Biotic resistance predictably shifts microbial invasion regimes by Xiaozhou Ye, Or Shalev, Christoph Ratzke

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ANALYSIS OF PREDICTION METHODS FOR THE FUNCTIONAL DURABILITY OF ROAD MARKINGS by V. V. Golubenko, A. A. Aleksandrov, V. V. Sirotyuk

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Secondary structure of the SARS-CoV-2 genome is predictive of nucleotide substitution frequency by Zach Hensel

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Predictive value of the perivascular fat attenuation index for MACE in young people suspected of CAD by Yani Yu, Dongkai Shan, Xi Wang, Zinuan Liu, Guanhua Dou, Junjie Yang, Yuqi Liu, Yundai Chen

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PREDICTION OF MEAT PRODUCT QUALITY BY THE MATHEMATICAL PROGRAMMING METHODS by A. B. Lisitsyn, M. A. Nikitina, A. N. Zakharov, E. B. Sus, V. V. Nasonova, L. I. Lebedeva

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Predicting Fetal Growth with Curve Fitting and Machine Learning by Huan Zhang, Chuan-Sheng Hung, Chun-Hung Richard Lin, Hong-Ren Yu, You-Cheng Zheng, Cheng-Han Yu, Chih-Min Tsai, Ting-Hsin Huang

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Global genetic variations predict brain response to faces. by Erin W Dickie, Amir Tahmasebi, Leon French, Natasa Kovacevic, Tobias Banaschewski, Gareth J Barker, Arun Bokde, Christian Büchel, Patricia Conrod, Herta Flor, Hugh Garavan, Juergen Gallinat, Penny Gowland, Andreas Heinz, Bernd Ittermann, Claire Lawrence, Karl Mann, Jean-Luc Martinot, Frauke Nees, Thomas Nichols, Mark Lathrop, Eva Loth, Zdenka Pausova, Marcela Rietschel, Michal N Smolka, Andreas Ströhle, Roberto Toro, Gunter Schumann, Tomáš Paus, IMAGEN consortium, IMAGEN consortium, IMAGEN consortium

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Machine Learning‐Enabled Drug‐Induced Toxicity Prediction by Changsen Bai, Lianlian Wu, Ruijiang Li, Yang Cao, Song He, Xiaochen Bo

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Seismic Prediction of Shallow Unconsolidated Sand in Deepwater Areas by Jiale Chen, Yingfeng Xie, Tong Wang, Haoyi Zhou, Zhen Zhang, Yonghang Li, Shi Zhang, Wei Deng

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Predicting survival in malignant glioma using artificial intelligence by Wireko Andrew Awuah, Adam Ben-Jaafar, Subham Roy, Princess Afia Nkrumah-Boateng, Joecelyn Kirani Tan, Toufik Abdul-Rahman, Oday Atallah

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Food Sales Prediction Using MLP, RANSAC, and Bagging by Hussam Mezher Merdas, Ayad Hameed Mousa

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Enhancing Explainability in Predictive Maintenance : Investigating the Impact of Data Preprocessing Techniques on XAI Effectiveness by Mouhamadou Lamine NDAO, Genane YOUNESS, Ndèye NIANG, Gilbert SAPORTA

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Enhancing Predictive Maintenance in Mining Mobile Machinery Through a Hierarchical Inference Network by Raul de la Fuente, Luciano Radrigan, Anibal S. Morales

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Feasibility of the Implementation of Tools for Heart Failure Risk Prediction by Michael C. Wang, MD, Bridget Dolan, PharmD, Xiaoning Huang, PhD, Priya M. Freaney, MD, Benjamin H. Freed, MD, Lourdes Vega, PharmD, Nikola Markoski, PharmD, MBA, Amy Wainright, PharmD, Bonnie Kane, BS, RDCS, Laura E. Seegmiller, MPH, Sanjiv J. Shah, MD, Clyde W. Yancy, MD, MSc, Ian J. Neeland, MD, Hongyan Ning, MD, MS, Donald M. Lloyd-Jones, MD, ScM, Sadiya S. Khan, MD, MSc

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Review on Sound-Based Industrial Predictive Maintenance: From Feature Engineering to Deep Learning by Tongzhou Ye, Tianhao Peng, Lidong Yang

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Interfaith Educational Collaboration Enhances Cultural Adaptation in Lombok and Bali by Fathurrahman Muhtar, Mohamad Abdun Nasir, Sudarsono, Hasniza Nordin

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Crowd Evacuation in Stadiums Using Fire Alarm Prediction by Afnan A. Alazbah, Osama Rabie, Abdullah Al-Barakati

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Predicting pain and its association with mortality in patients with stroke by Adam Viktorisson, Aref Haj Hashem, Katharina S Sunnerhagen, Tamar Abzhandadze

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Machine Learning for the Prediction of Thermodynamic Properties in Amorphous Silicon by Nicolás Amigo

“…The MD simulations provided a detailed dataset that captured the atomic-level behavior of the a-Si, which enabled exploration of how thermodynamic factors, such as the cooling rate, temperature, and pressure, affect the material’s density, internal energy, and enthalpy. Machine learning models were trained on this dataset and demonstrated exceptional predictive accuracy with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> values that exceeded 0.95 and minimal root-mean-square errors. …”
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Evaluating the relative predictive validity of measures of self-referential processing for depressive symptom severity by Ethel Siew Ee Tan, Hong Ming Tan, Kah Vui Fong, Sheryl Yu Xuan Tey, Nikita Rane, Chong Wei Ho, Zhao Yuan Tan, Rachel Jing Min Ong, Chloe Teo, Jerall Yu, Maxine Lee, An Rae Teo, Sin Kee Ong, Xin Ying Lim, Jin Lin Kee, Jussi Keppo, Geoffrey Chern-Yee Tan

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