Machine learning and spatio-temporal analysis of meteorological factors on waterborne diseases in Bangladesh. by Arman Hossain Chowdhury, Md Siddikur Rahman

“…Exploratory spatial analysis, spatial regression and tree-based machine learning models were utilized to analyze the data.…”
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A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs by Jiangchuan Zhang, Yude Bu, Mengmeng Zhang, Duo Xie, Zhenping Yi

“…In recent years, machine learning has played a significant role in astronomical data mining, due to its speed, real time, and precision. …”
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Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques by Anselim M. Mwaura, Yong-Kuo Liu

“…The inherent limitations of the current fault diagnosis methods make machine learning techniques and their hybrid methodologies possible solutions to remedy this challenge. …”
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Surveying Nearshore Bathymetry Using Multispectral and Hyperspectral Satellite Imagery and Machine Learning by David Hartmann, Mathieu Gravey, Timothy David Price, Wiebe Nijland, Steven Michael de Jong

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Machine Learning Analysis of Arterial Oscillograms for Depression Level Diagnosis in Cardiovascular Health by Vladislav Kaverinsky, Dmytro Vakulenko, Liudmyla Vakulenko, Kyrylo Malakhov

Subjects: “…machine learning…”
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Gaussian Process Regression and Machine Learning Methods for Carbon-Based Material Adsorption by Manar Ahmed Hamza, Maha M. Althobaiti, Fahd N. Al-Wesabi, Rana Alabdan, Hany Mahgoub, Anwer Mustafa Hilal, Abdelwahed Motwakel, Mesfer Al Duhayyim

“…Antibiotic adsorption on carbon-based materials (CBMs) such as charcoal and activated carbon has been identified as mainly effective for treating the wastewater strategies. Machine learning (ML) approaches were used to create generalized computation methods for tetracycline (TC) and sulfamethoxazole (SMX) adsorption in CBMs in this investigation. …”
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Validity of a machine learning estimation of blood volumes during altitude training by Basile Moreillon, Bastien Krumm, Lena Mettraux, Julian Wackernell, James Spragg, Martin Faulhaber, Raphael Faiss

“…We recently proposed a machine learning model to estimate Hbmass and PV from a single blood sample (Moreillon et al., 2023). …”
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Machine Learning  Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka by Piyal Ekanayake, Windhya Rankothge, Rukmal Weliwatta, Jeevani W. Jayasinghe

“…Moreover, RF was used to develop a paddy yield prediction model and four more techniques, namely, Power Regression (PR), Multiple Linear Regression (MLR) with stepwise selection, forward (step-up) selection, and backward (step-down) elimination, were used to benchmark the performance of the machine learning technique. Their performances were compared in terms of the Root Mean Squared Error (RMSE), Correlation Coefficient (R), Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE). …”
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Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning by Lixia Liu, Xueli Zhan

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Retracted: Estimation of International Gold Price by Fusing Deep/Shallow Machine Learning by Journal of Advanced Transportation

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Machine Learning Model for Gas–Liquid Interface Reconstruction in CFD Numerical Simulations by Tamon Nakano, Michele Alessandro Bucci, Jean-Marc Gratien, Thibault Faney

“…In this work, we propose a machine learning-enhanced VoF method based on graph neural networks (GNNs) to accelerate interface reconstruction on general unstructured meshes. …”
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Retracted: Prediction of Coal Mining Subsidence Based on Machine Learning Probability Theory by Journal of Electrical and Computer Engineering

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Milk Composition Is Predictive of Low Milk Supply Using Machine Learning Approaches by Xuehua Jin, Ching Tat Lai, Sharon L. Perrella, Xiaojie Zhou, Ghulam Mubashar Hassan, Jacki L. McEachran, Zoya Gridneva, Nicolas L. Taylor, Mary E. Wlodek, Donna T. Geddes

“…<b>Conclusions:</b> These findings demonstrate the potential of machine learning models to predict low milk supply with high accuracy. …”
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Tropospheric Ducting: A Comprehensive Review and Machine Learning-Based Classification Advancements by Mohammed Banafaa, Ali Hussein Muqaibel

“…Our findings demonstrate that machine learning models, particularly support vector machines, can effectively classify ducting conditions, offering superior predictive performance compared to other models. …”
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A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy by Chetan Shetty, S. Seema, B. J. Sowmya, Rajesh Nandalike, S. Supreeth, Dayananda P., Rohith S., Vishwanath Y., Rajeev Ranjan, Venugopal Goud

“…In this work, density-based spatial clustering of applications with noise (DBSCAN) is used which is among the widely used clustering algorithms in machine learning. It is not only capable of finding clusters of various sizes and shapes but can also detect outliers. …”
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Identification of therapeutic targets for Alzheimer’s Disease Treatment using bioinformatics and machine learning by ZhanQiang Xie, YongLi Situ, Li Deng, Meng Liang, Hang Ding, Zhen Guo, QinYing Xu, Zhu Liang, Zheng Shao

“…This study aimed to identify potential therapeutic targets for the treatment of AD using comprehensive bioinformatics methods and machine learning algorithms. By integrating differential gene expression analysis, weighted gene co-expression network analysis, Mfuzz clustering, single-cell RNA sequencing, and machine learning algorithms including LASSO regression, SVM-RFE, and random forest, five hub genes related to AD, including PLCB1, NDUFAB1, KRAS, ATP2A2, and CALM3 were identified. …”
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Machine learning and facial recognition for down syndrome detection: A comprehensive review by Khosro Rezaee

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Digital Recruitment Using Intelligent Dialogue Systems Based on Machine Learning Principles by I. N. Kalinouskaya

“…The article suggests the technology of implementation of digital recruiting by Belarusian companies; the method of evaluation of candidates' CVs is given; the method of conducting preliminary interviews with the use of intelligent dialog systems based on the principles of machine learning is given; the example of using the chat-bot in the process of selection and evaluation of candidates is considered; the advantages of digital recruitment over the classical methods of personnel recruitment are specified.…”
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Development and experimental validation of a machine learning model for the prediction of new antimalarials by Mukul Kore, Dimple Acharya, Lakshya Sharma, Shruthi Sridhar Vembar, Sandeep Sundriyal

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Retracted: Machine Learning and Artificial Intelligence in the Food Industry: A Sustainable Approach by Journal of Food Quality

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