Advanced Neural Network Applications

BehaveAI: a framework for rapidly detecting and classifying objects and behaviour from motion

BehaveAI detects and classifies objects and behaviors from motion using a color-from-motion encoding strategy, enabling efficient and accurate analysis with minimal annotation effort.

PlanktonFlow : hands-on deep-learning classification of plankton images for biologists

PlanktonFlow is a Python pipeline that simplifies deep-learning classification of plankton images, demonstrating that EfficientNet-B5 outperforms other CNNs and EcoTaxa in classification accuracy.

Utilizing CNNs for classification and uncertainty quantification for 15 families of European fly pollinators

This study uses CNNs to classify 15 European fly families and quantify uncertainty, achieving 95.61% accuracy with EfficientNetB4.

Potentials and limitations in the application of Convolutional Neural Networks for mosquito species identification using wing images

This study develops a CNN-based system for mosquito species identification using wing images, achieving high accuracy and demonstrating practical usability in real-world conditions.

Addressing the open world: detecting and segmenting pollen on palynological slides with deep learning

Fusing a small-grain expert detector, fine-tuning across microscopes, human-in-the-loop continual learning and zero-shot SAM-2 segmentation improve detection and isolation of fossil pollen on scanned slides.

DaphnAI: A Deep Learning Approach for High-Throughput Zooplankton Community Analysis

DaphnAI uses deep learning to automate zooplankton identification and segmentation, achieving high accuracy and speed for ecological monitoring.

Dental Age Assessment of White-tailed Deer via Computer Vision and Deep Learning

This paper develops a computer vision model to estimate the age of white-tailed deer using dental features from jawbone images, achieving 90.7% accuracy.

Age Classification of White-tailed Deer Via Computer Vision and Deep Learning

This paper develops a deep learning model for age classification of white-tailed deer using trail camera images, achieving higher accuracy than traditional methods and human experts.

AI-Based Detection of Coliform Colonies Using CNN Transfer Learning for Application to Cultured Plate Analysis in Water Quality Research

This paper develops an AI pipeline using YOLOv8 and transfer learning CNNs to detect and classify coliform colonies on agar plates, achieving faster and more accurate results than traditional methods.

Comparison of a computer vision model to a human observer in detecting African mammals in camera trap images within a safari park

This study compares a computer vision model and human observers in detecting African mammals in camera trap images, finding the model reduces processing time by 82% but undercounts species compared to humans.

A deep learning approach to detect and visualise sexual dimorphism in monomorphic species

This paper uses deep learning to detect sexual dimorphism in sociable weavers, finding higher accuracy than humans and identifying beak regions as key for sex prediction.

A joint probabilistic model of human scene and object recognition via non-hierarchical residual computation

The paper introduces a joint probabilistic model for human scene and object recognition using residual computation, showing functional specialization in coarse-to-fine processing.

The MacqD Deep Learning-based Model for Automatic Detection of Socially Housed Laboratory Macaques

This paper introduces MacqD, a deep learning model for detecting macaques in complex lab environments, achieving high F1-scores across various scenarios.

Deep Learning-Driven Gender Classification of Drosophila melanogaster: Advancing Real-Time Analysis with Mobile Integration

This study uses deep learning models to classify Drosophila melanogaster gender with high accuracy and integrates the models into a mobile app for real-time analysis.

AInsectID Version 1.1: an Insect Species Identification Software Based on the Transfer Learning of Deep Convolutional Neural Networks

AInsectID Version 1.1 is an insect species identification software using transfer learning with CNNs, achieving 99.65% validation accuracy.

Self-supervised Component Segmentation To Improve Object Detection and Classification For Bumblebee Identification

This paper improves bumblebee species identification by integrating object detection with segmentation and classification, enhancing performance in complex images.

Back Home: A Machine Learning Approach to Seashell Classification and Ecosystem Restoration

The paper develops a CNN for seashell classification to aid in ecosystem restoration by accurately identifying confiscated seashells and processing over 36,000 images.

Classification of dog breeds using convolutional neural network models and support vector machine

This study improves dog breed classification accuracy using a combination of CNN features, PCA, GWO, and SVM, achieving 95.24% and 99.34% accuracy for 120 and 76 breeds respectively.

Teaching deep networks to see shape: Lessons from a simplified visual world.

The paper investigates why deep networks rely on color and texture over shape for classification, showing that training methods bias networks toward simpler features.

Towards a Taxonomy Machine. A Training Set of 5.6 Million Arthropod Images

This paper releases a dataset of 5.6 million arthropod images for training machine learning models in taxonomic identification.

Data-centric AI approach for automated wildflower monitoring

The paper introduces a dataset and a PyTorch model for automated wildflower monitoring, achieving a high mAP score for species identification and counting.

YOLOv8 Enables Automated Dragonfly Species Classification Using Wing Images

This study compares YOLOv8 models trained on different preprocessing methods for dragonfly wing images to determine the most effective approach for species classification.

Rough Grouping Enhances YOLO's Pollinator Classification and Detection from Small Datasets

This paper improves YOLO's pollinator classification and detection on small datasets by grouping classes into broader categories, finding that 'Pollinator/Non-pollinator' grouping performs best.

SegColR: Deep Learning for Automated Segmentation and Color Extraction

SegColR automates image segmentation and color extraction using deep learning models, enabling efficient phenotypic data analysis from biodiversity images.

WOLO: Wilson Only Looks Once - Estimating ant body mass from reference-free images using deep convolutional neural networks

This paper develops WOLO, a deep learning model that estimates ant body mass from reference-free images, achieving accuracy comparable to humans and significantly faster.

LizardNet: A mobile hybrid deep learning tool for classification of 3D representations of Amazonian lizards

The paper develops a mobile hybrid deep learning tool for classifying 3D Amazonian lizard species using a dataset from the Emilio Goeldi Museum, achieving high accuracy with MobileNetV3-Small and Linear SVM.

SnakeFace: a transfer learning based app for snake classification

The paper demonstrates the use of transfer learning for snake classification, showing high accuracy in species identification using deep learning models.

Improved prediction of behavioral and neural similarity spaces using pruned DNNs

This paper improves prediction of behavioral and neural similarity spaces by pruning DNN embeddings to align with human judgments and neurobiological data.

Detection and measurement of butterfly eyespot and spot patterns using convolutional neural networks

The paper develops two CNNs to detect and measure butterfly spots and eyespots in wing images, achieving high accuracy in distinguishing these patterns from other color patches.

The role of capacity constraints in Convolutional Neural Networks for learning random versus natural data

This paper investigates how capacity constraints in CNNs affect learning of random versus natural data, finding that internal noise most effectively reduces performance on random data to chance levels.