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75 results for “behavior classification”
FIGURE 9 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 9. Worker caste of Brachyponera croceicornis: lateral and dorsal view of body and full-face view of head (CASENT0172432, April Nobile and www.antweb.org); world distribution of Brachyponera.
FIGURE 1 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 1. Relationships among the genera of Ponerinae based on Schmidt (2013) with additional unpublished data from P.S. Ward. These two sources differ in their included taxa but have the majority of overlapping clades identical. Where sister-group relationships were inconsistent between these two studies taxa were inserted as unresolved nodes or, in cases where this was not possible, placed as indicated in one of the studies. The numbers on the right represent the following genus groups: 1, Pachycondyla; 2, Ponera; 3, Harpegnathos; 4, Hypoponera; 5, Plectroctena; 6, Odontomachus. Cryptopone-a represents C. hartwigi, Cryptopone-b represents C. gilva and C. testacea, Mesoponera-a represents M. melanaria and Mesoponera-b represents M. ambigua.
FIGURE 12 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 12. Worker caste of Euponera antsiraka: lateral and dorsal view of body and full-face view of head (CASENT0317590, Jean Claude Rakotonirina and www.antweb.org).
Illustration of IMTs Behavior in Recurrent Expansion Algorithm for Complex Classification Problems
<p>This video provides a visual representation of the Inputs Mappings and estimated Targets (IMTs) behavior within the Recurrent Expansion Algorithm when addressing highly complex classification problems. Specific details about this experiment and the dataset used can be downloaded from [1]. The video serves as a valuable illustrative resource for understanding the algorithm's performance and its approach to complex classification challenges. It also serves as supplementary material aiding in the understanding of Figure 9 from [1]. For more detailed information, please refer to the accompanying references. Please cite our paper.</p> <p>[1] Berghout, T. & Benbouzid, M. (2024). Empirical Analysis of Aeroengine Inter-Shaft Bearing Faults: Multiverse Recurrent Expansion and Data Quality Enhancement Strategies. 1–21. https://doi.org/http://dx.doi.org/10.2139/ssrn.4833247</p>
Application of Sequential Analysis on Runtime Behavior for Ransomware Classification
<p>This is the data used to create the dataset for ransomware</p>
Data from: Using artificial intelligence classification of videos to examine the environmental, evolutionary and physiological constraints on provisioning behavior
<p>The use of artificial intelligence (AI) technologies can revolutionize how we approach data collection and analysis in behavioral ecology. One such example is in provisioning behavior. Parents of altricial species are selected to provide parental care (such as food provisioning) for their offspring, but there is substantial variation in the level of this care. Provisioning rate may be determined environmentally, by the physiological ability of parents and needs of nestlings, or by evolutionary incentives. We quantified provisioning rate in 20 purple martin (<i>Progne subis</i>) nests in the context of an experimental reduction of nest ectoparasites. 10 nests had a parasite reduction treatment, and 10 nests were controls. By using AI to automate the analysis of nest camera videos we were able to obtain nearly-continuous provisioning rate information at a high temporal resolution for the first half of the nestling period. We used random forest modeling to assess the factors determining provisioning rate and found evidence for environmental, evolutionary and physiological constraints and incentives on provisioning. Birds appeared to be environmentally limited in their provisioning in cool, wet conditions, especially later in the breeding season; but adjusted their provisioning according to the changing physiological needs of nestlings. We found evidence for a compensatory response to increased parasite load, in which parents increased provisioning to more heavily parasitized nests.</p>
Data from: Behavioral classification of low frequency acceleration and temperature data from a free ranging small mammal
1. The miniaturization and affordability of new technology is driving a biologging revolution in wildlife ecology with use of animal-borne data logging devices. Among many new biologging technologies, accelerometers are emerging as key tools for continuously recording animal behavior. Yet a critical, but under-acknowledged consideration in biologging is the trade-off between sampling rate and sampling duration, created by battery- (or memory-) related sampling constraints. This is especially acute among small animals, causing most researchers to sample at high rates for very limited durations.Here, we show that high accuracy in behavioral classification is achievable when pairing low frequency acceleration recordings with temperature. 2. We conducted 84 hours of direct behavioral observations on 67 free-ranging red squirrels (200-300 g) that were fitted with accelerometers (2 g) recording tri-axial acceleration and temperature at 1 Hz. We then used a random forest algorithm and a manually-created decision tree, with variable sampling window lengths, to associate observed behavior with logger recorded acceleration and temperature. Finally, we assessed the accuracy of these different classifications using an additional 60 hours of behavioral observations, not used in the initial classification. 3. The accuracy of the manually-created decision tree classification using observational data varied from 70.6% to 91.6% depending on the complexity of the tree, with increasing accuracy as complexity decreased. Short duration behavior like running had lower accuracy than long duration behavior like feeding. The random forest algorithm offered similarly high overall accuracy, but the manual decision tree afforded the flexibility to create a hierarchical tree, and to adjust sampling window length for behavioral states with varying durations. 4. Low frequency biologging of acceleration and temperature allows accurate behavioral classification of small animals over multi-month sampling durations. Nevertheless, low sampling rates impose several important limitations, especially related to assessing the classification accuracy of short duration behavior.
Supplementary material 9 from: Portman ZM, Orr MC, Griswold T (2019) A review and updated classification of pollen gathering behavior in bees (Hymenoptera, Apoidea). Journal of Hymenoptera Research 71: 171-208. https://doi.org/10.3897/jhr.71.32671
: Data type: multimedia
Supplementary material 6 from: Portman ZM, Orr MC, Griswold T (2019) A review and updated classification of pollen gathering behavior in bees (Hymenoptera, Apoidea). Journal of Hymenoptera Research 71: 171-208. https://doi.org/10.3897/jhr.71.32671
: Data type: multimedia
Supplementary material 7 from: Portman ZM, Orr MC, Griswold T (2019) A review and updated classification of pollen gathering behavior in bees (Hymenoptera, Apoidea). Journal of Hymenoptera Research 71: 171-208. https://doi.org/10.3897/jhr.71.32671
: Data type: multimedia
Supplementary material 5 from: Portman ZM, Orr MC, Griswold T (2019) A review and updated classification of pollen gathering behavior in bees (Hymenoptera, Apoidea). Journal of Hymenoptera Research 71: 171-208. https://doi.org/10.3897/jhr.71.32671
: Data type: multimedia
Supplementary material 8 from: Portman ZM, Orr MC, Griswold T (2019) A review and updated classification of pollen gathering behavior in bees (Hymenoptera, Apoidea). Journal of Hymenoptera Research 71: 171-208. https://doi.org/10.3897/jhr.71.32671
: Data type: multimedia
Supplementary material 4 from: Portman ZM, Orr MC, Griswold T (2019) A review and updated classification of pollen gathering behavior in bees (Hymenoptera, Apoidea). Journal of Hymenoptera Research 71: 171-208. https://doi.org/10.3897/jhr.71.32671
: Data type: multimedia
Supplementary material 3 from: Portman ZM, Orr MC, Griswold T (2019) A review and updated classification of pollen gathering behavior in bees (Hymenoptera, Apoidea). Journal of Hymenoptera Research 71: 171-208. https://doi.org/10.3897/jhr.71.32671
: Data type: multimedia
Supplementary material 2 from: Portman ZM, Orr MC, Griswold T (2019) A review and updated classification of pollen gathering behavior in bees (Hymenoptera, Apoidea). Journal of Hymenoptera Research 71: 171-208. https://doi.org/10.3897/jhr.71.32671
: Data type: multimedia
Supplementary material 1 from: Portman ZM, Orr MC, Griswold T (2019) A review and updated classification of pollen gathering behavior in bees (Hymenoptera, Apoidea). Journal of Hymenoptera Research 71: 171-208. https://doi.org/10.3897/jhr.71.32671
: Data type: multimedia
Figure 7 from: Portman ZM, Orr MC, Griswold T (2019) A review and updated classification of pollen gathering behavior in bees (Hymenoptera, Apoidea). Journal of Hymenoptera Research 71: 171-208. https://doi.org/10.3897/jhr.71.32671
Figure 7 Specialized hairs on the venter of the thorax of APerdita turgiceps Timberlake (Andrenidae) with specialized hairs on the fore-coxae (left) and ventral mesepisternum (right) and BProtandrena maculata Timberlake (Andrenidae) with specialized hairs on the ventral mesepisternum (left) and hind-coxae (far right). Specimens are positioned belly-up, with the head to the left. Scale bars: 200 micrometers.
Figure 4 from: Portman ZM, Orr MC, Griswold T (2019) A review and updated classification of pollen gathering behavior in bees (Hymenoptera, Apoidea). Journal of Hymenoptera Research 71: 171-208. https://doi.org/10.3897/jhr.71.32671
Figure 4 Osmia sp. tapping on Asteraceae. The directionality of the rapid movement of the abdomen is denoted by A down B up, and C down arrows.
Figure 6 from: Portman ZM, Orr MC, Griswold T (2019) A review and updated classification of pollen gathering behavior in bees (Hymenoptera, Apoidea). Journal of Hymenoptera Research 71: 171-208. https://doi.org/10.3897/jhr.71.32671
Figure 6 Osmia sp. (Megachilidae) performing rasping behavior on Penstemon (Plantaginaceae). The bee repeatedly jerks the whole body in and out to rasp the anthers, and then uses the midleg to scrape pollen from the dorsum of the thorax upon exiting the flower.
Figure 3 from: Portman ZM, Orr MC, Griswold T (2019) A review and updated classification of pollen gathering behavior in bees (Hymenoptera, Apoidea). Journal of Hymenoptera Research 71: 171-208. https://doi.org/10.3897/jhr.71.32671
Figure 3 Rubbing with the body and/or scopae. AAndrena sp. (Andrenidae) rubbing with the abdomen and scopae on Camissonia (Onagraceae) BPtilothrix bombiformis (Cresson) (Apidae) rubbing with the abdomen and scopae on Hibiscus (Malvaceae) C, DMelissodes sp. (Apidae) rubbing with the abdomen on Helianthus (Asteraceae) E, FAndrena helianthi Robertson rubbing with the abdomen on HelianthusG, HMacropis sp. (Melittidae) rubbing with the venter of the thorax and abdomen on Lysimachia (Primulaceae).
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.