Results 121 to 130 of about 5,451,620 (296)
Multiprocessing neural network simulator
, 2013 Over the last few years tremendous progress has been made in neuroscience by employing simulation tools for investigating neural network behaviour.
Kulakov, Anton
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Chemie Ingenieur Technik, EarlyView.This work explores generative AI for automated revision of Piping and Instrumentation Diagrams (P&IDs). We frame P&ID correction as a translation problem, converting attributed P&ID graphs into sequences and learning revisions with a transformer‐based model.
Lukas Schulze Balhorn +5 more
wiley +1 more source
An Introduction to Recurrent Neural Networks (RNNs)
<strong> Understanding how RNNs work and its applications </strong> <strong> Author </strong> Wenyi Pi ( <strong> ORCID </strong> : 0009–0002–2884–2771) Introduction In the ever-evolving landscape of artificial intelligence (AI), bridging the gap between humans and machines has seen remarkable progress.
openaire +1 more source
Orchestrating Green Transformation: How AI Adoption Enables Corporate Carbon Neutrality
Corporate Social Responsibility and Environmental Management, EarlyView.ABSTRACT As carbon neutrality has become a central goal of global climate governance, how firms achieve low‐carbon transformation has emerged as a critical research issue. However, prior studies have primarily focused on macro‐ or industry‐level analyses, offering limited and fragmented insights into how digital technologies—particularly AI—affect firm‐
Xiaonan Dong, Sungjin Son
wiley +1 more source
Corporate Social Responsibility and Environmental Management, EarlyView.ABSTRACT This study aims at shedding light on the vast landscape of natural language processing (NLP) tasks used when analyzing sustainability reports or sustainability within integrated annual reports. A systematic literature review is carried out, identifying 160 studies of relevance.
Hannes Cordes
wiley +1 more source
Parallel thinning by a recurrent neural network [PDF]
, 2009 [[abstract]]A 3-layer recurrent neural network is proposed for parallel thinning. This network iteratively removes the contour points of an object shape by template matching. After the neural network converges, a perfectly 8-connected skeleton is derived.
R. Y. Wu;W. H. Tsai
core
Deep Underground Science and Engineering, EarlyView.This paper presents temporal and adaptive‐frequency network with MixStyle (TAMNet), a deep time‐series modeling framework for accurate and robust multi‐well oil productivity forecasting. TAMNet integrates transformer and long short‐term memory architectures to capture both short‐ and long‐term temporal dependencies, enhanced by a temporal gate unit ...
Chunxi Yang +6 more
wiley +1 more source
Neural Network Models for Inflation Forecasting: An Appraisal [PDF]
We assess the power of artificial neural network models as forecasting tools for monthly inflation rates for 28 OECD countries. For short out-of-sample forecasting horizons, we find that, on average, for 45% of the countries the ANN models were a ...
Adnan Haider, Ali Choudhary
core
Eye &ENT Research, EarlyView.ABSTRACT Artificial intelligence (AI) is reshaping ophthalmology from task‐specific image analysis toward multimodal, longitudinal, and clinically integrated decision support. This narrative review summarizes the methodological evolution of ophthalmic AI, including traditional machine learning, task‐specific deep learning, self‐supervised learning ...
Yuxin Liu, Hanruo Liu
wiley +1 more source
Artificial intelligence for adaptive neuromodulation in drug‐resistant epilepsy
Epilepsia, EarlyView.Abstract Drug‐resistant epilepsy (DRE) affects nearly one third of people with epilepsy and is associated with substantial cognitive, psychiatric, and mortality burdens. For patients who are not candidates for resection or laser interstitial thermal therapy, neuromodulation therapies such as vagus nerve stimulation, deep brain stimulation, and ...
Amir Hossein Daraie +10 more
wiley +1 more source