Hyperspectral Detection of Pesticide Residues in Black Vegetable Based on Multi-Classifier Entropy Weight Method by Rongchang Jiang, Guoqiang Zhuang, Shijie Xie, Yang Wang, Guoqi Zhang, Dandan Qu, Wanzhi Wen

“…The entropy weight method was then used to optimize model weights, developing the multi-classifier entropy weighted method algorithm to improve detection accuracy and robustness. …”
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Estimates of Lake Nitrogen, Phosphorus, and Chlorophyll‐a Concentrations to Characterize Harmful Algal Bloom Risk Across the United States by Meredith M. Brehob, Michael J. Pennino, Amalia M. Handler, Jana E. Compton, Sylvia S. Lee, Robert D. Sabo

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Customizable metamaterial design for desired strain-dependent Poisson’s ratio using constrained generative inverse design network by Sukheon Kang, Hyunggwi Song, Hyun Seok Kang, Byeong-Soo Bae, Seunghwa Ryu

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Framingham Risk Score Prediction at 12 Months in the STANDFIRM Randomized Control Trial by Thanh G. Phan, Velandai K. Srikanth, Dominique A. Cadilhac, Mark Nelson, Joosup Kim, Muideen T. Olaiya, Sharyn M. Fitzgerald, Christopher Bladin, Richard Gerraty, Henry Ma, Amanda G. Thrift

“…Training (n=404) and test (n=103) data were evenly matched for age, sex, baseline, and 12‐month FRS. The optimal model for predicting FRS at 12 months was category boosting (R2=0.712; root mean square error, 7.32). …”
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STSA‐Based Early‐Stage Detection of Small Brain Tumors Using Neural Network by Nafiul Hasan, Md. Masud Rana, Md Mahmudul Hasan, AKM Azad, Dil Afroz, Md Mostafizur Rahman Komol, Mousumi Aktar, Mohammad Ali Moni

“…These findings highlight the potential of STSA‐based machine learning models for accurate, non‐invasive early‐stage brain tumor classification, enabling cost‐effective, scalable diagnostics.…”
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Integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers: A multi-cohort stud... by Hongjie Xin, Qianwei Lai, Yanping Liu, Naying Liao, Ying Wang, Bihong Liao, Keyang Zhou, Yuchen Zhou, Yang Bai, Zhihua Chen, Yuanping Zhou

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Predicting future occlusion or stenosis of lower extremity bypass grafts using artificial intelligence to simultaneously analyze all flow velocities collected in current and previo... by Xiao Luo, PhD, Fattah Muhammad Tahabi, BS, Dave M. Rollins, RVT, Alan P. Sawchuk, MD

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Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples by Sai Spandana Chintapalli, Rongguang Wang, Zhijian Yang, Vasiliki Tassopoulou, Fanyang Yu, Vishnu Bashyam, Guray Erus, Pratik Chaudhari, Haochang Shou, Christos Davatzikos

“…Successful application of machine learning techniques for disease diagnosis, prognosis, and precision medicine, requires large amounts of data for model building and optimization. …”
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Optimizing surface quality in PMEDM using SiC powder material by combined solution response surface methodology – Adaptive neuro fuzzy inference system by Huu Phan Nguyen, Shirguppikar Shailesh, Ganachari Vaibhav, Muthuramalingam Thangaraj, Trong Ly Nguyen

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Response estimation and system identification of dynamical systems via physics-informed neural networks by Marcus Haywood-Alexander, Giacomo Arcieri, Antonios Kamariotis, Eleni Chatzi

“…The results demonstrate that PINNs deliver an efficient tool across all aforementioned tasks, even in the presence of modelling errors. However, these errors tend to have a more significant impact on parameter estimation, as the optimization process must reconcile discrepancies between the prescribed model and the true system behavior. …”
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Improving kinetic model fitting for total titratable acidity in bananas using genetic algorithms by Alejandro Kevin Méndez Castillo, Elizabeth Contreras López, Jesús Guadalupe Pérez Flores, Laura García Curiel, Emmanuel Pérez Escalante, Karla Soto Vega, Carlos Ángel-Jijón, Alicia Cervantes Elizarrarás

“… This research aimed to automate the fitting of kinetic models using genetic algorithms (GAs) to optimize the estimation of kinetic parameters—the reaction rate constant (k) and the initial value of total titratable acidity (TTA, C₀)—and enhance predictive accuracy. …”
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Surrogate modeling for flow simulations using design variable-coded deep learning networks by Racheet Matai

“…This demonstrates its ability to provide precise predictions of near-wall flow behavior for similar geometries, making it a valuable tool for industries requiring swift and reliable CFD predictions for design refinement and optimization. The DV-MLP model is designed primarily for interpolation between geometries and flow conditions represented in the training data, providing accurate predictions for cases that fall within the range of the sampled design space.…”
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Multi-domain Urdu fake news detection using pre-trained ensemble model by Sheetal Harris, Hassan Jalil Hadi, Naveed Ahmad, Mohammed Ali Alshara

“…Previous studies used Machine Learning (ML), Deep Learning (DL), and individual Pre-trained Language Models (PLMs) for Urdu FND. …”
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Entropy driven optimization of non-linear radiative chemically reactive sutterby nanofluid flow in presence of gyrotactic micro-organism with Hall effect and activation energy by Muhammad Jameel, Zahir Shah, Muhammad Rooman, Mansoor H. Alshehri, Narcisa Vrinceanu, Elisabeta Antonescu

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Neutrosophic OWA-TOPSIS Model for Decision-Making in AI Systems with Large Volumes of Data by Juan Roberto Pereira Salcedo, Karla Melissa Ruiz Quezada, Edison Luis Cruz Navarrete, Pedro Manuel García Arias

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Multiscale Feature Modeling and Interpretability Analysis of the SHAP Method for Predicting the Lifespan of Landslide Dams by Zhengze Huang, Yuqi Bai, Hengyu Liu, Yun Lin

“…This study proposes a hybrid CNN–Transformer model optimized using the Improved Black-Winged Kite Algorithm (IBKA) aimed at improving the accuracy of landslide dam lifespan prediction by combining local feature extraction with global dependency modeling. …”
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Dynamic oxygen-redox evolution of cathode reactions based on the multistate equilibrium potential model by Nian Ran, Chengbo Li, Qinwen Cui, Dezhen Xue, Jianjun Liu

“…Abstract Understanding the mechanisms of oxygen anion electrochemical reactions within crystals has long perplexed electrochemical scientists and hindered the structural design and composition optimization of Li-ion cathode materials. Machine learning interatomic potentials (MLIP) are transforming the landscape by enabling high-accuracy atomistic modeling on a large scale in materials science and chemistry. …”
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Developing advanced datadriven framework to predict the bearing capacity of piles on rock by Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom, Ahmed M. Ebid, Fabián Danilo Reyes Silva, José Luis Allauca Palta, José Luis Llamuca Llamuca, Siva Avudaiappan

“…Abstract Developing accurate predictive models for pile bearing capacity on rock is crucial for optimizing foundation design and ensuring structural stability. …”
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Multi-Objective Optimal Control of Variable Speed Alternating Current-Excited Pumped Storage Units Considering Electromechanical Coupling Under Grid Voltage Fault by Tao Liu, Yu Lu, Xiaolong Yang, Ziqiang Man, Wei Yan, Teng Liu, Changjiang Zhan, Xingwei Zhou, Tianyu Fang

“…The weighting factor can be dynamically adjusted based on operating conditions and grid requirements using turbine input power, grid current unbalance, and voltage dip depth as key indicators to achieve adaptive control optimization. Finally, a multi-objective optimization model incorporating coupling characteristics and operational requirements is developed. …”
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Elucidating structure-property correlations in ferroelectric Hf0.5Zr0.5O2 films using variational autoencoders by Kévin Alhada-Lahbabi, Brice Gautier, Damien Deleruyelle, Grégoire Magagnin

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