Ulnar variance detection from radiographic images using deep learning by Sahar Nooh, Abdelrahim Koura, Mohammed Kayed

“…Our model achieved a segmentation accuracy of 97.7% and an ulna variance classification accuracy of 92.1%. It outperformed previous deep learning-based methods in automatically segmenting the ulna and radius. …”
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MORPHOMETRIC CHARACTERIZATION OF THREE DIFFERENT HONEYBEES (HYMENOPTERA: APIDAE) SPECIES FROM BANGLADESH by Tanzum Islam Nabila, Md Mamunur Rahman, Mst Nura Jahan, Md Ahsanul Haque, Pratik Raj Karki, Mohammad Mehfuz Hassan Saikat

“…The principal component analysis delineated three morphometric characters (along with their morphometric characters in parentheses): The distance of the cubular vein a (19), longitudinal length of the forewing (17), and the angle of venation of the forewing B4 (22), which contributed the most to the variance. Classification of honeybee species using multilayer perceptron considering only these three morphometric characters achieved an accuracy of 100% while significantly reducing the overall computation required. …”
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