Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application by Anupam Yadav, B. Jayaprakash, Laith Hussein Jasim, Mayank Kundlas, Maan Younis Anad, Ankur Srivastava, M. Janaki Ramudu, B. Bharathi, Prabhat Kumar Sahu

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Optimizing Concrete Mix Design for Cost and Carbon Reduction Using Machine Learning by Angga T. Yudhistira, Arief S. B. Nugroho, Iman Satyarno, Tantri N. Handayani, Malindu Sandanayake, Rimba Erlangga, Jonathan Lianto, Alfa Rosyid Ernanto

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Investigating the Effects of Labeled Data on Parameterized Physics-Informed Neural Networks for Surrogate Modeling: Design Optimization for Drag Reduction over a Forward-Facing Ste... by Erik Gustafsson, Magnus Andersson

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Integrating clinical predictors and glial fibrillary acidic protein in prediction models for the prehospital identification of intracerebral haemorrhage in suspected stroke by Adrian R Parry-Jones, Mohammed Almubayyidh, David A Jenkins, Edoardo Gaude

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Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices by Laeeq Aslam, Runmin Zou, Ebrahim Shahzad Awan, Sayyed Shahid Hussain, Kashish Ara Shakil, Mudasir Ahmad Wani, Muhammad Asim

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PRINCIPAL COMPONENT ANALYSIS-VECTOR AUTOREGRESSIVE INTEGRATED (PCA-VARI) MODEL USING DATA MINING APPROACH TO CLIMATE DATA IN THE WEST JAVA REGION by Devi Munandar, Budi Nurani Ruchjana, Atje Setiawan Abdullah

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A machine learning model for predicting postoperative complication risk in young and middle-aged patients with femoral neck fractures by Yixin Huang, Yixin Huang, Dongze Lin, Bin Chen, Xiaole Jiang, Shanglin Shangguan, Fengfei Lin

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Markov Model Predicts Changes in STH Prevalence during Control Activities Even with a Reduced Amount of Baseline Information. by Antonio Montresor, Arminder Deol, Natacha À Porta, Nam Lethanh, Dina Jankovic

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MambaLLM: Integrating Macro-Index and Micro-Stock Data for Enhanced Stock Price Prediction by Jin Yan, Yuling Huang

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Identification of key programmed cell death genes for predicting prognosis and treatment sensitivity in colorectal cancer by Jian-ying Ma, Jian-ying Ma, Yi-xian Wang, Zhen-yu Zhao, Zhen-yu Xiong, Zi-long Zhang, Jun Cai, Jia-wei Guo

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Urban Air Quality Shifts in China: Application of Additive Model and Transfer Learning to Major Cities by Yuchen Ji, Xiaonan Zhang, Yueqian Cao

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Flight Trajectory Prediction Based on Automatic Dependent Surveillance-Broadcast Data Fusion with Interacting Multiple Model and Informer Framework by Fan Li, Xuezhi Xu, Rihan Wang, Mingyuan Ma, Zijing Dong

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Ecological and Ergonomic Aspects of Operating Hybrid Sunflower Cultivation Technologies by M. A. Kerimov, V. V. Vetushko

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Bridging the Gap: A Review of Machine Learning in Water Quality Control by Herlina Abdul Rahim, Nur Athirah Syafiqah Noramli, Indrabayu

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Analysis and Prediction of PM2.5 Pollution in Madrid: The Use of Prophet–Long Short-Term Memory Hybrid Models by Jesús Cáceres-Tello, José Javier Galán-Hernández

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The TRIPOD Statement: how to present the results of diagnostic and prognostic studies by I. P. Kolos, D. A. Anichkov, S. A. Boytsov

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NitroNet – a machine learning model for the prediction of tropospheric NO₂ profiles from TROPOMI observations by L. Kuhn, L. Kuhn, S. Beirle, S. Osipov, S. Osipov, A. Pozzer, T. Wagner, T. Wagner

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Development and validation of interpretable machine learning models for predicting AKI risk in patients treated with PD-1/PD-L1: a retrospective study by Wentong Liu, Kaiyue Ji, Qianwen Tang, Weiqi Xia, Wei Zhang, Lina Shao, Jiana Shi, Yukun Li, Ping Huang, Xiaolan Ye

“…Previous research has been constrained by significant limitations, underscoring the necessity for AKI risk prediction models for patients treated with PD-1/PD-L1 inhibitors. …”
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A Novel Empirical Interpolation Surrogate for Digital Twin Wave-Based Structural Health Monitoring with MATLAB Implementation by Abhilash Sreekumar, Linjun Zhong, Dimitrios Chronopoulos

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Evaluating Predictive Accuracy of Regression Models with First-Order Autoregressive Disturbances: A Comparative Approach Using Artificial Neural Networks and Classical Estimators by Rauf I. Rauf, Masad A. Alrasheedi, Rasheedah Sadiq, Abdulrahman M. A. Aldawsari

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