Machine learning applied to the design and optimization of polymeric materials: A review by Sudarsan M. Pai, Karim A. Shah, Sruthi Sunder, Rodrigo Q. Albuquerque, Christian Brütting, Holger Ruckdäschel

“…Machine learning (ML) offers a promising solution to this issue. By leveraging ML algorithms, researchers can accelerate the design process, predict material properties more rapidly, and optimize formulations. …”
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MORIX: Machine learning-aided framework for lethality detection and MORtality inference with eXplainable artificial intelligence in MAFLD subjects by Domenico Lofù, Paolo Sorino, Tommaso Colafiglio, Caterina Bonfiglio, Rossella Donghia, Gianluigi Giannelli, Angela Lombardi, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci

“…Moreover, a web application was developed to predict mortality risk and provide explanations of how the input features influenced the final prediction. …”
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Advanced classification of optical water types and ensemble learning models for Chl-a inversion in Dongting and Poyang lakes using Sentinel-2 remote sensing: assessing the impact o... by Kai Xiong, Bin Deng, Jiang Liu, Zhixin Guan, Weizhi Lu, Changbo Jiang, Wei Luo, Han Rao, Longbin Yin, Kang Yang

“…The results demonstrated the superior stability and predictive accuracy of the Voting strategy under low Chl-a conditions in Dongting Lake, achieving a maximum MAPE reduction of 84.76 %. …”
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Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model. by Elias Vera-Siguenza, Cristina Escribano-Gonzalez, Irene Serrano-Gonzalo, Kattri-Liis Eskla, Fabian Spill, Daniel Tennant

“…This study employs an interdisciplinary approach that leverages the predictive power of mathematical modelling to enrich experimental findings. …”
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Diagnostic Analysis of Crosswalk Safety Hazards in Pedestrian Environments: A SHAP-Enhanced Machine Learning Approach With Street-View Imagery by Caryl Anne M. Barquilla, Jeongwoo Lee

“…It used data from 36,750 crosswalks in Seoul, South Korea, to rigorously evaluate multiple machine learning algorithms for predicting pedestrian crash risk. Among the models assessed, the Random forest (RF) demonstrated the highest precision, aligning with the objective of enhancing pedestrian safety through accurate risk identification. …”
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Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation by Karthik Raman, Rukmini Kumar, Cynthia J. Musante, Subha Madhavan

“…Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug–target interactions from big data, enabling more accurate predictions and novel hypothesis generation. …”
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Exploring alumina nanoparticle deposition in heat exchangers with hexagonal tubes: A hybrid approach integrating numerical simulations and machine learning by Seyed Hamed Godasiaei, Pouyan Talebizadehsardari, Amir Keshmiri

“…The proposed hybrid model demonstrated an impressive predictive accuracy of 97 % using the DNN algorithm, confirming its reliability and robustness for industrial applications.…”
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Artificial Intelligence in Digital Marketing: Towards an Analytical Framework for Revealing and Mitigating Bias by Catherine Reed, Martin Wynn, Robin Bown

“…Traditional AI centres on supervised learning algorithms to support and enable the application of data rules, predictive functionality and other AI-based features. …”
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SPL-PlaneTR: Lightweight and Generalizable Indoor Plane Segmentation Based on Prompt Learning by Zhongchen Deng, Yuanlong Ge, Xiatian Qi, Kai Sun, Ruixi Wan, Bingxu Zhang, Shenman Zhang, Xun Zhang, Yan Meng

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Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks by Mundher Mohammed Taresh, Ningbo Zhu, Talal Ahmed Ali Ali, Asaad Shakir Hameed, Modhi Lafta Mutar

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Robust development of data-driven models for methane and hydrogen mixture solubility in brine by Kashif Saleem, Abhinav Kumar, K. D. V. Prasad, Ahmad Alkhayyat, T. Ramachandran, Protyay Dey, Navdeep Kaur, R. Sivaranjani, I. B. Sapaev, Mehrdad Mottaghi

“…The results indicate that Ensemble Learning and AdaBoost yield the highest accuracy algorithms in prediction capability as they tend to illustrate the lowest values of mean squared error and mean absolute relative error (%) and highest R-squared values. …”
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Root-Zone Salinity in Irrigated Arid Farmland: Revealing Driving Mechanisms of Dynamic Changes in China’s Manas River Basin over 20 Years by Guang Yang, Xuejin Qiao, Qiang Zuo, Jianchu Shi, Xun Wu, Alon Ben-Gal

“…The approach demonstrated high predictive accuracy (<i>R</i>² = 0.96 ± 0.01, root mean squared error <i>RMSE</i> = 0.19 ± 0.03 g kg⁻¹, maximum absolute error <i>MAE</i> = 0.14 ± 0.02 g kg⁻¹) in evaluating <i>SSC</i> drivers. …”
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HMSTNet: A Deep Learning Multimodal Approach for Personalized English Literature Recommendations by Xiaopeng Wang, Xue Liang

“…This research serves to unite conventional recommendation algorithms with English literature reading requirements. …”
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Rediscovering histology – the application of artificial intelligence in inflammatory bowel disease histologic assessment by Giovanni Santacroce, Irene Zammarchi, Olga Maria Nardone, Ivan Capobianco, Miguel Puga-Tejada, Snehali Majumder, Subrata Ghosh, Marietta Iacucci

“…Additionally, AI shows promise in predicting adverse outcomes and therapeutic responses, making it a promising tool for clinical practice and clinical trials. …”
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Data-Driven Diagnostics for Pediatric Appendicitis: Machine Learning to Minimize Misdiagnoses and Unnecessary Surgeries by Deborah Maffezzoni, Enrico Barbierato, Alice Gatti

“…In this study, we explore the potential of machine learning (ML) algorithms to improve the diagnosis, management, and prediction of appendicitis severity in pediatric patients. …”
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Streaming LiDAR Scene Flow Estimation by Mazen Abdelfattah, Z. Jane Wang, Rabab Ward

“…Scene flow estimation models this dynamic environment by predicting point motion between sequential point cloud scans, and is crucial for safe navigation. …”
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Safeguarding against Cyber Threats: Machine Learning-Based Approaches for Real-Time Fraud Detection and Prevention by Vikas R. Shetty, Pooja R., Rashmi Laxmikant Malghan

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Uncertainty evaluation of surface profile measurement error based on adaptive sparse grid polynomial chaos expansion by Ke Zhang, Xinya Zheng, Ruiyu Zhang

“…Numerical and experimental validations based on the car door outer surface demonstrate that compared to the ISO standard method, this method achieves a mean prediction error of less than 0.3% and a standard deviation relative error of less than 0.5% with a reduction of 99.6% in the number of sample calculation points. …”
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ACUTE GASTRODUODENAL BLEEDINGS – FROM STRATEGY TO TREATMENT DECISION by V. K. Gostischev, M. A. Evseev

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Thermal boundary condition optimization of feed system based on RSM-IRIME by Haiyang Liu, Xianying Feng, Ming Yao, Anning Wang, Yandong Liu

“…And for the first time, the results are demonstrated that the simulation results solved by the optimized thermal boundary conditions can still achieve the dynamic following of the temperature rise and deformation data under the variable rotational speed, and thus maintain a high predictive accuracy. The effectiveness of the proposed optimization method is verified. …”
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