Zobrazeno 1 - 10
of 8 031
pro vyhledávání: '"Meta learning"'
Publikováno v:
Smart Cities, Vol 7, Iss 4, Pp 1888-1906 (2024)
The timely and accurate recognition of multi-type structural surface damage (e.g., cracks, spalling, corrosion, etc.) is vital for ensuring the structural safety and service performance of civil infrastructure and for accomplishing the intelligent ma
Externí odkaz:
https://doaj.org/article/5cd82c36ee39463bafb86652e524303e
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2024, Iss 1, Pp 1-8 (2024)
Abstract The disparities in phonetics and corpuses across the three major dialects of Tibetan exacerbate the difficulty of a single task model for one dialect to accommodate other different dialects. To address this issue, this paper proposes task-di
Externí odkaz:
https://doaj.org/article/aca1be2657d9409b9fc2a5c900804861
Publikováno v:
网络与信息安全学报, Vol 10, Iss 3, Pp 107-116 (2024)
Many security risk control issues, such as public opinion analysis in international scenarios, have been identified as text classification problems, which are challenging due to the involvement of multiple languages. Previous studies have demonstrate
Externí odkaz:
https://doaj.org/article/95c87d70a0f24767a1156b3f9c122387
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 17, Iss 1, Pp 1-10 (2024)
Abstract In virtual reality, due to factors such as light sources and surface materials of objects, the details of the scene exhibit extremely complex changes, making it difficult to capture environmental modeling relationships and reducing the quali
Externí odkaz:
https://doaj.org/article/8a82e3cd5d2b42adbafad178496e8d2a
Publikováno v:
Nuclear Engineering and Technology, Vol 56, Iss 6, Pp 1989-2001 (2024)
Artificial intelligence (AI) techniques are now being considered in the nuclear field, but application faces with the lack of actual plant data. For this reason, most previous studies on AI applications in nuclear power plants (NPPs) have relied on s
Externí odkaz:
https://doaj.org/article/a0793bf968154085a4b12c855d73ab63
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 4, Pp 5743-5758 (2024)
Abstract Multi-objective optimization (MOO) endeavors to identify optimal solutions from a finite array of possibilities. In recent years, deep reinforcement learning (RL) has exhibited promise through its well-crafted heuristics in tackling NP-hard
Externí odkaz:
https://doaj.org/article/7f6240bac7dd4d2b8b02559d4ad9d715
Publikováno v:
AIMS Mathematics, Vol 9, Iss 7, Pp 17504-17530 (2024)
Learning from imbalanced data is a challenging task in the machine learning field, as with this type of data, many traditional supervised learning algorithms tend to focus more on the majority class while damaging the interests of the minority class.
Externí odkaz:
https://doaj.org/article/23cf291005224d8d951cab92b08a252c
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-15 (2024)
Abstract Background Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision support systems only pre
Externí odkaz:
https://doaj.org/article/f90c78138b4e4ccb83187efd81455c80
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Due to the challenge of collecting a substantial amount of production-quality data in real-world industrial settings, the implementation of production quality prediction models based on deep learning is not effective. To achieve the goal of
Externí odkaz:
https://doaj.org/article/33e522fabc41492386e90a0bc48dad5f
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 5, Pp 1197-1210 (2024)
Recommender systems provide important functions in areas such as dealing with data overload, providing personalized consulting services, and assisting clients in investment decisions. However, the cold start problem in recommender systems has always
Externí odkaz:
https://doaj.org/article/ba7e441673af4d86810c7904b4ba325d