Concept for Predictive Quality in Carbon Fibre Manufacturing by Sebastian Gellrich, Thomas Groetsch, Maxime Maghe, Claudia Creighton, Russell Varley, Anna-Sophia Wilde, Christoph Herrmann

Subjects: “…data analytics…”
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Machine learning in environmental sustainability factor analysis in the agricultural sector by Kukartsev Vladislav, Kozlova Anastasia, Kukarceva Svetlana

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Censoring Sensitivity Analysis for Benchmarking Survival Machine Learning Methods by János Báskay, Tamás Mezei, Péter Banczerowski, Anna Horváth, Tamás Joó, Péter Pollner

“…(1) Background: Survival analysis models in clinical research must effectively handle censored data, where complete survival times are unknown for some subjects. …”
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MACHINE LEARNING AND ECONOMETRICS: BRIDGING THE GAP FOR ENHANCED ECONOMIC ANALYSIS by Jamiu Adeniyi Yusuf, Abdulkadri IDRIS, Azeez Olawale AKINLOLU

“…The findings suggest that interdisciplinary collaboration between economists and data scientists will be crucial for advancing economic analysis and translating machine learning innovations into practical economic insights. …”
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Machine Learning Based Method for Insurance Fraud Detection on Class Imbalance Datasets With Missing Values by Ahmed A. Khalil, Zaiming Liu, Ahmed Fathalla, Ahmed Ali, Ahmad Salah

“…Prior research has employed machine learning methods to address this class imbalance dataset problem, but there is limited effort handling the class imbalance dataset present in insurance fraud datasets. …”
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Comparative analysis of machine learning algorithms for money laundering detection by Sunday Adeola Ajagbe, Simphiwe Majola, Pragasen Mudali

“…This research examined contemporary machine learning (ML) algorithms, including XGBoost, K-Nearest Neighbors, Random Forest, Isolation Forest, and Support Vector Machines, to analyze transaction data for anomalies indicative of fraudulent behavior. …”
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Language task-based fMRI analysis using machine learning and deep learning by Elaine Kuan, Elaine Kuan, Elaine Kuan, Viktor Vegh, Viktor Vegh, Viktor Vegh, John Phamnguyen, John Phamnguyen, John Phamnguyen, Kieran O’Brien, Amanda Hammond, David Reutens, David Reutens, David Reutens, David Reutens

“…Data comprising of seven task-based language fMRI paradigms were collected from 26 individuals, and ML and DL models were trained to classify voxel-wise fMRI time series.ResultsThe general machine learning and the interval-based methods were the most promising in identifying language areas using fMRI time series classification. …”
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Applications of machine learning in decision analysis for dose management for dofetilide. by Andrew E Levy, Minakshi Biswas, Rachel Weber, Khaldoun Tarakji, Mina Chung, Peter A Noseworthy, Christopher Newton-Cheh, Michael A Rosenberg

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Comparison of Machine Learning Methods for Menstrual Cycle Analysis and Prediction by Mutiara Khairunisa, Desak Made Sidantya Amanda Putri, I Gusti Ngurah Lanang Wijayakusuma

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Sentiment Analysis for Tourism Insights: A Machine Learning Approach by Kenza Charfaoui, Stéphane Mussard

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Comparative Analysis of Machine Learning Algorithms on Family Wellness Classification by Retno Budiarti, Febri Hemarani, Mohammad Reza, Rindi Melati Mulyasari

“…This study aims to compare the performance of three machine learning algorithms, namely KNN (K-Nearest Neighbors), random forest, and naive Bayes, in classifying the status of families per province in Indonesia as prosperous or not prosperous. …”
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Advanced multiscale machine learning for nerve conduction velocity analysis by Hossein Sadeghi

“…Abstract This paper presents an advanced machine learning (ML) framework for precise nerve conduction velocity (NCV) analysis, integrating multiscale signal processing with physiologically-constrained deep learning. …”
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Helicopter reconnaissance indicators sensitivity analysis based on machine learning by JIANG Xinyi, LOU Benchao, ZENG Weiping

“…Secondly, sensitivity analysis of helicopter reconnaissance mission effectiveness is achieved through task planning, constructing indicator systems, collecting relevant data, evaluating effectiveness, and using machine learning algorithms for modeling and training. …”
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Developing tunable machine learning workflow for traffic analysis in SDN by Samaan Sama Salam, Jeiad Hassan Awheed

“…It presents a complete machine learning (ML) workflow that begins with data ingestion and end with a trained model that is capable of analyzing packets in a production network. …”
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Characterization of High-Speed Steels—Experimental Data and Their Evaluation Supported by Machine Learning Algorithms by Manfred Wiessner, Ernst Gamsjäger

“…The clusters obtained by this procedure agree well with the labeled data. By supervised learning via a support vector machine, hyperplanes are constructed that allow separating the clusters from each other based on the X-ray measurements. …”
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Optimization of machine learning algorithms for proteomic analysis using topsis by Javanbakht T., Chakravorty S.

“…The present study focuses on a new application of the TOPSIS method for the optimization of machine learning algorithms, supervised neural networks (SNN), the quick classifier (QC), and genetic algorithm (GA) for proteomic analysis. …”
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The analysis of fraud detection in financial market under machine learning by Jing Jin, Yongqing Zhang

“…Therefore, this paper proposes a financial fraud detection model based on Stacking ensemble learning algorithm, which integrates many basic learners such as logical regression (LR), decision tree (DT), random forest (RF), Gradient Boosting Tree (GBT), support vector machine (SVM) and neural network (NN), and introduces feature importance weighting and dynamic weight adjustment mechanism to improve the model performance. …”
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Suicide risk prediction for Korean adolescents based on machine learning by Haitao Wang, Han Yuan, Yunong Zhang, Qixuan Wang, Zeng Gao, Mujuan Zhao

“…Abstract Traditional clinical risk assessment tools proved inadequate for reliably identifying individuals at high risk for suicidal behavior. As a result, machine learning (ML) techniques have become progressively incorporated into psychiatric care. …”
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Comparison of Machine Learning Methods for Predicting Electrical Energy Consumption by Retno Wahyusari, Sunardi Sunardi, Abdul Fadlil

“…The study employs three Machine Learning (ML) models: k-Nearest Neighbors (KNN), Random Forest (RF), and CatBoost. …”
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Cutting-Edge Machine Learning in Biomedical Image Analysis: Editorial for <i>Bioengineering</i> Special Issue: “Recent Advance of Machine Learning in Biomedical Image Analysis” by Sheng Lian, Zhiming Luo

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