Marine ecological information prediction by using adjacent location spatiotemporal deep learning model with ensemble learning techniques DOI Creative Commons
Yue‐Shan Chang, Shuting Huang, Haobijam Basanta

и другие.

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102964 - 102964

Опубликована: Дек. 1, 2024

Язык: Английский

Optimized SVR model for predicting dissolved oxygen levels using wavelet denoising and variable reduction: Taking the Minjiang River estuary as an example DOI Creative Commons
Peng Zhang, Xinyang Liu,

Huiru Zhang

и другие.

Ecological Informatics, Год журнала: 2025, Номер 86, С. 103007 - 103007

Опубликована: Янв. 11, 2025

Язык: Английский

Процитировано

1
Emerging Technologies for Automation in Environmental Sensing: Review DOI Creative Commons
Shekhar Suman Borah, Aaditya Khanal, Prabha Sundaravadivel

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(8), С. 3531 - 3531

Опубликована: Апрель 22, 2024

This article explores the impact of automation on environmental sensing, focusing advanced technologies that revolutionize data collection analysis and monitoring. The International Union Pure Applied Chemistry (IUPAC) defines as integrating hardware software components into modern analytical systems. Advancements in electronics, computer science, robotics drive evolution automated sensing systems, overcoming traditional limitations manual collection. Environmental sensor networks (ESNs) address challenges weather constraints cost considerations, providing high-quality time-series data, although issues interoperability, calibration, communication, longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role monitoring due to their versatility cost-effectiveness. Despite regulatory compliance technical limitations, UAVs offer detailed spatial temporal information. Pollution faces related high costs maintenance requirements, prompting exploration cost-efficient alternatives. Smart agriculture encounters hurdle integration, device durability adverse conditions, cybersecurity threats, necessitating privacy-preserving techniques federated learning approaches. Financial barriers, including ongoing maintenance, impede widespread adoption smart technology agriculture. Integrating robotics, notably underwater vehicles, proves indispensable various applications, accurate challenging conditions. review details significant transfer edge computing, which are integral wireless frameworks. These advancements aid underscoring necessity for research innovation enhance solutions. Some state-of-the-art frameworks datasets analyzed provide a comprehensive basic steps involved applications.

Язык: Английский

Процитировано

4
Monitoring and estimation of urban emissions with low-cost sensor networks and deep learning DOI Creative Commons
Huynh Nguyen, Trung H. Le, Merched Azzi

и другие.

Ecological Informatics, Год журнала: 2024, Номер 82, С. 102750 - 102750

Опубликована: Авг. 3, 2024

Sustainable development in cities requires advanced technologies for monitoring and estimating air pollution emissions, which directly affect the health of local inhabitants residents neighborhoods. For this, low-cost sensors information are increasingly used to provide accurate quality forecasts. They are, however, subject data constraints. This paper presents new techniques accurate, reliable forecasting at various scales using from IoT-enabled along with state-run air-quality stations. Here, we develop an extended deep-learning model based on neural networks algorithms optimization hyperparameters network dropout rates. These can yield a significant improvement over 31% prediction accuracy while maintaining coverage approximately 80% air-particle levels 24-h period. The advantages effectiveness our validated verified two real-world scenarios, suburban construction site civil infrastructure project. Comparison analysis is conducted indicate outperformance proposed method recent probabilistic time series estimation regular days extreme events.

Язык: Английский

Процитировано

4
Unveiling the infectious morphological behaviour of banana crop pathogenic nematodes inhabited from soil medium to pseudostem using an artificial intelligence approach DOI

S.S. Jayakrishna,

S. Sankar Ganesh

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 234, С. 110277 - 110277

Опубликована: Март 17, 2025

Язык: Английский

Процитировано

0
Efficient dual-stream neural networks: A modeling approach for inferring wild mammal behavior from video data DOI Creative Commons

Ao Xu,

Zhenjie Hou, Jiuzhen Liang

и другие.

Ecological Informatics, Год журнала: 2024, Номер 84, С. 102902 - 102902

Опубликована: Ноя. 17, 2024

Язык: Английский

Процитировано

0
A Deep Learning System for Water Pollutant Detection Based on the SENSIPLUS Microsensor DOI
Hamza Mustafa, Mario Molinara, Luigi Ferrigno

и другие.

Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 192 - 203

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

0
Marine ecological information prediction by using adjacent location spatiotemporal deep learning model with ensemble learning techniques DOI Creative Commons
Yue‐Shan Chang, Shuting Huang, Haobijam Basanta

и другие.

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102964 - 102964

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

0