Effectiveness of Radiomics-Based Machine Learning Models in Differentiating Pancreatitis and Pancreatic Ductal Adenocarcinoma: Systematic Review and Meta-Analysis by Lechang Zhang, Dewei Li, Tong Su, Tong Xiao, Shulei Zhao

“…Some investigators have explored radiomics-based machine learning (ML) models for distinguishing PDAC from MFP. …”
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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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No-load and on-load performance analysis of 10-stator-slots five phase flux switching machines with non-overlapped winding configurations by Muhammad Yousuf, Faisal Khan, Ahmed Tameemi, Wasiq Ullah

“…An evolutionary optimization process based on the genetic algorithm (GA) integrated with the JMAG software is executed to optimize the machine models, resulting in significant improvements in the analyzed WFFS machine models. …”
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Prediction of Diabetes in Middle-Aged Adults: A Machine Learning Approach by Gideon Addo, Bismark Amponsah Yeboah, Michael Obuobi, Raphael Doh-Nani, Seidu Mohammed, David Kojo Amakye

“…This study focuses on this demographic to examine symptom-diabetes associations, examine the influence of symptoms in diabetes prediction, and determine an optimal machine learning (ML) model for diabetes prediction. …”
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Application of the digital annealer unit in optimizing chemical reaction conditions for enhanced production yields by Shih-Cheng Li, Pei-Hua Wang, Jheng-Wei Su, Wei-Yin Chiang, Tzu-Lan Yeh, Alex Zhavoronkov, Shih-Hsien Huang, Yen-Chu Lin, Chia-Ho Ou, Chih-Yu Chen

“…Our results suggest that the performance of models is comparable to classical machine learning (ML) methods (i.e., Random Forest and Multilayer Perceptron (MLP)), while the inference time of our models requires only seconds with a DAU. …”
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Low-cycle fatigue life prediction method for stud connectors based on interpretable machine learning by Jianan Pan, Xiaoling Liu, Bing Wang, Ying Liu

“…Secondly, the predictive performance of nine machine learning models was compared by combining cross-validation and hyperparameter optimization. …”
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Real-Time Decision Support System for Dynamic Optimization in Multi-Product Process Manufacturing by Mustapha Belmouadden, Camelia Dadouchi, Robert Pellerin

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Optimizing unsupervised feature engineering and classification pipelines for differentiated thyroid cancer recurrence prediction by Emmanuel Onah, Uche Jude Eze, Abdullahi Salahudeen Abdulraheem, Ugochukwu Gabriel Ezigbo, Kosisochi Chinwendu Amorha, Fidele Ntie-Kang

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A tutorial review of policy iteration methods in reinforcement learning for nonlinear optimal control by Yujia Wang, Xinji Zhu, Zhe Wu

“…Additionally, the review addresses practical challenges encountered in real-world applications, such as the development of accurate process models, incorporating safety guarantees during learning, leveraging physics-informed machine learning and transfer learning techniques to overcome learning difficulties, managing model uncertainties, and enabling scalability through distributed RL. …”
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A Digital Twin Framework With Bayesian Optimization and Deep Learning for Semiconductor Process Control by Chin-Yi Lin, Tzu-Liang Tseng, Tsung-Han Tsai

“…By unifying DT-driven real-time insights with advanced machine learning and multi-restart optimization, this framework offers a robust and precise solution for tackling the complexities of modern semiconductor manufacturing.…”
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A Model-Based Heuristic for Packing Soft Rotated Rectangles in an Optimized Convex Container with Prohibited Zones by Oksana Melashenko, Tetyana Romanova, Igor Litvinchev, Carlos Gustavo Martínez Gomez, Rui Yang, Bingtao Sun

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Real-Time Model Predictive Control for Two-Level Voltage Source Inverters with Optimized Switching Frequency by Ariel Villalón, Claudio Burgos-Mellado, Marco Rivera, Rodrigo Zuloaga, Héctor Levis, Patrick Wheeler, Leidy Y. García

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Lymph node metastasis in patients with hepatocellular carcinoma using machine learning: a population-based study by Li Yuqin, Li Yuqin, Li Hongyan, Li Hongyan, Li Hongyuan, Li Tingting, He Kun, Fang Jie, Han Yunhui

“…AimThis study aims to develo\p a population-adapted machine learning-based prediction model for hepatocellular carcinoma (HCC) lymph node metastasis (LNM) to identify high-risk patients requiring intensive surveillance.MethodsData from 23511 HCC patients in the SEER database and 57 patients from our hospital were analyzed. …”
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Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search by Peifeng Wu, Yaqiang Chen

“…To further improve the model’s performance, the sparrow search algorithm (SSA) is employed for parameter optimization, ensuring the best configuration of the CNN-LSTM-Attention framework. …”
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Streamflow Prediction at the Intersection of Physics and Machine Learning: A Case Study of Two Mediterranean‐Climate Watersheds by S. Adera, D. Bellugi, A. Dhakal, L. Larsen

“…Recent studies have examined the use of hybrid models that integrate machine learning models with process‐based (PB) hydrologic models to improve streamflow predictions. …”
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Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques by Stephanie Ness, Vishwanath Eswarakrishnan, Harish Sridharan, Varun Shinde, Naga Venkata Prasad Janapareddy, Vineet Dhanawat

“…By comparing different algorithms, this research contributes to advancing the application of machine learning in network security, offering guidance on model selection and optimization for improved detection of cyber threats.…”
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Optimal Design of Gain-Flattened Raman Fiber Amplifiers Using a Hybrid Approach Combining Randomized Neural Networks and Differential Evolution Algorithm by Jing Chen, Hao Jiang

“…An efficient method based on a hybrid approach that combines extreme learning machine (ELM) technique and differential evolution (DE) algorithm is proposed to optimize the multipumped Raman fiber amplifier (RFA). …”
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Optimizing public health management with predictive analytics: leveraging the power of random forest by Hongman Wang, Yifan Song, Yifan Song, Hua Bi

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Traffic classification model based on fusion of multiple classifiers with flow preference by Shi DONG, Wei DING

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Application of Feedforward Artificial Neural Networks to Predict the Hydraulic State of a Water Distribution Network by Leandro Evangelista, Débora Móller, Bruno Brentan, Gustavo Meirelles

“…Surrogate models based on machine learning are being studied to estimate the hydraulic state of WDNs and reduce the processing time, and the results have been successful. …”
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