Machine learning based crystal collimator alignment optimization by Gianmarco Ricci, Marco D’Andrea, Mario Di Castro, Eloise Matheson, Daniele Mirarchi, Andrea Mostacci, Stefano Redaelli

“…A newly developed machine learning model automatically classifies the channeling condition of crystals using beam loss monitor signals during slow rotation of the crystal. …”
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Optimization Techniques for Verification of Out-of-Order Execution Machines by Sudarshan K. Srinivasan

“…We develop two optimization techniques, flush-machine and collapsed flushing, to improve the efficiency of automatic refinement-abased verification of out-of-order (ooo) processor models. …”
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Machine learning approach for optimizing usability of healthcare websites by Amandeep Kaur, Jaswinder Singh, Satinder Kaur

“…Despite the recognized importance of website usability in the healthcare sector, there is a notable lack of empirical studies leveraging machine learning to assess this usability. This study aims to fill this gap by evaluating the user-friendliness of hospital websites using machine learning models, including Decision Trees, Random Forests, Ridge Regression, and Support Vector Regression. …”
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Sustainable soil organic carbon prediction using machine learning and the ninja optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Marwa M. Eid, Marwa M. Eid, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy

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An MILP Model for Optimizing Quality Inspection Allocation with Technology Selection and Variable Sampling Rates by Michele Ronchi, Cristian Cafarella, Matteo Gabellini, Alberto Regattieri, Mauro Gamberi

“…Motivated by gaps in the existing research, this paper proposes a novel MILP model that introduces several previously unaddressed capabilities in inspection planning. …”
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Machine learning-based optimization of photogrammetric JRC accuracy by Qinzheng Yang, Ang Li, Yipeng Liu, Hongtian Wang, Zhendong Leng, Fei Deng

“…Subsequently, a dataset incorporating GSD, point density, RMSE, and three JRC estimation metrics was established, revealing their correlations and sensitivities. Using seven machine learning algorithms, optimization models for photogrammetric JRC accuracy were developed, with Linear Multidimensional Regression and Gaussian Process Regression models improving JRC accuracy by an average of 85.73%. …”
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Computational fluid dynamics and machine learning integration for evaluating solar thermal collector efficiency -Based parameter analysis by Xiaoyu Hu, Lanting Guo, Jiyuan Wang, Yang Liu

“…While baseline LR achieved R² = 0.61, optimized SVR and ANN models demonstrated superior performance with R² values of 0.96 and 0.94 respectively. …”
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Analyzing Financial Stability by Predicting Bankruptcy Situations with Machine Learning by Mohd Naved, Ravi Kumar, Shaiku Saheb

“…This paper endeavors to forecast bankruptcies by leveraging ML models. Specifically, the selected model, Histogram Gradient Boosting Classification (HGBC), is enriched through the integration of the Snake Optimization Algorithm (SOA), Gradient-Based Optimization (GBO), and Bonobo Optimization Algorithm (BOA). …”
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Developing a machine learning model for fast economic optimization of solar power plants using the hybrid method of firefly and genetic algorithms, case study: optimizing solar thermal collector in Calgary, Alberta by Ali Omidkar, Razieh Es'haghian, Hua Song

Subjects: “…machine learning model…”
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Advanced hybrid computational analysis of febuxostat solubility using machine learning in supercritical processing method by Turki Al Hagbani, Rami M. Alzhrani, Majed Ahmed Algarni

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OPTIMIZATION OF THE PROCESS OF FORMING A REGIONAL EQUIPMENT FLEET OFGROUND TRANSPORT AND TECHNOLOGICAL MACHINES by S. Mongush

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Optimization for threat classification of various data types-based on ML model and LLM by Chaerim Hong, Taeyeon Oh

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A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization by Mostafa Akbari, Ezatollah Hassanzadeh, Yaghuob Dadgar Asl, Amirhossein Moghanian

“…AI techniques are primarily employed for modeling, monitoring, optimization, and management of complex systems influenced by multiple parameters within industrial processes. …”
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Optimization of Characteristic Diagram Based Thermal Error Compensation via Load Case Dependent Model Updates by Christian Naumann, Janine Glänzel, Martin Dix, Steffen Ihlenfeldt, Philipp Klimant

“…This allows the optimization of the compensation for serial production load cases without the effort of computing a new model. …”
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Modelling distillate output of a solar distiller with eco-friendly wood-based steam generation basin using machine learning model and starling murmuration optimizer by Mohammed Azmi Al-Betar, Otto L. Muskens, Karrar A. Hammoodi, Mohamed Abd Elaziz, Qusai Yousef Shambour, Salam Fraihat, Ammar H. Elsheikh

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Machine learning-based prediction of optimal antenatal care utilization among reproductive women in Nigeria by Jamilu Sani, Adeyemi Oluwagbemiga, Mohamed Mustaf Ahmed

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Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning models by Xiuxian Chen, Guzhong Chen, Kunchi Xie, Jie Cheng, Jiahui Chen, Zhen Song, Zhiwen Qi

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Optimizing tomato yield prediction using phenologically timed UAV-based spectral data and machine learning by Carolina Trentin, Yiannis Ampatzidis, Sotirios Tasioulas, Pavlos Tsouvaltzis

“…This study evaluated the performance of machine learning models in predicting tomato yield using weather data, spectral bands, and vegetation indices under varying nitrogen rates and bio-stimulant treatments to induce plant growth variability. …”
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End-to-End Detector Optimization with Diffusion Models: A Case Study in Sampling Calorimeters by Kylian Schmidt, Krishna Nikhil Kota, Jan Kieseler, Andrea De Vita, Markus Klute, Abhishek, Max Aehle, Muhammad Awais, Alessandro Breccia, Riccardo Carroccio, Long Chen, Tommaso Dorigo, Nicolas R. Gauger, Enrico Lupi, Federico Nardi, Xuan Tung Nguyen, Fredrik Sandin, Joseph Willmore, Pietro Vischia

Subjects: “…computational modeling…”
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AlphaBind, a domain-specific model to predict and optimize antibody–antigen binding affinity by Aditya A. Agarwal, James Harrang, David Noble, Kerry L. McGowan, Adrian W. Lange, Emily Engelhart, Miranda C. Lahman, Jeffrey Adamo, Xin Yu, Oliver Serang, Kyle J. Minch, Kimberly Y. Wellman, David A. Younger, Randolph M. Lopez, Ryan O. Emerson

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