Machine-Learning-Based Classification of Electronic Devices Using an IoT Smart Meter by Paulo Eugênio da Costa Filho, Leonardo Augusto de Aquino Marques, Israel da S. Felix de Lima, Ewerton Leandro de Sousa, Márcio Eduardo Kreutz, Augusto V. Neto, Eduardo Nogueira Cunha, Dario Vieira

“…The experimental results emphasize the importance of data preprocessing—especially normalization—in optimizing model performance, revealing distinct behavior between MLP and KNN models depending on the platform. …”
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Machine learning study on magnetic structure of rare earth based magnetic materials by Dan Liu, Jiahe Song, Zhixin Liu, Jine Zhang, Weiqiang Chen, Yinong Yin, Jianfeng Xi, Xinqi Zheng, Jiazheng Hao, Tongyun Zhao, Fengxia Hu, Jirong Sun, Baogen Shen

“…The prediction accuracy of all models is above 0.73. Compared with non-decision tree models, optimized decision tree algorithms such as Gradient Boosting have greater advantages in binary classification. …”
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Automatic diagnosis of extraocular muscle palsy based on machine learning and diplopia images by Xiao-Lu Jin, Xue-Mei Li, Tie-Juan Liu, Ling-Yun Zhou

“…AIM: To develop different machine learning models to train and test diplopia images and data generated by the computerized diplopia test. …”
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Implications of machine learning techniques for prediction of motor health disorders in Saudi Arabia by Ehab M. Almetwally, I. Elbatal, Mohammed Elgarhy, Amr R. Kamel

“…This system aims to detect and forecast motor impairment disorders early on using AI model approaches. This system is an efficient tool that properly detects and diagnoses a variety of motor impairment problems using ML algorithms. …”
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Dynamic performance improvement of oscillating water column wave energy conversion system using optimal walrus optimization algorithm-based control strategy by Habiba A. ElDemery, Hany M. Hasanien, Mohammed Alharbi, Chuanyu Sun, Dina A. Zaky

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Multi-port network based modeling and selection of capacitor for desired voltage regulation of a standalone six-phase short-shunt induction generator for application in remote area... by Saikat Ghosh, S.N. Mahato

“…The theory of multi-port network analysis has been applied for modelling of the SPIG, thus, the complex mathematical derivation to obtain the model equations is avoided. …”
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Machine Learning Applications for Predicting Longitudinal Cracking in Continuously Reinforced Concrete Pavement by Ali Alnaqbi, Ghazi G. Al-Khateeb, Waleed Zeiada

“…The study's findings underscore the superiority of certain machine learning models over traditional regression methods in predicting longitudinal cracking, offering practical implications for optimizing maintenance strategies and enhancing CRCP infrastructure longevity. …”
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Integrated Iot Approaches for Crop Recommendation and Yield-Prediction Using Machine-Learning by Mohamed Bouni, Badr Hssina, Khadija Douzi, Samira Douzi

“…The IoT data collection enabled real-time monitoring and accurate data input, significantly improving the models’ performance. These findings demonstrate the potential of combining IoT and machine learning to optimize resource use and improve crop management in smart farming. …”
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Machine‐learning based spatiotemporal prediction of soil moisture in a grassland hillslope by Timo Houben, Pia Ebeling, Swamini Khurana, Julia Sabine Schmid, Johannes Boog

“…For training and testing the ML models, we used SM point measurements obtained by a sensor network. …”
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Leveraging machine learning to predict residential location choice: A comparative analysis by Vahid Noferesti, Hamid Mirzahossein

“…By applying this method and different machine learning models, the study provides a detailed comparison of their performance in predicting residential choices. …”
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Energy Consumption of Machine Learning Enhanced Open RAN: A Comprehensive Review by Xuanyu Liang, Qiao Wang, Ahmed Al-Tahmeesschi, Swarna B. Chetty, David Grace, Hamed Ahmadi

“…Central to this innovation is the integration of Machine Learning (ML) and Artificial Intelligence (AI) within the RAN Intelligent Controller (RIC), aimed at optimizing network operations and enhancing control mechanisms. …”
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Energy performance estimation for large building portfolios with machine learning-based techniques by Frédéric Montet, Alessandro Pongelli, Jonathan Rial, Stefanie Schwab, Jean Hennebert, Thomas Jusselme

“…This research aims at the estimation of building energy performance to pave the way towards finding near-optimal refurbishment strategies. Thanks to the identification of easily-accessible building characteristics, the method applies machine learning models to scan a building portfolio based on a low level of details. …”
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Evaluating regional sustainable energy potential through hierarchical clustering and machine learning by Selen Avcı Azkeskin, Zerrin Aladağ

“…To validate the clustering results, supervised classification methods—including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—are utilized, alongside ensemble models based on RF and XGBoost. …”
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Research on the mechanism of human-machine security collaboration of miners considering automation trust by Juan Yang, Xue Yang, Shan Chai, Likun Ni, Xiao Wang, Langxuan Pan

“…With the implementation of intelligent construction in coal mines, humans–machine collaboration is critical to accident prevention. …”
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A Review on Machine Learning-Aided Hydrothermal Liquefaction Based on Bibliometric Analysis by Lili Qian, Xu Zhang, Xianguang Ma, Peng Xue, Xingying Tang, Xiang Li, Shuang Wang

“…However, the HTL process is influenced by various complex factors such as operating conditions, feedstock properties, and reaction pathways. Machine learning (ML) methods can utilize existing HTL data to develop accurate models for predicting product yields and properties, which can be used to optimize HTL operation conditions. …”
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QM40, Realistic Quantum Mechanical Dataset for Machine Learning in Molecular Science by Ayesh Madushanka, Renaldo T. Moura, Elfi Kraka

“…Abstract The growing popularity of machine learning (ML) and deep learning (DL) in scientific fields is hindered by the scarcity of high-quality datasets. …”
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Implementing artificial neural networks and support vector machines to predict lost circulation by Ahmed K. Abbas, Najim A. Al-haideri, Ali A. Bashikh

“…Finally, the best-proposed models were examined using a few examples of real lost circulation cases from the field. …”
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Further observations on the security of Speck32-like ciphers using machine learning by Zezhou Hou, Jiongjiong Ren, Shaozhen Chen

“…To assess different parameters security, we develop neural-differential distinguishers with considering of two distinct input difference models: (1) the low-Hamming-weight input differences and (2) the input differences from optimal differential characteristics. …”
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Recent advances in machine learning for defects detection and prediction in laser cladding process by X.C. Ji, R.S. Chen, C.X. Lu, J. Zhou, M.Q. Zhang, T. Zhang, H.L. Yu, Y.L. Yin, P.J. Shi, W. Zhang

“…Furthermore, it encapsulates prevalent machine learning models and algorithms employed for defect detection. …”
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Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators by Hoa Thi Nguyen, Roland Olsson, Oystein Haugen

“…We compare the performance of our evolved neurons with support vector machine and four well-established neural network models commonly used for time series data: simple recurrent neural networks, long-short-term-memory, independently recurrent neural networks, and transformers. …”
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