An artificial intelligence approach to palaeogeographic studies: a case study of the Late Ordovician brachiopods of Laurentia by Akbar Sohrabi

“…Based on the training algorithm and after 146 periods, the training error decreased, but the validation error increased (Fig. 7). …”
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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 comparative study of bone density in elderly people measured with AI and QCT by Min Guo, Min Guo, Yu Zhang, Yu Zhang, XinXin Gu, XinXin Gu, Xuhui Liu, Xuhui Liu, Fei Peng, Fei Peng, Zongjun Zhang, Zongjun Zhang, Mei Jing, Mei Jing, Yingxia Fu, Yingxia Fu

“…Although dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis, many patients have not undergone screening with this technique. …”
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Interleukin-4 and specific IgE to oranges levels study in persons with allergic anamnesis by A. S. Prilutskiy, N. B. Abylgazinova, K. Y. Tkachenko

“…Recent epidemiological studies show that oranges are largely consumed worldwide and commonly included in the diet in many countries being one of the main reasons of food allergy. …”
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Immune training enhances anti-viral responses and improves outcomes in Pax5 −/+ mice susceptible to chronic infection by Zhe Lu, Olivia Stencel, Wei Liu, Eleni Vasileiou, Haifeng C Xu, Piyush Pandey, Paweł Stachura, Abdelrahman Elwy, Anastassia Tsombal, Ann-Sophie Mai, Franziska Auer, Mina N F Morcos, Maximilian Seidl, Sarah Koziel, Peter-Martin Bruch, Sascha Dietrich, Sarah Elitzur, Gunther Hartmann, Karl S Lang, Stefan Janssen, Ute Fischer, Sanil Bhatia, Philipp A Lang, Arndt Borkhardt, Julia Hauer, Aleksandra A Pandyra

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Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data by Theodoros Petropoulos, Lefteris Benos, Remigio Berruto, Gabriele Miserendino, Vasso Marinoudi, Patrizia Busato, Chrysostomos Zisis, Dionysis Bochtis

“…Machine Learning (ML) has shown promise in this field; however, its application to legume crops, especially to lupin, remains limited, while many models lack interpretability, hindering real-world adoption. …”
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Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics by Yushu Xia, Jonathan Sanderman, Jennifer D. Watts, Megan B. Machmuller, Andrew L. Mullen, Charlotte Rivard, Arthur Endsley, Haydee Hernandez, John Kimball, Stephanie A. Ewing, Marcy Litvak, Tomer Duman, Praveena Krishnan, Tilden Meyers, Nathaniel A. Brunsell, Binayak Mohanty, Heping Liu, Zhongming Gao, Jiquan Chen, Michael Abraha, Russell L. Scott, Gerald N. Flerchinger, Patrick E. Clark, Paul C. Stoy, Anam M. Khan, E. N. Jack Brookshire, Quan Zhang, David R. Cook, Thomas Thienelt, Bhaskar Mitra, Marguerite Mauritz‐Tozer, Craig E. Tweedie, Margaret S. Torn, Dave Billesbach

“…Abstract Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. …”
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The use of patient-generated health data in the management of low anterior resection syndrome: a qualitative study by Olivia Monton, Olivia Monton, Allister Smith, Sarah Sabboobeh, Marie Demian, Julie Cornish, Steven D. Wexner, Peter Christensen, Peter Christensen, Amandeep Ghuman, Liliana G. Bordeianou, Liliana G. Bordeianou, Liliana G. Bordeianou, Celia Keane, Celia Keane, Syed Husain, Alessandra Gasior, Alessandra Gasior, Natalie Leon, Julie Savard, Lieba R. Savitt, Margit Majgaard, Gitte Kjær Sørensen, Melanie Mills, Fateme Rajabiyazdi, Marylise Boutros, Marylise Boutros

“…Finally, some participants saw promise in leveraging PGHD to inform the creation of automated treatment algorithms for LARS management.ConclusionsThis study highlights many gaps in the processes of patient-generated LARS data collection and review. …”
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Digital augmentation of aftercare for patients with anorexia nervosa: the TRIANGLE RCT and economic evaluation by Janet Treasure, Katie Rowlands, Valentina Cardi, Suman Ambwani, David McDaid, Jodie Lord, Danielle Clark Bryan, Pamela Macdonald, Eva Bonin, Ulrike Schmidt, Jon Arcelus, Amy Harrison, Sabine Landau

“…Carers particularly valued connection and support from others. Indeed, many carers in the TAU study arm spoke about accessing carer support groups outside of the trial (a factor that may have minimised the difference between groups). …”
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Development and retrospective validation of an artificial intelligence system for diagnostic assessment of prostate biopsies: study protocol by Martin Eklund, Brett Delahunt, Lars Egevad, Hemamali Samaratunga, Mattias Rantalainen, Marcello Gambacorta, Einar Gudlaugsson, Bodil Ginnerup Pedersen, Xiaoyi Ji, Karina Dalsgaard Sørensen, Kelvin Szolnoky, Nita Mulliqi, Anders Blilie, Henrik Olsson, Matteo Titus, Geraldine Martinez Gonzalez, Sol Erika Boman, Masi Valkonen, Svein Reidar Kjosavik, José Asenjo, Paolo Libretti, Marcin Braun, Radzislaw Kordek, Roman Łowicki, Kristina Hotakainen, Päivi Väre, Benedicte Parm Ulhøi, Pekka Ruusuvuori, Toyonori Tsuzuki, Emilius Adrianus Maria Janssen, Kimmo Kartasalo

“…Digital pathology and artificial intelligence (AI) algorithms for analysing whole slide images show promise in improving the accuracy and efficiency of histopathological assessments. …”
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The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients by Zhou Liu, Guijun Jiang, Liang Zhang, Palpasa Shrestha, Yugang Hu, Yi Zhu, Guang Li, Yuanguo Xiong, Liying Zhan

“…The Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors (SMOTE-ENN) and Adaptive Synthetic Sampling (ADASYN) were adopted to address the imbalance of the dataset. As many as 12 machine learning (ML) algorithms, namely, logistic regression (LR), decision tree (DT), random forest (RF), gradient boosting (GB), AdaBoost, XGBoost, Naive Bayes (NB), support vector machine (SVM), light gradient-boosting machine (LightGBM), K-nearest neighbors (KNN), extremely randomized trees (ET), and voting classifier (VC), were performed. …”
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Providing a Framework for Assessing and Evaluating Network Data Studies in the Fight Against Social Anomalies by Mohammad Khalili, Hamzehali Nourmohammadi, Nader Naghshineh

“…Data mining is regarded as one of the most effective tools for data and information analysis in the field of crime detection. Many effective parameters are available for analyzing crime through data mining methods. …”
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Application of Concentration-Area fractal modeling and artificial neural network to identify Cu, Zn±Pb geochemical anomalies in Hashtjin area, NW of Iran by Ali Imamalipour, Hamed Ebrahimi, Amir reza Abdollahpur

“…Recent research investigations have shown that Machine Learning (ML) algorithms can identify geochemical anomalies associated with mineralization that represent targets for mineral exploration. …”
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A public benchmark for human performance in the detection of focal cortical dysplasia by Lennart Walger, Matthias H. Schmitz, Tobias Bauer, David Kügler, Fabiane Schuch, Christophe Arendt, Tobias Baumgartner, Johannes Birkenheier, Valeri Borger, Christoph Endler, Franziska Grau, Christian Immanuel, Markus Kölle, Patrick Kupczyk, Asadeh Lakghomi, Sarah Mackert, Elisabeth Neuhaus, Julia Nordsiek, Anna‐Maria Odenthal, Karmele Olaciregui Dague, Laura Ostermann, Jan Pukropski, Attila Racz, Klaus von derRopp, Frederic Carsten Schmeel, Felix Schrader, Aileen Sitter, Alexander Unruh‐Pinheiro, Marilia Voigt, Martin Vychopen, Philip vonWedel, Randi vonWrede, Ulrike Attenberger, Hartmut Vatter, Alexandra Philipsen, Albert Becker, Martin Reuter, Elke Hattingen, Alexander Radbruch, Rainer Surges, Theodor Rüber

“…A myriad of algorithms for automated FCD detection have been developed, but their true clinical value remains unclear since there is no benchmark dataset for evaluation and comparison to human performance. …”
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Project quality, regulation quality by Elena Mussinelli

“…Instead, deductive design approaches seem to prevail today, due to the growing availability of algorithmic procedures that do not merely support the design process, but develop it in an almost automated manner through conditioning and prevailing indicators and parameters. …”
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Permafrost–wildfire interactions: active layer thickness estimates for paired burned and unburned sites in northern high latitudes by A. C. Talucci, M. M. Loranty, J. E. Holloway, B. M. Rogers, H. D. Alexander, N. Baillargeon, J. L. Baltzer, L. T. Berner, A. Breen, L. Brodt, B. Buma, B. Buma, J. Dean, C. J. F. Delcourt, L. R. Diaz, C. M. Dieleman, T. A. Douglas, G. V. Frost, B. V. Gaglioti, R. E. Hewitt, T. Hollingsworth, T. Hollingsworth, M. T. Jorgenson, M. J. Lara, R. A. Loehman, M. C. Mack, K. L. Manies, C. Minions, S. M. Natali, J. A. O'Donnell, D. Olefeldt, A. K. Paulson, A. V. Rocha, L. B. Saperstein, T. A. Shestakova, T. A. Shestakova, T. A. Shestakova, S. Sistla, O. Sizov, A. Soromotin, M. R. Turetsky, S. Veraverbeke, M. A. Walvoord

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Modeling Habitat Suitability of Anser Anser in Iran by Fatemeh Biglari Quchan Atiq, Azita Farashi, Mitra Shariati Najafabadi

“…The findings from evaluating the algorithms used in modeling the habitat types of the Anser anser species reveal that the biomod software package has a high ability to predict the optimal habitats of this species. …”
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Smart CAR-T Nanosymbionts: archetypes and proto-models by Juan C. Baena, Juan C. Baena, Juan Sebastián Victoria, Juan Sebastián Victoria, Alejandro Toro-Pedroza, Alejandro Toro-Pedroza, Cristian C. Aragón, Cristian C. Aragón, Joshua Ortiz-Guzman, Joshua Ortiz-Guzman, Joshua Ortiz-Guzman, Juan Esteban Garcia-Robledo, Juan Esteban Garcia-Robledo, David Torres, Lady J. Rios-Serna, Lady J. Rios-Serna, Ludwig Albornoz, Joaquin D. Rosales, Joaquin D. Rosales, Carlos A. Cañas, Carlos A. Cañas, Carlos A. Cañas, Gustavo Adolfo Cruz-Suarez, Gustavo Adolfo Cruz-Suarez, Gustavo Adolfo Cruz-Suarez, Gustavo Adolfo Cruz-Suarez, Felipe Ocampo Osorio, Felipe Ocampo Osorio, Felipe Ocampo Osorio, Tania Fleitas, Ivan Laponogov, Alexandre Loukanov, Alexandre Loukanov, Alexandre Loukanov, Kirill Veselkov, Kirill Veselkov

“…At the same time, artificial intelligence (AI), with its powerful algorithms for data analysis and predictive modeling, is transforming how we design, evaluate, and monitor advanced therapies, including the optimization of manufacturing processes. …”
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Radiative closure assessment of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-DF product by H. W. Barker, J. N. S. Cole, N. Villefranque, Z. Qu, A. Velázquez Blázquez, C. Domenech, S. L. Mason, R. J. Hogan

“…Note that this study, like the ACMB-DF process with real EarthCARE observations, does not comment explicitly on performance of retrieval algorithms.</p>…”
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Evaluation of Knowledge Management Maturity Level in Iranian Audiovisual Archives Based on the APQC Model by Sepideh Ciruskabiri, Atefeh Sharif, Saeed Rezaei Sharifabadi, Mohammad Hassanzadeh

“…Moreover, investment in modernizing technological infrastructures and integrating innovative tools—such as artificial intelligence and machine learning algorithms—can markedly enhance the processes of data collection, storage, and retrieval. …”
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