Retracted: Prediction of Coal Mining Subsidence Based on Machine Learning Probability Theory by Journal of Electrical and Computer Engineering

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Machine-learning-based cost prediction models for inpatients with mental disorders in China by Yuxuan Ma, Xi Tu, Xiaodong Luo, Linlin Hu, Chen Wang

Subjects: “…Machine learning…”
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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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Machine learning-based modelling and analysis of carbonation depth of recycled aggregate concrete by Xuyong Chen, Xuan Liu, Shukai Cheng, Xiaoya Bian, Xixuan Bai, Xin Zheng, Xiong Xu, Zhifeng Xu

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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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A recurrence model for non-puerperal mastitis patients based on machine learning. by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

“…The aim of this research is to create and validate a recurrence model using machine learning for patients with non-puerperal mastitis.…”
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Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling by Daphne N. Katsarou, Eleni I. Georga, Maria A. Christou, Panagiota A. Christou, Stelios Tigas, Costas Papaloukas, Dimitrios I. Fotiadis

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Machine learning-driven benchmarking of China's wastewater treatment plant electricity consumption by Minjian Li, Chongqiao Tang, Junhan Gu, Nianchu Li, Ahemaide Zhou, Kunlin Wu, Zhibo Zhang, Hui Huang, Hongqiang Ren

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Machine Learning Applications in Manufacturing&#x2014;Challenges, Trends, and Future Directions by Alexandre Manta-Costa, Sara Oleiro Araujo, Ricardo Silva Peres, Jose Barata

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Machine Learning Applications in Energy Harvesting Internet of Things Networks: A Review by Olumide Alamu, Thomas O. Olwal, Emmanuel M. Migabo

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Letter and Person Recognition in Freeform Air-Writing Using Machine Learning Algorithms by Huseyin Kunt, Zeki Yetgin, Furkan Gozukara, Turgay Celik

“…Fourier and wavelet transforms are used to extract features and the performances of various machine learning algorithms, namely Decision Tree, Random-Forest, K-Nearest Neighbors, Support Vector Machine, Artificial Neural Networks, and SubSpace KNN, are comparatively studied. …”
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Perovskite Solar Cell: Chemical Composition and Bandgap Energy via Machine Learning by Filipi França dos Santos, Kelly Cristine Da Silveira, Gesiane Mendonça Ferreira, Daniella Herdi Cariello, Mônica Calixto de Andrade

“…This study utilized the comprehensive MaterialsZone database to feed machine learning algorithms, focusing on Support Vector Machine (SVM) and Random Forest (RF) methodologies to predict the bandgap energy in a targeted perovskite composition. …”
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Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine Learning by Mithila Akter Mim, M. R. Khatun, Muhammad Minoar Hossain, Wahidur Rahman, Arslan Munir

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A Sentinel-2 machine learning dataset for tree species classification in Germany by M. Freudenberg, M. Freudenberg, S. Schnell, P. Magdon

“…<p>We present a machine learning dataset for tree species classification in Sentinel-2 satellite image time series of bottom-of-atmosphere reflectance. …”
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