Smart extrusion via data-driven prediction of grain size and peripheral coarse grain defect formation by Marco Negozio, Lorenzo Donati, Adrian H. A. Lutey

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OKG-ConvGRU: A Domain Knowledge-Guided Remote Sensing Prediction Framework for Ocean Elements by Renhao Xiao, Yixiang Chen, Lizhi Miao, Jie Jiang, Donglin Zhang, Zhou Su

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Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer by Yuan Zeng, Yuhao Chen, Dandan Zhu, Jun Xu, Xiangting Zhang, Huiya Ying, Xian Song, Ruoru Zhou, Yixiao Wang, Fujun Yu

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A paper-and-pencil questionnaire outperforms GPT for measuring cognitive vulnerability to depression and predicting depressive symptoms by Jane K. Stallman, Gerald J. Haeffel

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HPRNA: Predicting synergistic drug combinations for angina pectoris based on human pathway relationship network algorithm. by Mengyao Zhou, Mengfan Xu, Xiangling Zhang, Xiaochun Xing, Yang Li, Guanghui Wang, Guiying Yan

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Spirituality and Religiosity—Do They Always Go Hand in Hand? The Role of Spiritual Transcendence in Predicting Centrality of Religiosity by Dominik Borawski, Katarzyna Lipska, Tomasz Wajs

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Transformer-Based Air-to-Ground mmWave Channel Characteristics Prediction for 6G UAV Communications by Borui Huang, Zhichao Xin, Fan Yang, Yuyang Zhang, Yu Liu, Jie Huang, Ji Bian

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AngleCam: Predicting the temporal variation of leaf angle distributions from image series with deep learning by Teja Kattenborn, Ronny Richter, Claudia Guimarães‐Steinicke, Hannes Feilhauer, Christian Wirth

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Machine learning prediction of breast cancer local recurrence localization, and distant metastasis after local recurrences by Kristóf Attila Kovács, Csaba Kerepesi, Dalma Rapcsák, Lilla Madaras, Ákos Nagy, Anikó Takács, Magdolna Dank, Gyöngyvér Szentmártoni, Attila Marcell Szász, Janina Kulka, Anna Mária Tőkés

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Utilizing machine learning algorithms for predicting Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI) by Min Tan, Jinjin Zhao, Yushun Tao, Uroosa Sehar, Yan Yan, Qian Zou, Qing Liu, Long Xu, Zeyang Xia, Lijuan Feng, Jing Xiong

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Predicting Heat Treatment Duration for Pest Control Using Machine Learning on a Large-Scale Dataset by Stavros Rossos, Paraskevi Agrafioti, Vasilis Sotiroudas, Christos G. Athanassiou, Efstathios Kaloudis

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Mean daily temperatures predict the thermal limits of malaria transmission better than hourly rate summation by Marta S. Shocket, Joey R. Bernhardt, Kerri L. Miazgowicz, Alyzeh Orakzai, Van M. Savage, Richard J. Hall, Sadie J. Ryan, Courtney C. Murdock

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Using artificial neural networks to predict the compressive strength of cement and sawdust ash-treated lateritic soil by James Ukpai, Ugochukwu Okonkwo

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Combining molecular characteristics and therapeutic analysis of PDOs predict clinical responses and guide PDAC personalized treatment by Peng Li, Minli Huang, Mengyao Li, Gen Li, Yifan Ma, Yong Zhao, Xiaowu Wang, Yongbin Zhang, Changhong Shi

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An Approach for Prediction of Acute Hypotensive Episodes via the Hilbert-Huang Transform and Multiple Genetic Programming Classifier by Dazhi Jiang, Liyu Li, Bo Hu, Zhun Fan

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AI-based prediction and detection of early-onset of digital dermatitis in dairy cows using infrared thermography by Marcelo Feighelstein, Amir Mishael, Tamir Malka, Jennifer Magana, Dinu Gavojdian, Anna Zamansky, Amber Adams-Progar

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TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change by Juan Frausto Solís, Erick Estrada-Patiño, Mirna Ponce Flores, Juan Paulo Sánchez-Hernández, Guadalupe Castilla-Valdez, Javier González-Barbosa

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Predicting Landslide Deposit Zones: Insights from Advanced Sampling Strategies in the Ilopango Caldera, El Salvador by Laura Paola Calderon-Cucunuba, Abel Alexei Argueta-Platero, Tomás Fernández, Claudio Mercurio, Chiara Martinello, Edoardo Rotigliano, Christian Conoscenti

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Analysis of mutations in precision oncology using the automated, accurate, and user-friendly web tool PredictONCO by Rayyan Tariq Khan, Petra Pokorna, Jan Stourac, Simeon Borko, Adam Dobias, Joan Planas-Iglesias, Stanislav Mazurenko, Ihor Arefiev, Gaspar Pinto, Veronika Szotkowska, Jaroslav Sterba, Jiri Damborsky, Ondrej Slaby, David Bednar

“…Various algorithms have been developed to predict the impact of missense variants that influence oncogenic drivers. …”
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A machine-learning approach for predicting butyrate production by microbial consortia using metabolic network information by Claudia Silva-Andrade, Sergio Hernández, Pedro Saa, Ernesto Perez-Rueda, Daniel Garrido, Alberto J. Martin

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