Scorpions of Angola (Arachnida, Scorpiones). Part I. Family Buthidae, with descriptions of two new species.

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  Scorpions of Angola (Arachnida, Scorpiones). Part I. Family Buthidae, with descriptions of two new species. Abstract All scorpion species of the family Buthidae known from Angola are listed, with color photographs and maps of their distribution. Diagnosis of Babycurus ansorgei Hirst, 1911 is revised; its male is described for the first time. Babycurus crassicaudatus Roewer, 1952, is revalidated (it was erroneously synonymized with B. ansorgei , since the females of both species are morphologically almost identical). Uroplectes angolensis sp. n . and U. xavieri sp. n. are described from Angola. Validity of Uroplectes ngangelarum Monard, 1930 is confirmed as different from U. planimanus (Karsch, 1879); its lectotype is designated. Lectotype is also designated for Uroplectes planimanus kuanyamarus Monard, 1937. Female of Uroplectes ebog o Kovařík et al., 2024 from Cameroon is recorded for the first time; its pectines are imaged (Fig. 603). Uroplectes machadoi Lourenço, 2...

Look Out for Dangerous Spiders: Araneae Classification Using Deep Learning Methods

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Look Out for Dangerous Spiders: Araneae Classification Using Deep Learning Methods

Abstract

Of the 50,000 known spider species that taxonomists have identified, a subset of them are considered to be particularly significant due to the harmful physiological effects their venom has on humans, which often demands prompt and precise identification of the spider in emergency scenarios. Traditional spider identification relies on expert knowledge of morphological characteristics for identification, but in critical scenarios this may be inadequate due to time or knowledge constraints. Thanks to the rise of machine learning, we have developed an effective solution to this problem through the testing of powerful deep learning models. In this paper, we utilize various proven image classification models as a backbone, then fine-tune them on a curated dataset of spider images from the citizen science platform iNaturalist with an emphasis on spiders that are particularly harmful to humans. Experimental results are favorable and indicate that modern image classification models perform well on the task of spider species identification. Our highest performing model is a ConvNeXtV2 backbone model which achieves 91.2% accuracy on our testing set. Compared to previous related works, our fine-tuned model is able to achieve higher classification accuracy while handling a much larger number of spider species.

Z. K. Deng and J. J. Rodriguez, "Look Out for Dangerous Spiders: Araneae Classification Using Deep Learning Methods," 2024 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), Santa Fe, NM, USA, 2024, pp. 134-137, doi: 10.1109/SSIAI59505.2024.10508676.