Optimizing Metro Passenger Flow Prediction: Integrating Machine Learning and Time-Series Analysis with Multimodal Data Fusion by Li Wan, Wenzhi Cheng, Jie Yang

“…In this paper, we propose a method that combines advanced machine learning with rigorous time series analysis to improve prediction accuracy by integrating different datasets, providing a prescriptive example for passenger flow prediction in urban rail transit systems. …”
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Low-carbohydrate diet score and chronic obstructive pulmonary disease: a machine learning analysis of NHANES data by Xin Zhang, Jipeng Mo, Kaiyu Yang, Tiewu Tan, Cuiping Zhao, Hui Qin

“…Additionally, we employed eight machine learning methods—Boost Tree, Decision Tree, Logistic Regression, MLP, Naive Bayes, KNN, Random Forest, and SVM RBF—to build predictive models and evaluate their performance. …”
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Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches by Ersin Aytaç, Noman Khalid Khanzada, Yazan Ibrahim, Mohamed Khayet, Nidal Hilal

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Stock Data Analysis by Machine Learning Techniques to Improve Predictive Model-ing and Decision-Making in Financial Markets by Rasool F. Jader, Mudhafar Haji M. Abd

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Interpretable Machine Learning Framework for Corporate Financialization Prediction: A SHAP-Based Analysis of High-Dimensional Data by Yanhe Wang, Wei Wei, Zhuodong Liu, Jiahe Liu, Yinzhen Lv, Xiangyu Li

Subjects: “…machine learning…”
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Evaluation of Machine Learning Models for Sentiment Analysis in the South Sumatra Governor Election Using Data Balancing Techniques by Febriyanti Panjaitan, Win Ce, Hery Oktafiandi, Ghanim Kanugrahan, Yudi Ramdhani, Vito Hafizh Cahaya Putra

Subjects: “…sentiment analysis, machine learning, governor election, twitter, youtube, countvectorizer, balancing data.…”
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Investigating imperceptibility of adversarial attacks on tabular data: An empirical analysis by Zhipeng He, Chun Ouyang, Laith Alzubaidi, Alistair Barros, Catarina Moreira

“…The findings gained from this empirical analysis provide valuable direction for enhancing the design of adversarial attack algorithms, thereby advancing adversarial machine learning on tabular data.…”
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Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

“…This study proposes an effective data-driven approach that utilizes machine learning algorithms to forecast reservoir pore pressure. …”
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Application of machine learning for seam profile identification in robotic welding by Fatemeh Habibkhah, Mehrdad Moallem

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Finding White Dwarfs’ Hidden Companions Using an Unsupervised Machine Learning Technique by Xabier Pérez-Couto, Minia Manteiga, Eva Villaver

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Data leakage detection in machine learning code: transfer learning, active learning, or low-shot prompting? by Nouf Alturayeif, Jameleddine Hassine

“…With the increasing reliance on machine learning (ML) across diverse disciplines, ML code has been subject to a number of issues that impact its quality, such as lack of documentation, algorithmic biases, overfitting, lack of reproducibility, inadequate data preprocessing, and potential for data leakage, all of which can significantly affect the performance and reliability of ML models. …”
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Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods by Abhinav Kumar, Paul Rodrigues, A. K. Kareem, Tingneyuc Sekac, Sherzod Abdullaev, Jasgurpreet Singh Chohan, R. Manjunatha, Kumar Rethik, Shivakrishna Dasi, Mahmood Kiani

“…In this communication, we aim to develop intelligent data-driven models of decision tree, random forest, ensemble learning, adaptive boosting, support vector machine and multilayer perceptron artificial neural network to predict rock saturation exponent parameter in terms of rock absolute permeability, porosity, resistivity index, true resistivity, and water saturation based on acquired 1041 field data. …”
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Machine learning for predicting resistance spot weld quality in automotive manufacturing by Nuttapong Chuenmee, Nattachai Phothi, Kontorn Chamniprasart, Sorada Khaengkarn, Jiraphon Srisertpol

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An Interpretable Machine Learning Framework for Analyzing the Interaction Between Cardiorespiratory Diseases and Meteo-Pollutant Sensor Data by Vito Telesca, Maríca Rondinone

“…The aim of this study is the development and verification of an interpretable machine learning framework applied to environmental and health data to assess the relationship between environmental factors and daily emergency room admissions for cardiorespiratory diseases. …”
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Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy by Mahadee Al Mobin

Subjects: “…Machine learning…”
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Development of a neural network model for spatial data analysis by E. O. Yamashkina, S. A. Yamashkin, O. V. Platonova, S. M. Kovalenko

“…The ontological model of the repository including the developed model is decomposed into domains of deep machine learning models, project tasks and data, thus providing a comprehensive definition of the formalizing area of knowledge. …”
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Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN by Vladislav Kaverinskiy, Illya Chaikovsky, Anton Mnevets, Tatiana Ryzhenko, Mykhailo Bocharov, Kyrylo Malakhov

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Chopped and minibars reinforced high-performance concrete: machine learning prediction of mechanical properties by Nikolai Ivanovich Vatin, Mohammad Hematibahar, Tesfaldet Hadgembes Gebre

“…In this study, the compressive strength and flexural strength are predicted via different types of machine learning models. Experiments carried out in the laboratory under standard controlled settings at 7, 14, and 28-day curing periods yielded sample data for analysis and model development. …”
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Federated Analytics With Data Augmentation in Domain Generalization Toward Future Networks by Xunzheng Zhang, Juan Marcelo Parra-Ullauri, Shadi Moazzeni, Xenofon Vasilakos, Reza Nejabati, Dimitra Simeonidou

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One techno-economic analysis to rule them all: Instant prediction of hydrothermal liquefaction economic performance with a machine learned analytic equation by Muntasir Shahabuddin, Nikolaos Kazantzis, Andrew R Teixeira, Michael T. Timko

“…The structure of the proposed framework is informed and based on empirical observations of cost projections made by a detailed TEA over a wide range of feedstock costs, biocrude yields, and process scales. A machine learning guided process was used to identify, train, and test a series of models using auto-generated data for training and independently reported data for testing. …”
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