Multivariate Machine Learning Model Based on YOLOv8 for Traffic Flow Prediction in Intelligent Transportation Systems by Fukui Wu, Hanzhong Tan, Linfeng Zhang, Shuangbing Wen, Tao Hu

“…To address these limitations, this study proposes a traffic flow prediction framework based on sensor networks and multivariate machine learning techniques. Real-time vehicle data are collected using cameras deployed along highways, and key traffic parameters such as flow, density, and speed are precisely extracted using the YOLOv8 object detection model. …”
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Development of a Dual-Sided Soil-Clearing Machine with Scraping, Rotating, and Vibrating Components for Winemaking Grapes by Xiang Li, Fazhan Yang, Baogang Li, Yuhuan Li, Ruijun Sun, Zehui Peng

“…The machine primarily consists of a gantry frame, rotary soil components, scraping components, and vibrating components. …”
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Enhancing predictive maintenance in automotive industry: addressing class imbalance using advanced machine learning techniques by Yashashree Mahale, Shrikrishna Kolhar, Anjali S. More

“…The on-board diagnostic dataset utilized has only 16.3% of the failure data, and to address this, 3 key approaches were explored: [i] synthetic minority oversampling technique (SMOTE), [ii] cost-sensitive learning, [iii] ensemble methods. Six machine learning models, including logistic regression, support vector machine, decision tree, and random forest, along with gradient boosting algorithms using extreme gradient boost (XGBoost) and light gradient boosting machine frameworks, were implemented. …”
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Predicting salinity levels in the Mekong delta (Viet Nam): analysis of machine learning and deep learning models by Phong Nguyen Duc, Thang Tang Duc, Giap Pham Van, Hoat Nguyen Van, Tuan Tran Minh

“…This paper assesses the efficacy of six different machine learning (ML) and deep learning models (DL) for hourly prediction of salinity in the Mekong Delta at four stations (Cau Quan, Tra Vinh, Ben Trai, and Tran De). …”
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Machine learning-driven design of wide-angle impedance matching structures for wide-angle scanning arrays by Sina Hasibi Taheri, Javad Mohammadpour, Ali Lalbakhsh, Slawomir Koziel, Stanislaw Szczepanski

“…The methodology involves training a network using three ML algorithms, including decision tree, bagging, and random forest. Optimal WAIM parameters are efficiently determined using a genetic algorithm (GA). …”
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Real-time surrogate compensation architecture for machine-tool thermal error compensation with high-performance model by Yubin Huang, Hongyou Hong, Huichen Zhou, Hua Xiang, Jianzhong Yang

“…Based on the look-ahead machine tool control command, the thermal error of next stage could be predicted according to the high-performance thermal error prediction model running on the edge, and the parameters of thermal error surrogate compensation model running on the CNC system could be fitted for high accuracy real-time compensation of the thermal error during the processing process. …”
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Random Reflectance: A New Hyperspectral Data Preprocessing Method for Improving the Accuracy of Machine Learning Algorithms by Pavel A. Dmitriev, Anastasiya A. Dmitrieva, Boris L. Kozlovsky

“…Furthermore, the efficacy of this method will be evaluated through its application in deep machine learning algorithms.…”
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Online High Frequency Impedance Identification Method of Inverter-Fed Electrical Machines for Stator Health Monitoring by Jérémy Creux, Najla Haje Obeid, Thierry Boileau, Farid Meibody-Tabar

“…They must be performed on a machine at standstill, which limits their application. …”
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Advanced hybrid machine learning models with explainable AI for predicting residual friction angle in clay soils by Mawuko Luke Yaw Ankah, Shalom Adjei-Yeboah, Yao Yevenyo Ziggah, Edmund Nana Asare

“…This study explores three advanced hybrid machine learning models: Gradient Boosting Neural Network (GrowNet), Reinforcement Learning Gradient Boosting Machine (RL-GBM), and a Stacking Ensemble to predict the residual friction angle of clay soils, addressing a critical gap in current predictive methodologies. …”
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RSA modulus length regression prediction based on the Run Test and machine learning in the ciphertext-only scenarios by Ke Yuan, Chenmeng Zhao, Longwei Yang, Hanlin Sun, Sufang Zhou, Chunfu Jia

“…Abstract RSA is a classical public key cryptographic algorithm, over 40 years of widespread use has proven that its security is reliable when the key parameters are properly configured. Attacks against RSA mainly rely on its internal mathematical constructs, such as modulus factorization, co-modulus attack, small exponent Attack, etc. …”
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Grape vine (Vitis vinifera) yield prediction using optimized weighted ensemble machine learning approach by Nobin Chandra Paul, Pratapsingh S. Khapte, Navyasree Ponnaganti, Sushil S. Changan, Sangram B. Chavan, K. Ravi Kumar, Dhananjay D. Nangare, K. Sammi Reddy

“…In this study, we propose an optimized weighted ensemble machine learning approach for predicting grape vine yield, integrating multiple morphological, physiological, and berry quality parameters. …”
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Equal performance of HTK-based and UW-based perfusion solutions in sub-normothermic liver machine perfusion by Bettina Leber, Sabrina Stimmeder, Kathrin Briendl, Jennifer Weber, Lisa Rohrhofer, Ariane Aigelsreiter, Tobias Niedrist, Robert Sucher, Philipp Stiegler

“…Abstract Machine perfusion (MP) is gaining importance in liver transplantation, the only cure for many end-stage liver diseases. …”
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Land suitability assessment for sisal production: A machine learning and Analytical Hierarchy Process integrated approach by R.N. Mgohele, B.H. J. Massawe, M.J. Shitindi, H.G. Sanga, M.M. Omar

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Flood risk mapping and performance efficiency evaluation of machine learning algorithms: Best practice in northern Iran by M. Shirmohammadi, M. Shirmohammadi, S. Pirasteh, W. Li, D. Mafi-Gholami

“…However, challenges remain in optimizing the accuracy and reliability of machine learning (ML) algorithms for flood susceptibility assessment. …”
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Real-time prediction of HFNC treatment failure in acute hypoxemic respiratory failure using machine learning by Xiaojie Li, Chunliang Jiang, Qingyan Xie, Huiquan Wang, Jiameng Xu, Guanjun Liu, Panpan Chang, Guang Zhang

“…Previous studies have highlighted inconsistencies in the predictive performance of existing indices, such as ROX and mROX, which are limited by their reliance on oxygenation parameters alone. To address this, we developed a machine learning-based predictive model using temporal data from AHRF patients, aimed at facilitating quicker development of individualized treatment plans and intervention strategies for healthcare professionals. …”
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Development of an Original Integrated System for Heat Recovery from Coolant in the Machining Process and Investigation of Its Efficiency by Osman Şahin, Durmuş Karayel

“…When a comparison is made between production methods, it will be seen that a significant amount of energy is consumed in machining processes and a large part of this energy is lost as waste heat. …”
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Finite Element and Machine Learning-Based Prediction of Buckling Strength in Additively Manufactured Lattice Stiffened Panels by Saiaf Bin Rayhan, Md Mazedur Rahman, Jakiya Sultana, Szabolcs Szávai, Gyula Varga

“…Moreover, the relationship of the parameters was found to be non-linear. Finally, the data samples collected from numerical outcomes were utilized to train four different machine learning models, namely multi-variable linear regression, polynomial regression, the random forest regressor and the K-nearest neighbor regressor. …”
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Machine learning-assisted design of immunomodulatory lipid nanoparticles for delivery of mRNA to repolarize hyperactivated microglia by Mehrnoosh Rafiei, Akbar Shojaei, Ying Chau

“…Four supervised ML classifiers were investigated to predict transfection efficiency and phenotypic changes based on LNP design parameters. The Multi-Layer Perceptron (MLP) neural network emerged as the best-performing model, achieving weighted F1-scores ≥0.8. …”
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Leveraging Machine Learning to Forecast Neighborhood Energy Use in Early Design Stages: A Preliminary Application by Andrea Giuseppe di Stefano, Matteo Ruta, Gabriele Masera, Simi Hoque

“…This study identifies three key phases in a design process framework where machine learning can be applied to optimize energy consumption in early design stages. …”
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Well Performance from Numerical Methods to Machine Learning Approach: Applications in Multiple Fractured Shale Reservoirs by Kailei Liu, Boyue Xu, Changjea Kim, Jing Fu

“…This paper presents a thorough analysis of the feasibility of machine learning in multiple fractured shale reservoirs. …”
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