On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

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15 results for “honeybee hive”

Pixel personality for dense object tracking in a 2D honeybee hivecs.CV

A novel tracking method for dense honeybee colonies using pixel personality.

problem Tracking large numbers of densely-arranged, interacting objects in a 2D environment.

method Segmentation-based object detection followed by adaptive object recognition through visual appearance.

result Reconstructed ~46% of trajectories in 5 minutes and 71% of tracks for at least 2 minutes.

honeybee tracking dense object tracking pixel personality 2D environment object recognition

2018-12-31abs ↗pdf ↗

Towards dense object tracking in a 2D honeybee hivecs.CV

New method tracks all bees in a hive with high accuracy.

problem Efficient tracking of multiple objects in dense configurations.

method Combining CNNs with U-Net architecture and temporal regularities.

result Near human-level performance with reduced network size.

dense object tracking honeybee hive convolutional neural networks U-Net temporal regularities

2017-12-22abs ↗pdf ↗

A mathematical modelization of the prosperity of an Apis Mellifera beehivemath.HO

Math model helps bees decide between winter survival and raising young.

problem Deciding between winter survival and raising young bees in honeybee colonies.

method Mathematical model considering resource geometry around the hive.

result Optimal resource allocation strategy for honeybee colonies.

honeybees resource allocation winter survival bee colony mathematical model

2023-02-27abs ↗pdf ↗

Honey Bee Dance Modeling in Real-time using Machine Learningstat.ML

Automates honeybee dance analysis for real-time food source information.

problem Manual labeling of honeybee dance videos for extracting food source information is time-consuming and inaccurate.

method Proposes an automated process to monitor and segment honeybee dance components.

result Highly accurate real-time analysis of honeybee dance components.

honeybees dance machine learning food source real-time

2017-05-20abs ↗pdf ↗

A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0cs.LG

HIVE-COTE v1.0 improves time series classification with enhanced usability.

problem Improving time series classification accuracy and usability.

method Presented a walkthrough guide and extensive experimental evaluation of HIVE-COTE v1.0.

result HIVE-COTE v1.0 outperforms three recently proposed algorithms in predictive performance and resource usage.

HIVE-COTE time series classification aeon toolkit meta ensemble ensemble learning

2020-04-13abs ↗pdf ↗

InceptionTime: Finding AlexNet for Time Series Classificationcs.LG

InceptionTime improves deep learning for time series classification, balancing accuracy and scalability.

problem Scalability issues in existing time series classification methods.

method InceptionTime, an ensemble of deep CNN models inspired by Inception-v4 architecture.

result InceptionTime achieves similar accuracy to HIVE-COTE but is significantly more scalable.

time series classification deep learning scalability CNN Inception-v4

2019-09-11abs ↗pdf ↗

TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classificationcs.LG

TS-CHIEF improves TSC accuracy while drastically reducing runtime.

problem Infeasibility of HIVE-COTE for large datasets.

method Combines time series embeddings into a tree-structured classifier.

result Achieves state-of-the-art accuracy with 130k time series in 2 days.

Time Series Classification HIVE-COTE TS-CHIEF Tree-Structured Classifier Embeddings

2019-06-25abs ↗pdf ↗

Anomaly Detection in Beehives using Deep Recurrent Autoencoderscs.LG

Deep learning autoencoder detects bee colony anomalies.

problem Early detection of bee swarms and other unusual behaviors.

method Deep Recurrent Autoencoder model trained on sensor data.

result Autoencoder detects anomalies independent of their origin.

beekeeping anomaly detection deep learning autoencoder swarming

2020-03-10abs ↗pdf ↗

Deep Neural Network Ensembles for Time Series Classificationcs.LG

Deep neural networks outperform traditional ensemble methods in time series classification.

problem Deep learning models struggle to match traditional ensemble methods in time series classification.

method Developed an ensemble of 60 deep learning models to improve time series classification performance.

result The proposed Neural Network Ensemble (NNE) outperforms current state-of-the-art methods.

deep learning time series classification ensemble methods neural network ensembles UCR/UEA archive

2019-03-15abs ↗pdf ↗

MultiRocket: Multiple pooling operators and transformations for fast and effective time series classificationcs.LG

MultiRocket boosts TSC speed and accuracy with pooling and transformations.

problem Efficient time series classification with high accuracy.

method Multiple pooling operators and transformations applied to raw and differenced series.

result MultiRocket outperforms MiniRocket and is competitive with state-of-the-art methods in terms of accuracy and speed.

time series classification fast algorithms transformations pooling operators accuracy

2021-01-31abs ↗pdf ↗

Cross-Domain Shopping and Stock Trend Analysisq-fin.ST

Analyzes stock trends and e-commerce user behavior using Twitter data.

problem Understanding the relationship between stock prices, stock news, and e-commerce user behavior.

method Cross-domain analysis using Hadoop, Hive, and Tableau on three datasets.

result Identified correlations between stock sentiment, stock trends, and e-commerce user behavior.

stock analysis e-commerce Twitter sentiment user behavior cross-domain

2022-12-23abs ↗pdf ↗

Benchmarking Multivariate Time Series Classification Algorithmscs.LG

New algorithms benchmarked for multivariate time series classification.

problem Comparing algorithms for multivariate time series classification.

method Review and comparison of recent MTSC algorithms using the UEA archive.

result HIVE-COTE ensemble is most accurate for MTSC, but dynamic time warping is competitive.

time series classification multivariate deep learning shapelets bag of words

2020-07-26abs ↗pdf ↗

From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifierscs.LG

Improved TSC with BOSS and SP techniques.

problem Comparing BOP and BOSS for time series classification.

method Deconstructed and measured components of BOP and BOSS, adapted CV techniques.

result SP with BOSS significantly more accurate than benchmarks.

time series classification Bag of Patterns Bag of Symbolic Fourier Approximation Symbols Scale Invariant Feature Transform Spatial Pyramids

2018-09-18abs ↗pdf ↗

Sparse Multi-Output Gaussian Processes for Medical Time Series Predictionstat.ML

MedGP improves online patient health status prediction using clinical and lab covariates.

problem Real-time monitoring of hospital patients for accurate health status inference.

method Bayesian nonparametric Gaussian process regression with a sparse kernel.

result MedGP significantly improves online prediction accuracy for patient health status across different disease subgroups and studies.

Gaussian process Bayesian nonparametric Electronic health records Medical time series Clinical covariates

2017-03-27abs ↗pdf ↗

Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settingscs.LG

Study compares time series classification algorithms using simulated data.

problem Understanding why some TSC algorithms outperform others.

method Design and implement simulators for five feature spaces, observe classifier performance.

result Ensemble methods often outperform single algorithms, especially when data representation is unknown.

time series classification simulated data ensemble methods feature spaces classifier performance

2017-03-28abs ↗pdf ↗