Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration by Maoqiang Lin, Maoqiang Lin, Shaolong Li, Yabin Wang, Yabin Wang, Guan Zheng, Guan Zheng, Fukang Hu, Fukang Hu, Qiang Zhang, Qiang Zhang, Pengjie Song, Haiyu Zhou, Haiyu Zhou

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Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer by Wenwen, Zekun Jiang, Jingyan Liu, Dingbang Liu, Yiyue Li, Yushuang He, Haina Zhao, Lin Ma, Yixin Zhu, Qiongxian Long, Jun Gao, Honghao Luo, Heng Jiang, Kang Li, Xiaorong Zhong, Yulan Peng

“…Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were used to evaluate the prediction performance. …”
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Automated Video Assistant Referee in Lead Climbing by Eliane Künzler, David Roder, Urs Stöcker, Peter Wolf

“…Next, we will compare our scoring algorithm representing given regulations with scores awarded. …”
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Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE by Nan Wei, René Michael Mathy, De-Hua Chang, Philipp Mayer, Jakob Liermann, Christoph Springfeld, Michael T Dill, Thomas Longerich, Georg Lurje, Hans-Ulrich Kauczor, Mark O. Wielpütz, Osman Öcal

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In-Season Automated Mapping of Xinjiang Cotton Based on Cumulative Spectral and Phenological Characteristics by Yongsheng Huang, Yaozhong Pan, Yu Zhu, Xiufang Zhu, Xingsheng Xia, Qiong Chen, Jufang Hu, Hongyan Che, Xuechang Zheng, Lingang Wang

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Development of Artificial Intelligence-Based Programs for the Diagnosis of Myocarditis in COVID-19 Using Chest Computed Tomography Data» by Ievgen A. Nastenko, Maksym O. Honcharuk, Vitalii O. Babenko, Mykola I. Lynnyk, Viktoria I. Ignatieva, Vitalii A. Yachnyk

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Will Artificial Intelligence Replace Physicians or Augment Their Capabilities? by Sara Rahmati Roodsari, Alireza Zali, Mohammad Rahmati-Roodsari, Behina Forouzanmehr

“…In dermatology, AI systems, and deep learning algorithms in particular, have reached levels of comparable accuracy to that of dermatologists in the detection of skin malignancies and inflammatory conditions, highlighting their potential as useful clinical decision-support tools11. …”
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WOMEN'S ENGAGEMENT IN LAW AND DIGITAL TECHNOLOGIES: by William Mokofe Mokofe

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Meta-Features Extracted from Use of kNN Regressor to Improve Sugarcane Crop Yield Prediction by Luiz Antonio Falaguasta Barbosa, Ivan Rizzo Guilherme, Daniel Carlos Guimarães Pedronette, Bruno Tisseyre

“…Accurate crop yield prediction is essential for sugarcane growers, as it enables them to predict harvested biomass, guiding critical decisions regarding acquiring agricultural inputs such as fertilizers and pesticides, the timing and execution of harvest operations, and cane field renewal strategies. …”
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Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru by Lucia Enriquez, Kevin Ortega, Dennis Ccopi, Claudia Rios, Julio Urquizo, Solanch Patricio, Lidiana Alejandro, Manuel Oliva-Cruz, Elgar Barboza, Samuel Pizarro

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Sistem Akuaponik untuk Peternakan Lele dan Tanaman Kangkung Hidroponik Berbasis IoT dan Sistem Inferensi Fuzzy by Fachrul Rozie, Iwan Syarif, Muhammad Udin Harun Al Rasyid, Edi Satriyanto

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Actionable Variants of Unknown Significance in Inherited Arrhythmogenic Syndromes: A Further Step Forward in Genetic Diagnosis by Estefanía Martínez-Barrios, Andrea Greco, José Cruzalegui, Sergi Cesar, Nuria Díez-Escuté, Patricia Cerralbo, Fredy Chipa, Irene Zschaeck, Miguel Fogaça-da-Mata, Carles Díez-López, Elena Arbelo, Simone Grassi, Antonio Oliva, Rocío Toro, Georgia Sarquella-Brugada, Oscar Campuzano

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Joint User Association and Resource Allocation for Hierarchical Federated Learning Based on Games in Satisfaction Form by Panagiotis Charatsaris, Maria Diamanti, Symeon Papavassiliou

“…Diverging from the prevailing research that proposes centralized solution concepts, each user makes autonomous decisions regarding its association and power level so as to attain a minimum acceptable tradeoff of three vital network factors. …”
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Duty of care, data science, and gambling harm: A scoping review of risk assessment models by Virve Marionneau, Kim Ristolainen, Tomi Roukka

“…Our analysis focuses on three critical stages of model development: 1) selection of estimation data; 2) decisions related to the model estimation process; and 3) assessment and interpretation of prediction model results. …”
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Mixed nontuberculous mycobacteria in an immunocompromised patient with progressive multifocal leukoencephalopathy by Dr Christoffel Opperman, Dr Janet Scott, Dr Aliasgar Esmail, Dr Sarishna Singh, Mr Yonas Ghebrekristos, Prof Rob warren, Dr Wynand Goosen

“…This case report sheds light on the intricate decision-making process involved in managing NTM mixed disease, addressing both laboratory and treatment impediments. …”
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Flexible Configurable Modular Neural Network-Based OFDM Receiver by A. B. Sergienko, P. V. Apalina, A. D. Lebedinskaya

“…Variants of the second perceptron were trained for three quadrature modulation (QAM) constellations: 4QAM, 16QAM, and 64QAM.Conclusion. …”
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New framework for the development of clinical guidelines in Russia by D. V. Blinov, E. S. Akarachkova, A. S. Orlova, E. V. Kryukov, D. I. Korabelnikov

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A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME) by C. Mathison, C. Mathison, E. J. Burke, G. Munday, C. D. Jones, C. D. Jones, C. J. Smith, C. J. Smith, C. J. Smith, C. J. Smith, N. J. Steinert, N. J. Steinert, A. J. Wiltshire, A. J. Wiltshire, C. Huntingford, E. Kovacs, L. K. Gohar, R. M. Varney, D. McNeall, D. McNeall

“…These patterns provide the inputs to a “weather generator” algorithm and a land surface model. The PRIME system thus generates an end-to-end estimate of the land surface impacts from the emissions scenarios. …”
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Predictive model for sarcopenia in chronic kidney disease: a nomogram and machine learning approach using CHARLS data by Renjie Lu, Shiyun Wang, Pinghua Chen, Fangfang Li, Fangfang Li, Pan Li, Qian Chen, Xuefei Li, Fangyu Li, Suxia Guo, Jinlin Zhang, Jinlin Zhang, Dan Liu, Zhijun Hu

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Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (<i>Annona squamosa</i> L.) by Xiangtai Jiang, Lutao Gao, Xingang Xu, Wenbiao Wu, Guijun Yang, Yang Meng, Haikuan Feng, Yafeng Li, Hanyu Xue, Tianen Chen

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