Data Mining of Infertility and Factors Influencing Its Development: A Finding From a Prospective Cohort Study of RaNCD in Iran by Hosna Heydarian, Masoumeh Abbasi, Farid Najafi, Mitra Darbandi

“…Methods In this study, we examined the impact of lifestyle factors on infertility using machine learning and data mining techniques, specifically Association Rules. …”
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Chronic nitrogen legacy in the aquifers of China by Xin Liu, Fu-Jun Yue, Li Li, Feng Zhou, Hang Wen, Zhifeng Yan, Lichun Wang, Wei Wen Wong, Cong-Qiang Liu, Si-Liang Li

“…Our understanding of groundwater nitrate concentrations is often limited by inaccessibility of groundwater and scarcity of nitrate data in groundwater. Here we used machine learning and decision tree-heatmap analysis by compiling nitrate concentrations and isotope data from 4047 groundwater sites across China to understand their dynamics and drivers across gradients of geographical, climate, and human factors. …”
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Artificial Intelligence and Postpartum Hemorrhage by Sam J Mathewlynn, Mohammadreza Soltaninejad, Sally L Collins, Yang Pan

“…Recently, there has been a surge in interest in using artificial intelligence (AI), including machine learning and deep learning, across many areas of health care. …”
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Multidimensional library for the improved identification of per- and polyfluoroalkyl substances (PFAS) by Kara M. Joseph, Anna K. Boatman, James N. Dodds, Kaylie I. Kirkwood-Donelson, Jack P. Ryan, Jian Zhang, Paul A. Thiessen, Evan E. Bolton, Alan Valdiviezo, Yelena Sapozhnikova, Ivan Rusyn, Emma L. Schymanski, Erin S. Baker

“…This information will provide the scientific community with essential characteristics to expand analytical assessments of PFAS and augment machine learning training sets for discovering new PFAS.…”
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Transfusion in trauma: empiric or guided therapy? by Liam Barrett, Nicola Curry

“…Such approaches may include the integration of machine learning technologies in clinical systems, with real-time linkage of clinical and laboratory data, to aid early recognition of patients at the greatest risk of bleeding and to direct and individualize transfusion therapies. …”
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The usefulness of automated high frequency ultrasound image analysis in atopic dermatitis staging by Joanna Czajkowska, Adriana Polańska, Anna Slian, Aleksandra Dańczak-Pazdrowska

“…The fully automated image processing framework combines advanced machine learning techniques for fast, reliable, and repeatable HFUS image analysis, supporting clinical decisions. …”
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Mpox in sports: A comprehensive framework for anticipatory planning and risk mitigation in football based on lessons from COVID-19 by Karim Chamari, Helmi Ben Saad, Wissem Dhahbi, Jad Washif, Abdelfatteh El Omri, Piotr Zmijewski, Ismail Dergaa

“…We propose innovative risk assessment methods using global positioning system tracking and machine learning for contact analysis, alongside tailored testing and hygiene protocols. …”
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ZleepNet: A Deep Convolutional Neural Network Model for Predicting Sleep Apnea Using SpO2 Signal by Hnin Thiri Chaw, Thossaporn Kamolphiwong, Sinchai Kamolphiwong, Krongthong Tawaranurak, Rattachai Wongtanawijit

“…We conducted experiments to evaluate the performance of the proposed CNN using real patient data and compared them with traditional machine learning methods such as least discriminant analysis (LDA) and support vector machine (SVM), baggy representation tree, and artificial neural network (ANN) on publicly available sleep datasets using the same parameter setting. …”
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Using Gradient Boosting Regression to Improve Ambient Solar Wind Model Predictions by R. L. Bailey, M. A. Reiss, C. N. Arge, C. Möstl, C. J. Henney, M. J. Owens, U. V. Amerstorfer, T. Amerstorfer, A. J. Weiss, J. Hinterreiter

“…Here, we present a machine learning approach in which solutions from magnetic models of the solar corona are used to output the solar wind conditions near the Earth. …”
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DAS to discharge: using distributed acoustic sensing (DAS) to infer glacier runoff by John-Morgan Manos, Dominik Gräff, Eileen Rose Martin, Patrick Paitz, Fabian Walter, Andreas Fichtner, Bradley Paul Lipovsky

“…While testing several types of machine learning (ML) models, we establish a regression problem, using the DAS data as the dependent variable, to infer the glacier discharge observed at a proglacial stream gauge. …”
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Fecal occult blood affects intestinal microbial community structure in colorectal cancer by Wu Guodong, Wu Yinhang, Wu Xinyue, Shen Hong, Chu Jian, Qu Zhanbo, Han Shuwen

“…Characteristic gut bacteria were screened, and various machine learning algorithms were applied to construct CRC risk prediction models. …”
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Signal Recovery in Power Systems by Correlated Gaussian Processes by Marcel Zimmer, Daniele Carta, Thiemo Pesch, Andrea Benigni

“…Based on only local power system topology, the presented algorithm combines cross-channel information of the considered signals with a universal, nonparametric probabilistic machine learning regression to recover missing data. Starting from the theoretical background, the proposed approach is presented and contextualized in the framework of signal recovery for power systems. …”
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Transforming precision medicine: The potential of the clinical artificial intelligent single‐cell framework by Christian Baumgartner, Dagmar Brislinger

“…The article explores development strategies such as data expansion, machine learning advancements, and interpretability improvements. …”
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Stock volatility as an anomalous diffusion process by Rubén V. Arévalo, J. Alberto Conejero, Òscar Garibo-i-Orts, Alfred Peris

“…In financial markets, accurately estimating asset volatility—whether historical or implied—is vital for investors.We introduce a novel methodology to estimate the volatility of stocks and similar assets, combining anomalous diffusion principles with machine learning. Our architecture combines convolutional and recurrent neural networks (bidirectional long short-term memory units). …”
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Data driven prediction of fragment velocity distribution under explosive loading conditions by Donghwan Noh, Piemaan Fazily, Songwon Seo, Jaekun Lee, Seungjae Seo, Hoon Huh, Jeong Whan Yoon

“…This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition. …”
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Sparse Linear Discriminant Analysis With Constant Between-Class Distance for Feature Selection by Shuangle Guo, Yongxia Li, Jianguang Zhang, Yue Liu, Tian Tian, Mengchen Guo

“…Feature selection is an important preprocessing step in machine learning to remove irrelevant and redundant features. …”
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Enhancing Air Conditioning System Efficiency Through Load Prediction and Deep Reinforcement Learning: A Case Study of Ground Source Heat Pumps by Zhitao Wang, Yubin Qiu, Shiyu Zhou, Yanfa Tian, Xiangyuan Zhu, Jiying Liu, Shengze Lu

“…This study proposes a control method that integrates deep reinforcement learning with load forecasting, to enhance the energy efficiency of ground source heat pump systems. Eight machine learning models are first developed to predict future cooling loads, and the optimal one is then incorporated into deep reinforcement learning. …”
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Rolling Bearing Fault Diagnosis Based on Domain Adaptation and Preferred Feature Selection under Variable Working Conditions by Xiao Yu, Wei Chen, Chuanlong Wu, Enjie Ding, Yuanyuan Tian, Haiwei Zuo, Fei Dong

“…In real industrial scenarios, with the use of conventional machine learning techniques, data-driven diagnosis models have a limitation that it is difficult to achieve the desirable fault diagnosis performance, and the reason is that the training and testing datasets are assumed to have the same feature distributions. …”
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Combination ATR-FTIR with Multiple Classification Algorithms for Authentication of the Four Medicinal Plants from <i>Curcuma</i> L. in Rhizomes and Tuberous Roots by Qiuyi Wen, Wenlong Wei, Yun Li, Dan Chen, Jianqing Zhang, Zhenwei Li, De-an Guo

“…We developed a rapid analysis method for identification of affinis and different medicinal materials using attenuated total reflection-Fourier-transform infrared spectroscopy (ATR-FTIR) combined with machine learning algorithms. The original spectroscopic data were pretreated using derivatives, standard normal variate (SNV), multiplicative scatter correction (MSC), and smoothing (S) methods. …”
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Toward Semi-Autonomous Robotic Arm Manipulation Operator Intention Detection From Force Data by Abdullah S. Alharthi, Ozan Tokatli, Erwin Lopez, Guido Herrmann

“…To address this challenge, we propose enhancing the teleoperation system with an assistive model capable of predicting operator intentions and dynamically adapting to their needs. The machine learning model processes robotic arm force data, analyzing spatiotemporal patterns to accurately detect the ongoing task before its completion. …”
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