Results 161 to 170 of about 30,189 (295)

Beginner's Guide to Recurrent Neural Networks (RNNs) with Keras

Understanding Sequential Data Modelling with Keras for Time Series Prediction <strong> Author </strong> Wenyi Pi ( <strong> ORCID </strong> : 0009–0002–2884–2771) Introduction Recurrent Neural Networks (RNNs) are a special type of neural networks that are suitable for learning representations of sequential data like text in ...
openaire   +1 more source

PENGEMBANGAN SISTEM ALAT DETEKSI GEMPA BUMI DENGAN KLASIFIKASI DATA MENGGUNAKAN RECURRENT NEURAL NETWORK (RNN)

Indonesia merupakan wilayah yang rentan terhadap gempa bumi karena lokasinya berada di pertemuan tiga lempeng benua dan dikelilingi oleh Cincin Api (Ring of Fire).
Anggraini, Ika Novia, Kurniawan, Adhadi, ALKHAIR, AFIF   +2 more
core  

Similarity‐Driven Framework for Efficient Polymer Property Prediction Under Data Scarcity Scenarios

Journal of Polymer Science, EarlyView.
A new approach, based on structural and chemical similarities between polymers, is proposed to improve the performance of artificial neural networks in predicting the glass transition temperature under data‐scarce conditions. ABSTRACT Predicting polymer properties directly from chemical structure is essential for the rational design of advanced ...
Amaia Elizaran Mendarte, Gustavo A. Schwartz   +1 more
wiley   +1 more source

THE ROLE OF CONVOLUTIONAL NEURAL NETWORK (CNN) AND RECURRENT NEURAL NETWORK (RNN) ON LEADERSHIP AND WORKFORCE AGILITY IN UMSU POSTGRADUATE PROGRAMS

Convolutional Neural Network (CNN) is a development of Multilayer Perceptron (MLP) designed to process and classify data. Recurrent Neural Network (RNN) is an artificial neural network architecture known for its good performance as it processes input ...
ayadi, B. Herawan H, Triwanda, Eri, Wanayumini, Wanayumini   +2 more
core   +1 more source

Polar‐low track prediction using machine‐learning methods

Quarterly Journal of the Royal Meteorological Society, EarlyView.
Machine‐learning models are developed to produce reliable and efficient forecasts of polar‐low (PL) trajectories 12 hours ahead. A temporal model (RLSTM) benefiting from the rolling‐forecast strategy, improves overall prediction accuracy and is suitable for quick experimentation, while a spatiotemporal model (PL‐UNet), incorporating both historical and
Ziying Yang, Rune Grand Graversen, Filippo Maria Bianchi, Jiping Liu, Ruibo Lei   +4 more
wiley   +1 more source

Enhancing Reference Tracking in Induction Motor Drives: A Hybrid Model Predictive Control and Artificial Neural Network Approach

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT Induction motors (IMs) are widely used in industry for their robustness and cost‐effectiveness, yet their nonlinear and multivariable dynamics pose significant challenges for high‐performance speed control. Model Predictive Control (MPC) is a well‐established solution but suffers from limitations related to model accuracy and parameter ...
Asier del Rio, Oscar Barambones, Eneko Artetxe, Jokin Uralde, Isidro Calvo   +4 more
wiley   +1 more source

Integrating Image Segmentation and Deep Learning to Improve Radio Frequency Propagation Models

International Journal of Satellite Communications and Networking, EarlyView.
ABSTRACT This paper proposes a multi‐sensor approach to improve radio frequency (RF) propagation models, which play a key role in the rapidly expanding field of connected vehicle technology. Focusing on the 1‐ to 20‐GHz frequency range, which is critical for both satellite‐to‐vehicle and base station‐to‐vehicle communications, our study introduces a ...
Jonathan Israel, Xavier Boulanger, Vegard Arneson   +2 more
wiley   +1 more source

How AI and Digital Technologies Can Enhance Sustainable Livestock Manure Management: An Overview From Treatment to Distribution

Sustainable Development, EarlyView.
ABSTRACT Sustainable livestock manure management sits at the nexus of climate, nutrient circularity and water quality. This review explores how artificial intelligence (AI) and digital platforms are used across four management stages, that is, treatment, storage, valorisation and distribution, and figures out where integration fails to deliver ...
Zhan Shi, Feihong Liang, Francesco Marinello, Andrea Pezzuolo   +3 more
wiley   +1 more source

Artificial Intelligence for Blue Transformation: A Review of Predictive Modeling and Decision Support Systems in Sustainable Aquaculture

Sustainable Development, EarlyView.
ABSTRACT The FAO's “blue transformation” roadmap necessitates a fundamental shift towards precision aquaculture to meet global food security targets while minimizing environmental footprints. This review provides a comprehensive overview of how artificial intelligence (AI) and decision support systems (DSS) serve as pivotal enablers for the “better ...
Mustafa Öz, Enes Üstüner
wiley   +1 more source

Integrating multimodal data and machine learning for entrepreneurship research

Strategic Entrepreneurship Journal, EarlyView.
Abstract Research Summary Extant research in neuroscience suggests that human perception is multimodal in nature—we model the world integrating diverse data sources such as sound, images, taste, and smell. Working in a dynamic environment, entrepreneurs are expected to draw on multimodal inputs in their decision making.
Yash Raj Shrestha, Vivianna Fang He
wiley   +1 more source