Results 11 to 20 of about 98,687 (237)
Novel multi‐domain attention for abstractive summarisation
CAAI Transactions on Intelligence Technology, 2023 The existing abstractive text summarisation models only consider the word sequence correlations between the source document and the reference summary, and the summary generated by models lacks the cover of the subject of source document due to models ...
Chunxia Qu +4 more
doaj +1 more source
IET Generation, Transmission & Distribution, 2023 Numerous studies on short‐term load forecasting (STLF) have used feature extraction methods to increase the model's accuracy by incorporating multidimensional features containing time, weather and distance information.
Shiyan Yi +4 more
doaj +1 more source
Brain inspired neuronal silencing mechanism to enable reliable sequence identification
Scientific Reports, 2022 Real-time sequence identification is a core use-case of artificial neural networks (ANNs), ranging from recognizing temporal events to identifying verification codes.
Shiri Hodassman +7 more
doaj +1 more source
The Context-Dependent Additive Recurrent Neural Net [PDF]
Proceedings of the 2018 Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language
Technologies, Volume 1 (Long Papers), 2018 Contextual sequence mapping is one of the fundamental problems in Natural Language Processing (NLP). Here, instead of relying solely on the information presented in the text, the learning agents have access to a strong external signal given to assist the learning process.
Quan Hung Tran +5 more
openaire +1 more source
A multivariate natural gas load forecasting method based on residual recurrent neural network
Electronics Letters, 2023 Current natural gas load forecasting encounters with the conundrum of unsatisfying accuracy and interpretability. To address the challenge, a multi‐variate forecasting method is proposed, which contains three phases: First, an integrate history‐climate ...
Xueqing Ni +3 more
doaj +1 more source
Journal of Intelligent & Fuzzy Systems, 2023 Human Activity Recognition (HAR) has reached its new dimension with the support of Internet of Things (IoT) and Artificial Intelligence (AI).
S. Arokiaraj, N. Viswanathan
semanticscholar +1 more source
Computer Science and Information Systems, 2020 Human Activity Recognition (HAR) is an active research field because of its versatility towards various application areas such as healthcare and lifecare.
E. V. Añazco +4 more
semanticscholar +1 more source
On Learning Interpreted Languages with Recurrent Models
Computational Linguistics, 2022 Can recurrent neural nets, inspired by human sequential data processing, learn to understand language? We construct simplified data sets reflecting core properties of natural language as modeled in formal syntax and semantics: recursive syntactic ...
Denis Paperno
doaj +1 more source
Enhanced road information representation in graph recurrent network for traffic speed prediction
IET Intelligent Transport Systems, 2023 Correctly capturing the spatial‐temporal correlation of traffic sequences will benefit to make accurate predictions of the future traffic states. In the paper, the methods of enhancing road spatial and temporal information representation are proposed ...
Lei Chang +4 more
doaj +1 more source
Comparing Human Activity Recognition Models Based on Complexity and Resource Usage
Applied Sciences, 2021 Human Activity Recognition (HAR) is a field with many contrasting application domains, from medical applications to ambient assisted living and sports applications.
Simon Angerbauer +3 more
doaj +1 more source