Zobrazeno 1 - 10
of 1 760
pro vyhledávání: '"attention networks"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Aspect-based sentiment analysis (ABSA) is a challenging task due to the presence of multiple aspect words with different sentiment polarities in a sentence. Recently, pre-trained language models like BERT have been widely used as context enc
Externí odkaz:
https://doaj.org/article/5d074dce7029422697431680bcd46843
Autor:
SK Khaja Shareef, R. Krishna Chaitanya, Srinivasulu Chennupalli, Devi Chokkakula, K. V. D. Kiran, Udayaraju Pamula, Ramesh Vatambeti
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract The Internet of Things (IoT) permeates various sectors, including healthcare, smart cities, and agriculture, alongside critical infrastructure management. However, its susceptibility to malware due to limited processing power and security pr
Externí odkaz:
https://doaj.org/article/99cae7d4c0bd47a1b064fb5d9799fce6
Autor:
Yuzhu Teng, Hailan Wu, Xiaoyun Zhou, Feiyang Li, Zhong Dong, Huafeng Wang, Kai Wang, Qianchun Yu
Publikováno v:
Frontiers in Psychology, Vol 15 (2024)
BackgroundSanda, a martial art that primarily involves punching, kicking, and throwing techniques, requires athletes to maintain high levels of concentration during combat. Sanda principally involves striking the opponent to secure victory, with trau
Externí odkaz:
https://doaj.org/article/19cca0e775f44cb88295864d44a84ea3
Publikováno v:
Electronic Research Archive, Vol 32, Iss 4, Pp 2310-2322 (2024)
Graph convolution networks (GCN) have demonstrated success in learning graph structures; however, they are limited in inductive tasks. Graph attention networks (GAT) were proposed to address the limitations of GCN and have shown high performance in g
Externí odkaz:
https://doaj.org/article/5332fa983831429cbab5e0dff4d21a33
Publikováno v:
Electronic Research Archive, Vol 32, Iss 4, Pp 2267-2285 (2024)
Nowadays, advancements in facial recognition technology necessitate robust solutions to address challenges in real-world scenarios, including lighting variations and facial position discrepancies. We introduce a novel deep neural network framework th
Externí odkaz:
https://doaj.org/article/e6581bac6cda405190e71be1e5a97143
Publikováno v:
Heliyon, Vol 10, Iss 16, Pp e35938- (2024)
In previous research, the prevailing assumption was that Graph Neural Networks (GNNs) precisely depicted the interconnections among nodes within the graph's architecture. Nonetheless, real-world graph datasets are often rife with noise, elements that
Externí odkaz:
https://doaj.org/article/b6d14e9dd9f5419b8dc59525a5e2be6a
Publikováno v:
Frontiers in Microbiology, Vol 15 (2024)
IntroductionAccumulating evidence shows that human health and disease are closely related to the microbes in the human body.MethodsIn this manuscript, a new computational model based on graph attention networks and sparse autoencoders, called GCANCAE
Externí odkaz:
https://doaj.org/article/9a9828a7feb240f9a18a21e0e0608281
Publikováno v:
PeerJ Computer Science, Vol 10, p e2200 (2024)
The rapid dissemination of unverified information through social platforms like Twitter poses considerable dangers to societal stability. Identifying real versus fake claims is challenging, and previous work on rumor detection methods often fails to
Externí odkaz:
https://doaj.org/article/754288c9bff74b6b870065462b53e927
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 5, Pp 102070- (2024)
Discovering trending social events (e.g., major meetings, political scandals, natural disasters, etc.) from social messages is vital because it emphasizes important events and can help people comprehend the world. However, the heterogeneous semantics
Externí odkaz:
https://doaj.org/article/517592c600144cd09bd8be7fd4afedea
Publikováno v:
Frontiers in Neurorobotics, Vol 18 (2024)
Next Point-of-Interest (POI) recommendation aims to predict the next POI for users from their historical activities. Existing methods typically rely on location-level POI check-in trajectories to explore user sequential transition patterns, which suf
Externí odkaz:
https://doaj.org/article/b8be33b1b089426eaa52ef6acf71e3e5