Leveraging Machine Learning for Advancing Circular Supply Chains: A Systematic Literature Review by Zeinab Farshadfar, Tomasz Mucha, Kari Tanskanen

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Robust Fault Classification in Permanent Magnet Synchronous Machines Under Dynamic and Noisy Conditions by Mikal Laursen, Van-Van Huynh, Duy-Hung Ha, Mahmoud S. Mahmoud, van Khang Huynh

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Predictive PID Control for Automated Guided Vehicles Using Genetic Algorithm and Machine Learning by Kinza Nazir, Yong-Woon Kim, Yung-Cheol Byun

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Topology Synthesis for In-Situ Machining Mechanism With Dual Platforms and Decoupled Motions of Position and Rotation by Yang Qi, Jinhan Yang, Xinran Tong

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MyWear revolutionizes real-time health monitoring with comparative analysis of machine learning by Krishna Prakash, Musam Naga Harshitha, Golla Naga Lakshmi, Pallem Moses, Madala Sumanth Chowdary, Shonak Bansal, Mohammad Rashed Iqbal Faruque, K. S. Al-Mugren

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CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis by Ruixue Wang, Ning Zhao

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Filament Type Recognition for Additive Manufacturing Using a Spectroscopy Sensor and Machine Learning by Gorkem Anil Al, Uriel Martinez-Hernandez

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An Effective Approach for Human Activity Classification Using Feature Fusion and Machine Learning Methods by Muhammad Junaid Ibrahim, Jaweria Kainat, Hussain AlSalman, Syed Sajid Ullah, Suheer Al-Hadhrami, Saddam Hussain

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Machine learning for improved path loss prediction in urban vehicle-to-infrastructure communication systems by Mongi Ben Ameur, Jalel Chebil, Mohamed Hadi Habaebi, Jamel Bel Hadj Tahar

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Machine Learning‐Assisted Pd‐Au/MXene Sensor Array for Smart Gas Identification by Yiheng Chen, Jiawang Hu, Nanlin Hu, Shikai Wu, Yuan Lu

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Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar by Raouf Hassan, Mohammad Reza Kazemi

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Effect of frame foundation geometry on the dynamic response of high-speed turbo machine foundations by Ameer A. Ahmed, Mohammed Y. Fattah, Makki K. Mohsen

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An electrocardiogram signal classification using a hybrid machine learning and deep learning approach by Faramarz Zabihi, Fatemeh Safara, Behrouz Ahadzadeh

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Machine-Learning-Driven Analysis of Wear Loss and Frictional Behavior in Magnesium Hybrid Composites by Barun Haldar, Hillol Joardar, Arpan Kumar Mondal, Nashmi H. Alrasheedi, Rashid Khan, Murugesan P. Papathi

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Machine learning-based detection of DDoS attacks on IoT devices in multi-energy systems by Hesham A. Sakr, Mostafa M. Fouda, Ahmed F. Ashour, Ahmed Abdelhafeez, Magda I. El-Afifi, Mohamed Refaat Abdellah

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Mechanical properties and machine learning analysis of concrete incorporating waste glass as coarse aggregate by Bhukya Govardhan Naik, G. Nakkeeran, Dipankar Roy, G. Uday Kiran, Kalyani Gurram, Gade Venkata Ramanjaneyulu, George Uwadiegwu Alaneme, Mutiu Shola Bakare

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Development of a Small CNC Machining Center for Physical Implementation and a Digital Twin by Claudiu-Damian Petru, Fineas Morariu, Radu-Eugen Breaz, Mihai Crenganiș, Sever-Gabriel Racz, Claudia-Emilia Gîrjob, Alexandru Bârsan, Cristina-Maria Biriș

“…To validate the contouring performance of the proposed CNC system, a circular groove with an outer diameter of 31 mm and an inner diameter of 29 mm was machined using a 1 mm cylindrical end mill. …”
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Comparative Analysis of Automated Machine Learning for Hyperparameter Optimization and Explainable Artificial Intelligence Models by Muhammad Salman Khan, Tianbo Peng, Hanzlah Akhlaq, Muhammad Adeel Khan

“…The study focuses on predicting the ultimate moment capacity of Ultra-High-Performance Concrete (UHPC) beams and U-shaped girders. …”
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Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approach by Zhuocheng Xie, Achraf Atila, Julien Guénolé, Sandra Korte-Kerzel, Talal Al-Samman, Ulrich Kerzel

“…Comparing the grain boundary solute concentrations calculated via the Langmuir-McLean isotherm with experimental data, we identified a pronounced segregation tendency for Nd, highlighting its potential for GB engineering in Mg alloys. This work demonstrates the powerful synergy of atomistic simulations and machine learning, paving the way for designing advanced lightweight Mg alloys with tailored properties.…”
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Predicting Cognitive Decline in Motoric Cognitive Risk Syndrome Using Machine Learning Approaches by Jin-Siang Shaw, Ming-Xuan Xu, Fang-Yu Cheng, Pei-Hao Chen

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