We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
61,005 resultsShowing papers similar to GANsemble for Small and Imbalanced Data Sets : A Baseline for Synthetic Microplastics Data
ClearGANsemble for Small and Imbalanced Data Sets: A Baseline for Synthetic Microplastics Data
Researchers developed a generative adversarial network (GAN) ensemble approach to generate synthetic training data for microplastic image classification, addressing the challenge that real microplastic image datasets are small and imbalanced by polymer type. The synthetic augmentation improved classifier accuracy and recall, particularly for underrepresented plastic categories.
Microplastic Identification Using AI-Driven Image Segmentation and GAN-Generated Ecological Context
Researchers built an AI-powered image segmentation system that can automatically identify microplastics in microscopic photos, then used a generative AI model to create synthetic training images to improve its accuracy. The system reached an F1 score of 0.91, outperforming a model trained without generated data, pointing toward faster and cheaper microplastic identification compared to current expert-driven methods.
Microplastic Identification in Seawater using Generative Adversarial Networks
Researchers trained a generative adversarial network (GAN) on microscope images of seawater samples and achieved 92.5% accuracy in automatically distinguishing microplastic particles from natural particulates. This AI-based detection approach could dramatically speed up the analysis of water samples, making routine monitoring of marine microplastic pollution faster and more scalable.
Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learning
Researchers created a new open-source dataset of microscopy images for training AI models to automatically detect and classify micro- and nanoplastics. The dataset fills an important gap in available tools for microplastic research and provides a foundation for developing faster, more efficient methods to identify plastic contamination across environmental samples.
SAM-Augmented Blending for Enhanced Microplastic Detection Using YOLO11
Researchers developed a synthetic data augmentation method using SAM-generated instance masks combined with the YOLOv11 object detection architecture to improve underwater microplastic detection in images. The approach significantly improved detection performance for small, sparsely labeled microplastic objects where real training data is scarce.
Potential threat of microplastics to humans: toxicity prediction modeling by small data analysis
Researchers developed a toxicity prediction model for microplastics using small data analysis techniques, enabling the anticipation of varying toxic effects depending on microplastic types and compositions found in nature.
Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning
Researchers used machine learning to predict microplastic levels in ocean surface waters and identify the key factors driving contamination. Their models found that geographic location, ocean currents, and proximity to populated coastlines were major predictors of microplastic abundance. This approach could help scientists map pollution hotspots without costly and time-consuming physical sampling.
Predicting the toxicity of microplastic particles through machine learning models
Researchers applied machine learning models to predict the toxicity of microplastic particles from their physical and chemical properties, addressing the challenge that microplastics lack the standardized identifiers used for chemical hazard classification. The models successfully predicted toxicity outcomes from particle descriptors, offering a framework for hazard screening of the diverse and complex microplastic contaminant class.
GoogLeNet-Based Deep Learning Framework for Underwater Microplastic Classification in Marine Environments
Researchers trained a GoogLeNet deep learning model on underwater images to classify microplastics into four categories, achieving strong classification performance for primary microplastics, secondary microplastics, non-microplastic debris, and marine biota in turbid coastal waters.
Predicting the toxicity of microplastic particles through machine learning models
Researchers developed machine learning models to predict microplastic particle toxicity from physical and chemical descriptors, addressing the classification challenge posed by the enormous diversity of particle types that cannot be characterized using conventional chemical hazard methods. The models provided accurate toxicity predictions across diverse microplastic types, offering a practical screening tool for the field.
Unraveling the ecotoxicity of micro(nano)plastics loaded with environmental pollutants using ensemble machine learning.
Researchers developed an ensemble machine learning algorithm to predict the ecotoxicity of micro(nano)plastics loaded with environmental pollutants, addressing a key knowledge gap where most studies examine plastic particles alone. The model revealed that co-pollutant loading substantially amplifies toxicity and that particle characteristics govern outcomes.
Microplastic Spectral Classification Using Deep Learning with Denoising and Dimensionality Reduction
Researchers developed a deep learning approach for microplastic spectral classification that incorporates denoising and dimensionality reduction steps, improving the accuracy of identifying and classifying microplastic polymer types from spectral data in marine ecosystems.
Machine Learning Advancements and Strategies in Microplastic and Nanoplastic Detection
This systematic review looks at how machine learning is improving our ability to detect tiny microplastics and nanoplastics in the environment. Better detection methods matter because accurately measuring plastic contamination is the first step toward understanding — and reducing — human exposure.
Efficient Prediction of Microplastic Counts from Mass Measurements
Scientists developed machine learning models to estimate the number of microplastic particles from aggregate weight measurements, potentially offering a faster and cheaper alternative to manual counting. Efficient quantification methods are critical for large-scale monitoring of microplastic contamination in environmental samples.
Automated micro-plastic detection and classification using deep convolution neural network pre-trained models and transfer learning
Researchers compared several artificial intelligence models for automatically detecting and classifying microplastics into categories like beads, fibers, and fragments from images. While the models performed well at identifying fiber-type microplastics, they struggled with beads and fragments, highlighting the need for better training data and techniques. Improving automated detection is important because it could enable faster, cheaper environmental monitoring of microplastic contamination in water and food sources.
Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
Researchers applied dual deep learning models (YOLOv8, YOLOv11, and several CNN architectures) to detect and classify microplastics in water, finding that these AI approaches could accurately identify plastic types across both aquatic and non-aquatic datasets.
Algorithm Comparison for Microplastic Classification: Evaluating Ensemble Models on Density and Measurement Features
Researchers compared machine learning algorithms for classifying microplastic types based on density and measurement features, evaluating ensemble models against standard classifiers. The ensemble approaches outperformed individual models, suggesting that combining multiple algorithms improves automated MP identification from physical measurement data.
Leveraging AI tools for microplastic data quality assessment
Researchers explored how AI tools can improve data quality assessment in microplastic studies, which vary widely in methodological rigor. The approach aims to standardize quality evaluation so that human health risk assessments based on microplastic research are more reliable.
Vision Transformer Model in Environmental Surveillance: Detection of Microplastics for Global Sustainability
Researchers developed a deep learning model using Vision Transformers within a Fast R-CNN framework to automatically detect and quantify microplastics from images. The hybrid model showed strong performance in detecting particles 3 millimeters or larger but had difficulty identifying very small particles. The approach offers a faster and potentially more efficient alternative to traditional laboratory methods for microplastic identification.
Global distribution of marine microplastics and potential for biodegradation
Researchers created a global map predicting marine microplastic pollution using machine learning based on over 9,400 samples and assessed the potential for biodegradation using marine metagenome data. The study found that microplastics converge in subtropical gyres and polar seas, and identified marine microbial communities with genetic potential for plastic biodegradation, suggesting nature may offer partial solutions to this pollution problem.
An ensemble machine learning method for microplastics identification with FTIR spectrum
Researchers developed an ensemble machine learning method to automatically identify microplastics using Fourier transform infrared (FTIR) spectroscopy data. The approach combines multiple classification algorithms to improve accuracy over individual methods for detecting and categorizing microplastic particles. The study suggests this automated approach could help standardize and accelerate microplastic monitoring in marine environments.
Detection of Microplastics Based on a Liquid–Solid Triboelectric Nanogenerator and a Deep Learning Method
Scientists developed a new microplastic detection device based on a liquid-solid friction generator combined with deep learning AI to identify different types of plastic particles. The system can classify microplastics by material type with high accuracy using electrical signals generated when plastic particles contact a liquid surface. This technology could make it easier and cheaper to monitor microplastic contamination in water supplies.
De Novo Design of Multiple Microplastic-Binding Peptideswith a Protein Language Model-Guided Generative Adversarial Network
Researchers used a protein language model combined with a generative adversarial network to design novel peptides predicted to bind multiple types of plastic simultaneously. The AI-generated peptides showed high predicted affinity for polystyrene, polyethylene terephthalate, and polyethylene, offering a new eco-friendly approach for detecting or capturing mixed-plastic microplastic pollution.
Detection of Microplastics Using Machine Learning
Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual methods.