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Ecosystem

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Data-driven modelling for assessing trophic status in marine ecosystems using machine learning approaches.

Environmental research
Assessing eutrophication in coastal and transitional waters is of utmost importance, yet existing Trophic Status Index (TSI) models face challenges like multicollinearity, data redundancy, inappropriate aggregation methods, and complex classification...

Forecasting water quality variable using deep learning and weighted averaging ensemble models.

Environmental science and pollution research international
Water quality variables, including chlorophyll-a (Chl-a), play a pivotal role in comprehending and evaluating the condition of aquatic ecosystems. Chl-a, a pigment present in diverse aquatic organisms, notably algae and cyanobacteria, serves as a val...

Microscopic image recognition of diatoms based on deep learning.

Journal of phycology
Diatoms are a crucial component in the study of aquatic ecosystems and ancient environmental records. However, traditional methods for identifying diatoms, such as morphological taxonomy and molecular detection, are costly, are time consuming, and ha...

Automated software for counting and measuring Hyalella genus using artificial intelligence.

Environmental science and pollution research international
Amphipods belonging to the Hyalella genus are macroinvertebrates that inhabit aquatic environments. They are of particular interest in areas such as limnology and ecotoxicology, where data on the number of Hyalella individuals and their allometric me...

Analysis of land use and land cover change using machine learning algorithm in Yola North Local Government Area of Adamawa State, Nigeria.

Environmental monitoring and assessment
The dynamic use of land that results from urbanization has an impact on the urban ecosystem. Yola North Local Government Area (Yola North LGA) of Adamawa state, Nigeria, has experienced tremendous changes in its land use and land cover (LULC) over th...

Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests.

Nature communications
Tropical forest recovery is fundamental to addressing the intertwined climate and biodiversity loss crises. While regenerating trees sequester carbon relatively quickly, the pace of biodiversity recovery remains contentious. Here, we use bioacoustics...

Three-dimensional forest foodscape in large herbivores' habitat based on UAV with LiDAR detection.

Integrative zoology
With the development of artificial intelligence, the integration of LiDAR technologies and foodscape theories to study wildlife habitat, nutritional ecology, species coexistence, and other existing hot and difficult issues would become an internation...

African bovid tribe classification using transfer learning and computer vision.

Annals of the New York Academy of Sciences
Objective analytical identification methods are still a minority in the praxis of paleobiological sciences. Subjective interpretation of fossils and their modifications remains a nonreplicable expert endeavor. Identification of African bovids is a cr...

A Survey of Deep Learning Techniques for Underwater Image Classification.

IEEE transactions on neural networks and learning systems
In recent years, there has been an enormous interest in using deep learning to classify underwater images to identify various objects, such as fishes, plankton, coral reefs, seagrass, submarines, and gestures of sea divers. This classification is ess...

Leveraging deep learning for automatic recognition of microplastics (MPs) via focal plane array (FPA) micro-FT-IR imaging.

Environmental pollution (Barking, Essex : 1987)
The fast and accurate identification of MPs in environmental samples is essential for the understanding of the fate and transport of MPs in ecosystems. The recognition of MPs in environmental samples by spectral classification using conventional libr...