Artificial Intelligence in Recordkeeping by Sigit Sumarsono, Muhamad Prabu Wibowo

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Artificial Intellect and Methodology of Economic Science by M. V. Dubovik, S. G. Dmitriev

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Cycle-based formulations in Distance Geometry by Liberti, Leo, Iommazzo, Gabriele, Lavor, Carlile, Maculan, Nelson

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Interpreting Predictive Models through Causality: A Query-Driven Methodology by Mahdi Hadj Ali, Yann Le Biannic, Pierre-Henri Wuillemin

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Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…This study utilizes a range of machine learning algorithms, including linear regression, decision tree, random forest, gradient boosting, K-nearest neighbour, Support Vector Regression, LightGBM and CatBoost, to analyse their effectiveness in predicting pavement performance. …”
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Multiverse Analysis for Dynamic Network Models: Investigating the Influence of Plausible Alternative Modeling Choices by Björn S. Siepe, Daniel W. Heck

“…Individual-level estimates were more heterogeneous, with some decisions strongly influencing results and conclusions. …”
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AI generations: from AI 1.0 to AI 4.0 by Jiahao Wu, Hengxu You, Jing Du

“…This paper proposes that Artificial Intelligence (AI) progresses through several overlapping generations: AI 1.0 (Information AI), AI 2.0 (Agentic AI), AI 3.0 (Physical AI), and a speculative AI 4.0 (Conscious AI). …”
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Data-Driven Neural Differential Equation Model and Stochastic Dynamics for Glioma Prediction by Mohammed Salman, Sanjay Kumar Mohanty

“…Next, we estimate the parameters involved in the mathematical model by fitting them into a set of clinical data and employing a neural ordinary differential equations algorithm. Compared to baseline mechanistic tumor models, our approach reduces prediction root mean squared error to 3.37, demonstrating improved data alignment and forecasting accuracy. …”
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A Review of Research on Path Planning of Unmanned Surface Vessel Swarm: Deep Reinforcement Learning by Yuli HOU, Ning WANG, Chidong QIU, Yongpeng WENG

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Detecting Fake Reviews in E-Commerce: A Case Study on Shopee Using Support Vector Machine and Random Forest by Khoirotulmuadiba Purifyregalia, Khothibul Umam, Nur Cahyo Hendro Wibowo, Maya Rini Handayani

“…This study aims to detect fake reviews on Shopee by applying a text classification approach using Random Forest and Support Vector Machine (SVM) algorithms. A dataset consisting of 3,686 Shopee product reviews was collected and underwent preprocessing steps including data cleaning, normalization, tokenization, and TF-IDF weighting. …”
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Design specifications for biomedical virtual twins in engineered adoptive cellular immunotherapies by Ulrike Weirauch, Markus Kreuz, Colin Birkenbihl, Miriam Alb, Maria Quaranta, Laurence Calzone, Sophia Orozco-Ruiz, Stefanie Binder, Luise Fischer, Solène Clavreul, Morine Maguri, Maximilian Ferle, Michael Rade, Guillaume Azarias, Jay R. Hydren, Jakub Jamarik, Daniel Schwarz, Zsolt Sebestyen, Jurgen Kuball, Georg Popp, Chloé Antoine, Manon Knockaert, Clara T. Schoeder, David Fandrei, Carmen Sanges, Vaclovas Radvilas, Nico Gagelmann, Markus Rückert, Olaf Penack, Stephan Fricke, Andreas Schmidt, Carol Ward, Carl Steinbeisser, Jean-Marc Van Gyseghem, Anna Niarakis, Laurent Garderet, Michael Hudecek, Thomas Neumuth, Uwe Platzbecker, Ulrike Köhl, Regina Demlova, Andreas Kremer, Stefan Franke, Holger Fröhlich, Maximilian Merz, Kristin Reiche, On behalf of the CERTAINTY Consortium

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Eco-Driving Level Evaluation Model for Electric Buses Entering and Leaving Stops by Aihong Lyu, Huiming Zhang, Yubo Shen, Yali Zhang

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Enhancing wheat yellow rust detection through modified deep learning approach by Shant Kumar, Rohit Singh, Sudheer Kumar, Sandeep Gupta

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Advancing Gamma Knife Radiosurgery with Artificial Intelligence: A New Era of Precision and Efficacy by Kavin K. Devani, Subhas K. Konar, Vikas V. Vazhayil, Arun Thimmarayappa, Nishanth Sadashiva

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A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperature by Trilok Nath Pandey, Vishvajeet Ravalekar, Sidharth D. Nair, Sunil Kumar Pradhan

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Application of Machine Learning for Target Selection and Acid Treatment Design by I. I. Mannanov, M. A. Varfolomeev, G. R. Ganieva, A. R. Gimaeva, R. R. Giniyatullin

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Artificial neural networks for predicting first-lactation 305-day milk yield in crossbred cattle by SM Usman, A Kumar, A Sherasiya, IM Youssef, MM Abo Ghanima, J Chandrakar, R Tiwari, NP Singh, QS Sahib, T Dutt, AA Swelum

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Integrating Textual Queries with AI-Based Object Detection: A Compositional Prompt-Guided Approach by Silvan Ferreira, Allan Martins, Daniel G. Costa, Ivanovitch Silva

“…While object detection and recognition have been extensively adopted by many applications in decision-making, new algorithms and methodologies have emerged to enhance the automatic identification of target objects. …”
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Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete by Omobolaji Opafola, Abisola Olayiwola, Ositola Osifeko, Adekunle David, Ajibola Oyedejı

“…Machine learning (ML) algorithms offer an alternative and this study presents a comparative analysis of five ML regression models; Gradient Boosting (GB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), and Linear Regression (LR) on a dataset of 1030 concrete samples. …”
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Highly adaptive Lasso for estimation of heterogeneous treatment effects and treatment recommendation by Nizam Sohail, Codi Allison, Rogawski McQuade Elizabeth, Benkeser David

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