Results 71 to 80 of about 5,451,620 (296)

Day-Ahead Solar Irradiance Forecasting Using Hybrid Recurrent Neural Network with Weather Classification for Power System Scheduling

Applied Sciences, 2021
At the present time, power-system planning and management is facing the major challenge of integrating renewable energy resources (RESs) due to their intermittent nature.
Rehman Zafar, Ba Hau Vu, Munir Husein, Il-Yop Chung   +3 more
doaj   +1 more source

Artificial Intelligence‐Driven Inverse Design of Singlet Fission Candidates in the Acene family

Advanced Science, EarlyView.
In this work, we describe an AI‐driven inverse design platform predicting S1 and T1 energies across acenes with high accuracy. Using Hammett σ constants as descriptors and RNN models coupled with optimization algorithms, it efficiently explores ≈1014 structures, uncovering novel SF candidates from benzene to pentacene. Freely accessible at https://alba.
Rafael G. Uceda, Boris Pérez‐Cañedo, Sandra Míguez‐Lago, Carlos M. Cruz, Joaquín J. Torres, Omar Núñez, Luis Álvarez de Cienfuegos, Antonio J. Mota, Delia Miguel, Juan M. Cuerva   +9 more
wiley   +1 more source

Comparing forecasting performances between multilayer feedforward neural network and recurrent neural network in Malaysia's load [PDF]

, 2010
This paper presents the use of two artificial neural networks models, namely the multilayer feedforward neural network (MLFF) and the recurrent neural network (RNN) are applied for Malaysia’s load forecasting. For this purpose, a half hourly load data is
Ismail, Zuhaimy, Ahmad, Maizah Hura, Arshad, Khairil Anuar, Norizan Mohamed, Mohamed, Norizan, Maizah Hura Ahmad, Khairil Anuar Arshad, Zuhaimy Ismail   +7 more
core   +1 more source

BraMARS: An Interpretable Histopathology‐Driven Deep Learning Model for Brain Metastasis Risk Stratification in Surgically Resected Limited‐Stage SCLC

Advanced Science, EarlyView.
This study presents BraMARS, an explainable deep learning model that estimates future brain metastasis risk in surgically resected limited‐stage small‐cell lung cancer using routine H&E‐stained whole‐slide images. By linking model‐attributed spatial histopathology with clinical outcomes and proteomic programs, BraMARS provides a biologically ...
Zijian Yang, Taolue Wang, Shilong Liu, Zicheng Zhang, Yibo Zhang, Fan Yang, Bo Yu, Shuaishuai Gao, Yu Chen, Lin Yang, Meng Zhou   +10 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

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

LLMs as Representation Generators for Gradient‐Free Scientific Optimization: A Case Study in Battery Fast Charging

Advanced Science, EarlyView.
Large language models serve as representation generators for gradient‐free scientific optimization. Two complementary approaches produce executable battery‐charging policies either directly or through LLM‐designed neural representations coupled to Bayesian optimization.
Ge Lei, Ferran Brosa Planella, Sterling G. Baird, Samuel J. Cooper   +3 more
wiley   +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

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   +4 more sources