Automated hip dysplasia detection using novel FlexiLBPHOG model with ultrasound images by Sefa Key, Huseyin Kurum, Omer Esmez, Abdul Hafeez Baig, Rena Hajiyeva, Sengul Dogan, Turker Tuncer

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ANALYSIS OF STABILITY OF THE CLASSIFYING RULES PROCESSINGS OF TEXT INFORMATION by M. A. Silbergleit, A. S. Ryzhankova

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Proportion of Variant Anatomy of the Circle of Willis and Association with Vascular Anomalies on Cerebral CT Angiography by Roy Munialo Machasio, Rose Nyabanda, Timothy Musila Mutala

“…Complete CW was seen in 37.2% (37.7% vs. 36.6% in males and females, respectively, p=0.909). …”
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Fluo-Cast-Bright: a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live oocytes by Xiangyu Zhang, Claudia Baumann, Rabindranath De La Fuente

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Sohn-Rethel anthropologue ? by Frédéric Monferrand

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Speech emotion recognition algorithm based on spectrogram feature extraction of deep space attention feature by Jinhua WANG, Na YING, Chendu ZHU, Zhaosen LIU, Zhedong CAI

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Detection of Viable but Nonculturable E. coli Induced by Low-Level Antimicrobials Using AI-Enabled Hyperspectral Microscopy by MeiLi Papa, Aarham Wasit, Justin Pecora, Teresa M. Bergholz, Jiyoon Yi

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Automatic assessment of nailfold capillaroscopy software: a pilot study by Olga Elżbieta Brzezińska, Krzysztof Andrzej Rychlicki-Kicior, Joanna Samanta Makowska

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A deep learning based model for diabetic retinopathy grading by Samia Akhtar, Shabib Aftab, Oualid Ali, Munir Ahmad, Muhammad Adnan Khan, Sagheer Abbas, Taher M. Ghazal

“…In our research, we have developed a deep neural network named RSG-Net (Retinopathy Severity Grading) to classify DR into 4 stages (multi-class classification) and 2 stages (binary classification). …”
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Identification of Maize Kernel Varieties Using LF-NMR Combined with Image Data: An Explainable Approach Based on Machine Learning by Chunguang Bi, Xinhua Bi, Jinjing Liu, He Chen, Mohan Wang, Helong Yu, Shaozhong Song

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Autism spectrum disorder diagnosis with neural networks by Asude Demir, Seher Arslankaya

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Deep learning based gasket fault detection: a CNN approach by S. Arumai Shiney, R. Seetharaman, V. J. Sharmila, S. Prathiba

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Very large scale integration implementation of seizure detection system with on‐chip support vector machine classifier by Shalini Shanmugam, Selvathi Dharmar

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Driver Distraction Identification with an Ensemble of Convolutional Neural Networks by Hesham M. Eraqi, Yehya Abouelnaga, Mohamed H. Saad, Mohamed N. Moustafa

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Optical link establishment method with CoS in ASON by LI Wei1, LI Yuan2, HUANG De-xiu1, LIU De-ming1

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Classifying tumour infiltrating lymphocytes in oral squamous cell carcinoma histopathology using joint learning framework by Barun Barua, Genevieve Chyrmang, Kangkana Bora, Gazi N. Ahmed, Lopamudra Kakoti, Manob Jyoti Saikia

“…An extensive evaluation of our methodology demonstrates that OralTILs-ViT with the configuration (Adam, $$\alpha$$ α = 0.001) outperforms existing approaches, achieving 96.37% accuracy, 96.34% precision, 96.37% recall, and a 96.35% F1 score. …”
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Native Aquatic and Wetland Plants: Cardinal Flower, Lobelia cardinalis by Kimberly A. Moore, Luci E. Fisher, Carl J. Della Torre, Lyn A. Gettys

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MATHEMATICAL MODELLING OF THE UNMANNED AERIAL VEHICLE DYNAMICS by V. Y. Stepanov

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Gestational diabetes versus pregestational diabetes. Perinatal results. by Cristóbal Torres González, Joselin Moreno Torres, Juan Carlos Pastrana Tapia, Vivian Vázquez Martínez

“…According to Marquez Guillén classification , the bad fetal-maternal prognosis (X=16.37, p < 0.05), the treatment with diet plus insulin(X=53.5, p < 0.05),abortio(X=32.2, p < 0.05),pre term delivery (X=11, p < 0.05),maternal morbidity (X=36.8, p < 0.05) and perinatal morbidity (X=39.6, p < 0.05) were the most frequent in pregestationaldiabetes than in gestational diabetes.…”
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Klasifikasi dan Autentifikasi Tanaman Seurapoh (Chromolaena odorata Linn) Menggunakan Metode Kombinasi Spektroskopi Inframerah dan Kemometrik by Taufiq Karma Karma, Pasyamei Rembune Kala, Shofi Shofi, Zafirah Zafirah

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