L. Gillen   +2 more
openaire   +2 more sources

AutoML With Parallel Genetic Algorithm for Fast Hyperparameters Optimization in Efficient IoT Time Series Prediction

IEEE Transactions on Industrial Informatics, 2023
With the development of artificial intelligence and the improvement of hardware computing power, deep learning models have become widely used in the Internet of Things (IoT) field, especially for analyzing spatiotemporal data collected by wireless ...
Di Wu, Qinghua Guan, Zhe Fan, Hanhui Deng, Tao Wu   +4 more
semanticscholar   +1 more source

Hyperparameters optimization of neural network using improved particle swarm optimization for modeling of electromagnetic inverse problems

International journal of microwave and wireless technologies, 2021
Optimization of hyperparameters of artificial neural network (ANN) usually involves a trial and error approach which is not only computationally expensive but also fails to predict a near-optimal solution most of the time.
Debanjali Sarkar, Taimoor Khan, Fazal Ahmed Talukdar   +2 more
semanticscholar   +1 more source