Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review by Joana Seringa, João Abreu, Teresa Magalhaes

“…All included studies employed supervised ML algorithms, with ensemble-based methods generally yielding the highest predictive performance. …”
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AI4EO hyperview challenge: combination of machine learning methods on hyperspectral images to predict the soil parameters by M. Sanità, E. S. Malinverni, R. Pierdicca, A. Mancini, E. Glowienka, L. Nepi

“…Artificial intelligence (AI) through Machine Learning (ML) and Deep Learning (DL) techniques can be a great support for farmers in optimising the use of natural resources and ensuring better land management. …”
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Optimization of Cocoa Pods Maturity Classification Using Stacking and Voting with Ensemble Learning Methods in RGB and LAB Spaces by Kacoutchy Jean Ayikpa, Abou Bakary Ballo, Diarra Mamadou, Pierre Gouton

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Predicting Wastewater Characteristics Using Artificial Neural Network and Machine Learning Methods for Enhanced Operation of Oxidation Ditch by Igor Gulshin, Nikolay Makisha

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Improving the Accuracy of a Wearable Uroflowmeter for Incontinence Monitoring Under Dynamic Conditions: Leveraging Machine Learning Methods by Faezeh Shanehsazzadeh, John O. L. DeLancey, James A. Ashton-Miller

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Evaluation of machine learning methods for forecasting turbidity in river networks using Sentinel-2 remote sensing data by Victor Oliveira Santos, Paulo Alexandre Costa Rocha, Jesse Van Griensven Thé, Bahram Gharabaghi

“…Turbidity is an important indicator of river water quality and of great interest to improve the data acquisition methods and efficiency of decision support systems for sustainable ecosystem management. …”
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Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods by Xiuping Xuan, Mingjin Sun, Donghui Hu, Chunli Lu

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Comparison of Machine Learning Methods (Linear Regression, Random Forest, and XGBoost) for Predicting Poverty in Central Java in 2024 by Zahwa Bunga Putri Pratama, Yani Parti Astuti

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Soil Organic Carbon Monitoring and Modelling via Machine Learning Methods Using Soil and Remote Sensing Data by Dimitrios Triantakonstantis, Andreas Karakostas

“…The accurate monitoring and modelling of SOC are essential for assessing soil fertility, facilitating sustainable land management, and mitigating climate change. (2) Methods: This research paper explores the integration of machine learning (ML) approaches with soil, terrain and remotely sensed data to enhance SOC estimation. …”
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Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables by Sajjad Farashi, Hossein Emad Momtaz

“…Method In this study, machine learning approaches were used for finding the important variables for a reliable prediction of UTI. …”
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A Systematic Approach to Enhancing ISO 26262 With Machine Learning-Specific Life Cycle Phases and Testing Methods by Padma Iyenghar, Emil Gracic, Gregor Pawelke

“…This paper presents a systematic approach to enhancing ISO 26262, a widely adopted standard for automotive functional safety, by integrating Machine Learning (ML)-specific life cycle phases and testing methods for Automotive Safety Integrity Level (ASIL) A/B. …”
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Teaching methods, facilities, and institutions in student ultrasound education (SUSE): e-learning, simulation, and ultrasound skills labs by Daum Nils, Schwanemann Jannis, Blaivas Michael, Prats Michael Ignacio, Hari Roman, Hoffmann Beatrice, Jenssen Christian, Krutz Alexander, Lucius Claudia, Neubauer Ricarda, Recker Florian, Sirli Roxana, Westerway Susan Cambell, Zervides Constantinos, Nürnberg Dieter, Barth Gregor, Nourkami-Tutdibi Nasenien, Dietrich Christoph Frank

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Unveiling the Spatial Heterogeneity of Urban Vitality Using Machine Learning Methods: A Case Study of Tianjin, China by Fengshuo Sun, Enxu Wang

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Identifying potential three key targets gene for septic shock in children using bioinformatics and machine learning methods by Wei Guo, Hao Chen, Feng Wang, Yingjiao Chi, Wei Zhang, Shan Wang, Kezhu Chen, Hong Chen

“…This study aims to use bioinformatics and machine learning algorithms to identify key genes and pathways associated with fatal sepsis in children, and provide theoretical basis for rational drug use in follow-up TCM treatment.MethodsGene expression profiles were obtained from the GEO database (GSE4607) for 15 blank patients and 14 children with sepsis death. …”
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Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories by Huan Lin, Weiye Yu, Zhan Lian

“…Accurately and rapidly predicting marine drifter trajectories under conditions of information scarcity is critical for addressing maritime emergencies and conducting marine surveys with resource-limited unmanned vessels. Machine learning-based tracking methods, such as Long Short-Term Memory networks (LSTM), offer a promising approach for trajectory prediction in such scenarios. …”
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Flexural strengthening of corroded steel beams with CFRP by using the end anchorage: Experimental, numerical, and machine learning methods by Amin Shabani Ammari, Younes Nouri, Habib Ghasemi Jouneghani, Seyed Amin Hosseini, Arash Rayegani, Mehrdad Ebrahimi, Pooria Heydari

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Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data by Chien Wei Oei, Eddie Yin Kwee Ng, Matthew Hok Shan Ng, Yam Meng Chan, Vinithasree Subbhuraam, Lai Gwen Chan, U. Rajendra Acharya

“…The objective of this study is to use deep learning (DL) methods to predict the risk of a stroke survivor experiencing post-stroke depression and/or post-stroke anxiety, which is collectively known as post-stroke adverse mental outcomes (PSAMO). …”
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Assessing the interactions between learning enjoyment, motivation, burnout, and grit in EFL students: a mixed-methods approach by Yao Song

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Two-stage prediction of drift ratio limits of corroded RC columns based on interpretable machine learning methods by Yan Zhou, Yizhi Qiu, Liuzhuo Chen

“…This model is then combined with the SHAP method to facilitate feature importance ranking and model interpretability. …”
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Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review by Qiuyu Yang, Xia Fan, Xiao Cao, Weijie Hao, Jiale Lu, Jia Wei, Jinhui Tian, Min Yin, Long Ge

“…Abstract Introduction There was limited evidence on the quality of reporting and methodological quality of prediction models using machine learning methods in preterm birth. This systematic review aimed to assess the reporting quality and risk of bias of a machine learning‐based prediction model in preterm birth. …”
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