Machine learning optimization of microwave-assisted extraction of phenolics and tannins from pomegranate peel by Fatemeh Mobasheri, Mostafa Khajeh, Mansour Ghaffari-Moghaddam, Jamshid Piri, Mousa Bohlooli

“…Thirty experiments were conducted using the microwave extraction system. Two machine learning models, LSBoost with Random Forest (LSBoost/RF) and LSBoost with K-Nearest Neighbors Neural Network (LSBoost/KNN-NN), were developed and compared for predicting extraction outcomes. …”
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Research on Machining Parameter Optimization and an Electrode Wear Compensation Method of Microgroove Micro-EDM by Xiaodong Zhang, Wentong Zhang, Peng Yu, Yiquan Li

“…Therefore, based on an established L27 orthogonal experiment, this paper uses the grey relational analysis (GRA) method to realize multi-objective optimization of machining time and electrode wear, so as to achieve the shortest machining time and the minimum electrode wear during machining under the optimal machining parameter combination. …”
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Inverse Design of Plasmonic Nanostructures Using Machine Learning for Optimized Prediction of Physical Parameters by Luana S. P. Maia, Darlan A. Barroso, Aêdo B. Silveira, Waleska F. Oliveira, André Galembeck, Carlos Alexandre R. Fernandes, Dayse G. C. Bandeira, Benoit Cluzel, Auzuir R. Alexandria, Glendo F. Guimarães

“…The results indicate that machine learning models are promising tools for optimizing the design and characterization of plasmonic nanostructures, thus reducing the need for costly experimental techniques.…”
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Deep Learning Model of Image Classification Using Machine Learning by Qing Lv, Suzhen Zhang, Yuechun Wang

“…Finally, the structure of the deep learning model was optimized to improve the classification efficiency and accuracy of the model. …”
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Integrated Machine Learning Models for Bakery Product Defect Prediction by Nataliia Zaiets, Nataliia Lutska, Lidiia Vlasenko

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A Machine Learning Model Relating Xrain and Rain Gauge by Miao Zhang, Christopher Gomez, Balazs Bradak, Hotta Norifumi, Shinohara Yoshinori

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Energy Optimization in Ultrasound Tomography Through Sensor Reduction Supported by Machine Learning Algorithms by Bartłomiej Baran, Tomasz Rymarczyk, Dariusz Majerek, Piotr Szyszka, Dariusz Wójcik, Tomasz Cieplak, Marcin Gąsior, Marcin Marczuk, Edmund Wąsik, Konrad Gauda

“…In subsequent steps, machine learning models are developed to predict the number, location, and size of the objects. …”
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Machine Learning for Decision Support and Automation in Games: A Study on Vehicle Optimal Path by Gonçalo Penelas, Luís Barbosa, Arsénio Reis, João Barroso, Tiago Pinto

“…Additionally, this work emphasizes the utility of using games to evolve such models, preparing them for real-world applications, namely in the field of vehicles’ autonomous driving and optimal route calculation.…”
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ANN and machine learning based predictions of MRR in AWSJ machining of CFRP composites by K. Ramesha, N. Santhosh, B. A. Praveena, Banakara Nagaraj, N. Channa Keshava Naik, Quadri Noorulhasan Naveed, Ayodele Lasisi, Anteneh Wogasso Wodajo

“…The integration of RSM and machine learning (ML) techniques enabled effective optimization of machining parameters, showcasing the potential for cost-effective and high-precision CFRP machining. …”
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Design Optimization of a 3-PUU Parallel Machine for Friction Stir Welding Robots by Eka Marliana, Latifah Nurahmi, Arif Wahjudi, I. Made Londen Batan, Guowu Wei

“…A prototype of the optimal design was built, and a series of experiments were conducted to verify and validate the kinematic and dynamic models within the defined cuboid workspace, which provides foundations for further industrial applications of the proposed machine in FSW.…”
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Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques by Hamed Shokrnia, Ashkan KhodabandehLou, Peyman Hamidi, Fedra Ashrafzadeh

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Optimized YOLOv8 for enhanced breast tumor segmentation in ultrasound imaging by Ayman Mohamed Mostafa, Alaa S. Alaerjan, Bader Aldughayfiq, Hisham Allahem, Alshimaa Abdelraof Mahmoud, Wael Said, Hosameldeen Shabana, Mohamed Ezz

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Soft-sensor modeling of silicon content in hot metal based on sparse robust LS-SVR and multi-objective optimization by GUO Dong-wei, ZHOU Ping

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SIBILA: Automated Machine-Learning-Based Development of Interpretable Machine-Learning Models on High-Performance Computing Platforms by Antonio Jesús Banegas-Luna, Horacio Pérez-Sánchez

“…As machine learning (ML) transforms industries, the need for efficient model development tools using high-performance computing (HPC) and ensuring interpretability is crucial. …”
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Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution S... by Amarendra Alluri, Srinivasa Rao Gampa, Balaji Gutta, Mahesh Babu Basam, Kiran Jasthi, Nibir Baran Roy, Debapriya Das

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Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification by Tales Boratto, Gabriel de Oliveira Costa, Alexsandro Meireles, Anna Klara Sá Teles Rocha Alves, Camila M. Saporetti, Matteo Bodini, Alexandre Cury, Leonardo Goliatt

“…In addition, there is also scope for further improvement through the application of still under-utilized optimization techniques. Thus, the present article proposes a novel approach that leverages the Differential Evolution (DE) algorithm to optimize hyperparameters within three selected ML models, with the aim of classifying singing-voice registers i.e., chest, mixed, and head registers). …”
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Improving frequency stability in grid-forming inverters with adaptive model predictive control and novel COA-jDE optimized reinforcement learning by Muhammad Zubair Yameen, Zhigang Lu, Fayez F. M. El-Sousy, Waqar Younis, Baqar Ali Zardari, Abdul Khalique Junejo

Subjects: “…Model Predictive Control…”
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Toward sustainable machining of hardened SKD11: Machine learning-based evaluation and optimization of surface roughness, tool wear, and CO2 emissions by Van-Canh Nguyen, Dung Hoang Tien, Van-Hung Pham, Thinh-Viet Nguyen, Thuy-Duong Nguyen

“…This research proposes an integrated methodology combining Random Forest regression modeling with the NSGA-III evolutionary algorithm for simultaneous optimization of surface quality and environmental performance during fine turning of SKD11 steel under hybrid cooling conditions merging Minimum Quantity Lubrication (MQL) with Vortex tube cooling. …”
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Interpretable machine learning models for evaluating strength of ternary geopolymers by Junfei Zhang, Huisheng Cheng, Ninghui Sun, Zehui Huo, Junlin Chen

“…Shapley Additive Explanations analysis was employed to interpret the machine learning models and elucidate the influence of different components on the properties of ternary geopolymers. …”
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Modelling of pome fruit pollen performance using machine learning by Sultan Filiz Güçlü

“…The best model was selected through a validation test.This study aimed to develop a machine learning model for predicting pollen germination rates in pome fruits. …”
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