Results 81 to 90 of about 30,189 (295)

Causal‐Guided Ultra‐Long‐Term Time Series Forecasting Via Anticipated Covariates

Advanced Science, EarlyView.
Often treated as unknown, information from the future remains underutilized.We demonstrate that in a coupled dynamical system, providing the future state of the effect enables accurate forecasting of the cause for a long timesteps. A time series forecasting paradigm that introduces anticipated covariates to represent such known future states is ...
Jintong Zhao, Yufei Liu, Ruixi Huang, Zhongxue Gan, Siyang Leng   +4 more
wiley   +1 more source

DB-RNN: An RNN for Precipitation Nowcasting Deblurring

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Precipitation nowcasting based on artificial intelligence has garnered widespread attention in the meteorological and computer communities in recent years.
Zhifeng Ma, Hao Zhang, Jie Liu
doaj   +1 more source

Convolutional Recurrent Neural Networks for Observation-Centered Plant Identification

Journal of Electrical and Computer Engineering, 2018
Traditional image-centered methods of plant identification could be confused due to various views, uneven illuminations, and growth cycles. To tolerate the significant intraclass variances, the convolutional recurrent neural networks (C-RNNs) are ...
Xuanxin Liu, Fu Xu, Yu Sun, Haiyan Zhang, Zhibo Chen   +4 more
doaj   +1 more source

A Parallel GRU Recurrent Network Model and its Application to Multi-Channel Time-Varying Signal Classification

IEEE Access, 2019
This study presents a modified recurrent neural network (RNN) model designed as a parallel computing structure for serial information processing. The result is a novel parallel recurrent neural network (P-RNN), proposed for application to time-varying ...
Shaohua Xu, Jingjing Li, Kun Liu, Lu Wu
doaj   +1 more source

Evaluation of neural network models for landslide susceptibility assessment

International Journal of Digital Earth, 2022
Identifying and assessing the disaster risk of landslide-prone regions is very critical for disaster prevention and mitigation. Owning to their special advantages, neural network algorithms have been widely used for landslide susceptibility mapping (LSM)
Yaning Yi, Wanchang Zhang, Xiwei Xu, Zhijie Zhang, Xuan Wu   +4 more
doaj   +1 more source

Enhanced High Dimensionality and the Information Processing Capacity in Interfered Spin Wave‐Based Reservoir Computing, Achieved With Eight Detectors

Advanced Electronic Materials, EarlyView.
Physical reservoir computing (PRC) based on spin wave interference has demonstrated high computational performance, yet room for improvement remains. In this study, we fabricated this concept PRC with eight detectors and evaluated the impact of the number of detectors using a chaotic time series prediction task.
Sota Hikasa, Wataru Namiki, Daiki Nishioka, Maki Nishimura, Ryo Iguchi, Kazuya Terabe, Takashi Tsuchiya   +6 more
wiley   +1 more source

Catching the Phish: Detecting Phishing Attacks Using Recurrent Neural Networks (RNNs) [PDF]

, 2020
The emergence of online services in our daily lives has been accompanied by a range of malicious attempts to trick individuals into performing undesired actions, often to the benefit of the adversary. The most popular medium of these attempts is phishing attacks, particularly through emails and websites.
Halgas, L, Agrafiotis, I, Nurse, J
openaire   +2 more sources

pohl-michel/Time-series-prediction-using-a-RNN-trained-with-RTRL: First release

, 2020
<p>Prediction of multidimensional time-series data using a recurrent neural network (RNN) trained by real-time recurrent learning (RTRL) with gradient clipping.</p ...
Pohl Michel
core   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

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