Zobrazeno 1 - 10
of 1 806
pro vyhledávání: '"CNN-LSTM"'
Publikováno v:
Smart Cities, Vol 7, Iss 5, Pp 3022-3054 (2024)
The advent of Sixth Generation (6G) wireless technologies introduces challenges and opportunities for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), necessitating a reevaluation of traditional routing protocols. This paper in
Externí odkaz:
https://doaj.org/article/7d393a574edf4d47a46ef6369cb977d5
Autor:
R. Geethanjali, A. Valarmathi
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-27 (2024)
Abstract In the rapidly evolving field of artificial intelligence, the importance of multimodal sentiment analysis has never been more evident, especially amid the ongoing COVID-19 pandemic. Our research addresses the critical need to understand publ
Externí odkaz:
https://doaj.org/article/123713eca6d145818ceaee8ce932f920
Autor:
Sizhe Deng, Jian Zhou
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 17, Iss 1, Pp 1-12 (2024)
Abstract Accurately predicting the remaining useful life (RUL) of aircraft engines is crucial for maintaining financial stability and aviation safety. To further enhance the prediction accuracy of aircraft engine RUL, a deep learning-based RUL predic
Externí odkaz:
https://doaj.org/article/a457a29d5a014692bbd382e70d3305c7
Autor:
Karl Jensen Cayme, Vince Andrei Retutal, Miguel Edwin Salubre, Philip Virgil Astillo, Luis Gerardo Cañete, Gaurav Choudhary
Publikováno v:
Knowledge, Vol 4, Iss 3, Pp 358-381 (2024)
In response to the recent formalization of Filipino Sign Language (FSL) and the lack of comprehensive studies, this paper introduces a real-time FSL gesture recognition system. Unlike existing systems, which are often limited to static signs and asyn
Externí odkaz:
https://doaj.org/article/efc7d60bcb6d4dc8b3909c723066c824
Autor:
Assila Yousuf, David Solomon George
Publikováno v:
AIMS Electronics and Electrical Engineering, Vol 8, Iss 3, Pp 282-300 (2024)
Singing voice conversion methods encounter challenges in achieving a delicate balance between synthesis quality and singer similarity. Traditional voice conversion techniques primarily emphasize singer similarity, often leading to robotic-sounding si
Externí odkaz:
https://doaj.org/article/84c69169f5174828a94a2c30d3f3298c
Publikováno v:
Green Energy and Intelligent Transportation, Vol 3, Iss 5, Pp 100178- (2024)
Railroad condition monitoring is paramount due to frequent passage through densely populated regions. This significance arises from the potential consequences of accidents such as train derailments, hazardous materials leaks, or collisions which may
Externí odkaz:
https://doaj.org/article/ba39701bba054a5caa2d0b4c6a647250
Autor:
Yungang Tang, Ye Wu
Publikováno v:
Ciência Rural, Vol 55, Iss 2 (2024)
ABSTRACT: This study adopted a more macroscopic perspective to focus on the issue of rural poverty in China. By selecting indicators reflecting various levels of poverty recurrence, considering risk factors across multiple dimensions, and employing a
Externí odkaz:
https://doaj.org/article/2c3810a7d59149cd96d53c893911ca91
Autor:
Imane Hammou Ou Ali, Ali Agga, Mohammed Ouassaid, Mohamed Maaroufi, Ali Elrashidi, Hossam Kotb
Publikováno v:
Frontiers in Energy Research, Vol 12 (2024)
The forecasting of home energy consumption is a crucial and challenging topic within the realm of artificial intelligence (AI)-enhanced energy management in smart grids (SGs). The primary goal of this study is to provide accurate energy consumption f
Externí odkaz:
https://doaj.org/article/b56825a710f14ef28762572b4c3f9dbc
Load forecasting for charging stations considering multiple influencing factors and error correction
Publikováno v:
Zhejiang dianli, Vol 43, Iss 4, Pp 21-28 (2024)
The rapid development of electric vehicles has led to a yearly increase in charging load levels， characterized by strong randomness and unpredictability. Therefore， research on load forecasting for charging stations holds significant importance.
Externí odkaz:
https://doaj.org/article/9f4b44e0fee8463190f7a02532cb690b
Publikováno v:
Alexandria Engineering Journal, Vol 91, Iss , Pp 222-236 (2024)
In this research paper, we propose a novel hybrid deep learning approach, SSA-CNN-LSTM, for forecasting solar power generation. The approach combines Singular Spectrum Analysis (SSA), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (
Externí odkaz:
https://doaj.org/article/655d21a9b07a42f799b24b49b4d6396b