Results 221 to 230 of about 212,585 (307)

Short‐Term Multi‐Horizon Line Loss Rate Forecasting of a Distribution Network Using Attention‐GCN‐LSTM

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Accurately predicting line loss rates is crucial for effective management in distribution networks, particularly for short‐term multihorizon forecasts ranging from 1 hour to 1 week. In this study, we propose attention‐GCN–LSTM, a novel method that integrates graph convolutional networks (GCN), long short‐term memory (LSTM) and a three‐level ...
Jie Liu, Yijia Cao, Yong Li, Yixiu Guo, Wei Deng   +4 more
wiley   +1 more source

Deep learning optimized dual-analyte detection-based biosensor for monitoring pregnancy stage using a urine sample. [PDF]

Biomed Opt Express
Ahmed K, Shohidullah M, Mamun Ali M, Bui FM, Chen L, Kumar S.   +5 more
europepmc   +1 more source

Deep learning model for enhanced power loss prediction in the frequency domain for magnetic materials

IET Power Electronics, EarlyView.
This paper outlines the methodology for predicting power loss in magnetic materials. A neural network based method is introduced, which adopts a long short‐term memory network, expressing the core loss as a function of magnetic flux density in the frequency domain, temperature, frequency, and classification of the waveforms.
Dixant Bikal Sapkota, Puskar Neupane, Mecon Joshi, Shahabuddin Khan   +3 more
wiley   +1 more source

Predictive remapping and allocentric coding as consequences of energy efficiency in recurrent neural network models of active vision. [PDF]

Patterns (N Y)
Nortmann T, Sulewski P, Kietzmann TC.
europepmc   +1 more source

High‐throughput information extraction of printed specimen labels from large‐scale digitization of entomological collections using a semi‐automated pipeline

Methods in Ecology and Evolution, EarlyView.
Abstract Natural history museums curate billions of insect specimens, representing an unparalleled record of biodiversity. Although large‐scale digitization has expanded access to specimen images, extracting label metadata remains a major bottleneck, typically requiring time‐intensive manual transcription.
Margot Belot, Joël Tuberosa, Leonardo Preuss, Olha Svezhentseva, Magdalena Claessen, Christian Bölling, Franziska Schuster, Théo Léger   +7 more
wiley   +1 more source

Development and validation of a clinical wearable deep learning based continuous inhospital deterioration prediction model. [PDF]

Nat Commun
Scheid MR, Friedmann B, Oppenheim M, Hirsch JS, Zanos TP.   +4 more
europepmc   +1 more source

Scoping review on natural language processing applications in counselling and psychotherapy

British Journal of Psychology, EarlyView.
Abstract Recent years have witnessed some rapid and tremendous progress in natural language processing (NLP) techniques that are used to analyse text data. This study endeavours to offer an up‐to‐date review of NLP applications by examining their use in counselling and psychotherapy from 1990 to 2021.
Maria Laricheva, Yan Liu, Edward Shi, Amery Wu   +3 more
wiley   +1 more source

Impact Analysis of the Market Penetration Rate of Connected Vehicles and the Failure Rate of Roadside Equipment on Data Accuracy. [PDF]

Sensors (Basel)
Zhan F.
europepmc   +1 more source

Artificial intelligence streamlines scientific discovery of drug–target interactions

British Journal of Pharmacology, EarlyView.
Abstract Drug discovery is a complicated process through which new therapeutics are identified to prevent and treat specific diseases. Identification of drug–target interactions (DTIs) stands as a pivotal aspect within the realm of drug discovery and development. The traditional process of drug discovery, especially identification of DTIs, is marked by
Yuxin Yang, Feixiong Cheng
wiley   +1 more source

Accurate prediction of sepsis from pediatric emergency department to PICU using a machine-learning model. [PDF]

Front Pediatr
Shi X, Wang X, Yang H, Fan X, Liu G, Song Y, Peng Q, Wang Q, Sun X, Ma W, Li P.   +10 more
europepmc   +1 more source