Monitoring Soil Salinity in Arid Areas of Northern Xinjiang Using Multi-Source Satellite Data: A Trusted Deep Learning Framework by Mengli Zhang, Xianglong Fan, Pan Gao, Li Guo, Xuanrong Huang, Xiuwen Gao, Jinpeng Pang, Fei Tan

“…These variables are then integrated into various machine learning models—such as Ensemble Tree (ETree), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and LightBoost—as well as deep learning models, including Convolutional Neural Networks (CNN), Residual Networks (ResNet), Multilayer Perceptrons (MLP), and Kolmogorov–Arnold Networks (KAN), for modeling. …”
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Long-term forecasting of shield tunnel position and attitude deviation using the 1DCNN-informer method by Jiajie Zhen, Ming Huang, Shuang Li, Kai Xu, Qianghu Zhao

“…However, current machine learning models for predicting the position and attitude deviations of shield machines encounter significant challenges in achieving reliable long-term forecasting during shield tunneling. …”
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Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data by Xiangqian Li, Qiongyan Peng, Ruoque Shen, Wenfang Xu, Zhangcai Qin, Shangrong Lin, Si Ha, Dongdong Kong, Wenping Yuan

“…This study revised a machine learning spatiotemporal fusion model (InENVI) to produce a high-resolution NDVI dataset with 8-day temporal and 30 m spatial resolution, covering China from 2001 to 2020. …”
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A Novel Active Learning Technique for Fetal Health Classification Based on XGBoost Classifier by Kaushal Bhardwaj, Niyati Goyal, Bhavika Mittal, Vandna Sharma, Shiv Naresh Shivhare

“…The application of machine learning algorithms in monitoring fetal health helps to improve the chances of timely intervention and better outcomes in the event of any possible health issues in fetuses. …”
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Advancements in Liposomal Nanomedicines: Innovative Formulations, Therapeutic Applications, and Future Directions in Precision Medicine by Izadiyan Z, Misran M, Kalantari K, Webster TJ, Kia P, Basrowi NA, Rasouli E, Shameli K

“…The integration of artificial intelligence and machine learning in optimizing liposomal designs promises to revolutionize personalized medicine, paving the way for innovative strategies in disease detection and therapeutic interventions. …”
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APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions by Eva Viesi, Ugo Perricone, Patrick Aloy, Rosalba Giugno

“…Moreover, the interactivity between biological entities can be represented through combined feature vectors that can be given as input to a machine learning (ML) model to capture the underlying interaction. …”
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Monitoring Yield and Quality of Forages and Grassland in the View of Precision Agriculture Applications—A Review by Abid Ali, Hans-Peter Kaul

“…At a larger scale, we discuss coupling of remote sensing with weather data (synergistic grassland yield modelling), Sentinel-2 data with radiative transfer modelling (RTM), Sentinel-1 backscatter, and Catboost–machine learning methods for digital mapping in terms of precision harvesting and site-specific farming decisions. …”
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Feature Representations Using the Reflected Rectified Linear Unit (RReLU) Activation by Chaity Banerjee, Tathagata Mukherjee, Eduardo Pasiliao Jr.

“…Deep Neural Networks (DNNs) have become the tool of choice for machine learning practitioners today. One important aspect of designing a neural network is the choice of the activation function to be used at the neurons of the different layers. …”
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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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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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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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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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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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Biomimic models for in vitro glycemic index: Scope of sensor integration and artificial intelligence by Mohammed Salman C K, Muskan Beura, Archana Singh, Anil Dahuja, Vinayak B. Kamble, Rajendra P. Shukla, Sijo Joseph Thandapilly, Veda Krishnan

“…Non-enzymatic sensors offer superior stability and repeatability in complex matrices, enabling real-time glucose quantification across multiple timepoints without enzyme degradation constraints. Machine learning algorithms, both supervised and unsupervised, enhance predictive accuracy by elucidating complex relationships within digestion data. …”
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