Association of Per- and Polyfluoroalkyl Substances with Pan-Cancers Associated with Sex Hormones by Elizabeth Olarewaju, Emmanuel Obeng-Gyasi

“…Additionally, Bayesian kernel machine regression (BKMR) was applied to capture potential nonlinear relationships and interactions. …”
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Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon by Chao Wang, Shijie Jiang, Yi Zheng, Feng Han, Rohini Kumar, Oldrich Rakovec, Siqi Li

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Bio-Magneto Sensing and Unsupervised Deep Multiresolution Analysis for Labor Predictions in Term and Preterm Pregnancies by Ejay Nsugbe, Oluwarotimi Williams Samuel, Jose Javier Reyes-Lagos, Dawn Adams, Olusayo Obajemu

“…DWS is combined with select pattern-recognition-based prediction machines in order to assemble a clinical decision pipeline for the prediction of the states of various pregnancies, with a greater degree of machine intelligence. …”
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3D Radio Map-Based GPS Spoofing Detection and Mitigation for Cellular-Connected UAVs by Yongchao Dang, Alp Karakoc, Saba Norshahida, Riku Jantti

“…With the upcoming 5G and beyond wireless communication system, cellular-connected Unmanned Aerial Vehicles (UAVs) are emerging as a new pattern to give assistance for target searching, emergency rescue, and network recovery. …”
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A method for predicting postpartum depression via an ensemble neural network model by Yangyang Lin, Dongqin Zhou

“…The structure of the FCNN is simple and straightforward. The connection pattern among the neurons of the FCNN makes it easy to understand the relationship between the features and the target feature, endowing the proposed model with interpretability. …”
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An Automated Compliance Framework for Critical Infrastructure Security Through Artificial Intelligence by Sardar Muhammad Ali, Abdul Razzaque, Muhammad Yousaf, Rafi Us Shan

“…These impacts encompass the theft of confidential information, service interruptions, and expenses tied to breach remediation, underscoring the urgent necessity for strengthened cybersecurity strategies. Machine learning (ML) is highly effective in signifying cybersecurity standards, leveraging large-scale data analysis, pattern recognition, and adaptability to emerging threats. …”
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Viewing Soil Moisture Flash Drought Onset Mechanism and Their Changes Through XAI Lens: A Case Study in Eastern China by Jiajin Feng, Jun Li, Chong‐Yu Xu, Zhaoli Wang, Zhenxing Zhang, Xushu Wu, Chengguang Lai, Zhaoyang Zeng, Hongfu Tong, Shijie Jiang

“…Taking China as a case study, we present a novel framework that combines machine learning with interpretable and cluster techniques to investigate flash drought mechanisms from 1980 to 2018. …”
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Cooperative control method for multi-agent ground fracturing truck group based on offline reinforcement learning by RuYi Wang, HuiShen Jiao, YingCheng Tian, Yi Zhao, SiQi Wang, Ke Zhang, Bo Huang, QinRui Sun, DanDan Zhu

“…However, this manual decision-making pattern often exhibits insufficient collaborative capability when confronted with complex construction scenarios. …”
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Full-chain comprehensive assessment and multi-scenario simulation of geological disaster vulnerability based on the VSD framework: a case study of Yunnan province in China by Li Xu, Shucheng Tan, Runyang Li

“…The results show that from 2030 to 2050, GDV in Yunnan Province is generally at a medium–high level, with a spatial distribution pattern of “centered on Kunming, with a radial increase extending outward.” …”
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The utility of combining deep learning with metabarcoding to model biodiversity dynamics at a national scale by Adrian Baggström, Robert Goodsell, Laura van Dijk, Ela Iwaszkiewicz-Eggebrecht, Andreia Miraldo, Ayco J.M. Tack, Tobias Andermann

“…By combining detailed biodiversity surveys, geospatial data, and machine learning, we can model biodiversity with the aim of gaining insights into how these complex patterns behave. …”
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Automatic detection for a comprehensive view of Mayotte seismicity by Retailleau, Lise, Saurel, Jean-Marie, Laporte, Marine, Lavayssière, Aude, Ferrazzini, Valérie, Zhu, Weiqiang, Beroza, Gregory C., Satriano, Claudio, Komorowski, Jean-Christophe, OVPF Team

“…Moreover, while the VT earthquakes of the proximal cluster occur continuously with no apparent pattern, LP events occur in swarms that last for tens of minutes. …”
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Landslide and Collapse Susceptibility Analysis in Wenchuan Earthquake-damaged Area Based on Ensemble Learning Methods by DING Jiawei, WANG Xiekang

“…Recent advancements in data science and machine learning provide promising solutions. Two state-of-the-art ensemble learning algorithms, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM), are introduced to formulate dependable models for appraising susceptibility to landslides and collapses within the confines of Wenchuan County.MethodsA comprehensive evaluation of factors related to topography, geology, meteorology, and hydrology was conducted to select ten evaluative factors: Elevation, slope, aspect, terrain relief, distance to rivers, distance to faults, normalized difference vegetation index (NDVI), land cover type, average annual precipitation, and lithology. …”
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Unmasking Nuances Affecting Loneliness: Using Digital Behavioural Markers to Understand Social and Emotional Loneliness in College Students by Malik Muhammad Qirtas, Evi Zafeiridi, Eleanor Bantry White, Dirk Pesch

“…Our objectives were to (1) identify behavioural patterns linked to social and emotional loneliness, (2) evaluate the predictive power of these patterns for classifying loneliness types, and (3) determine the most significant digital markers used by machine learning models in loneliness prediction. …”
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A study on the correlation between MBTI dimensions and driving behavior characteristics by Liwei Bai, Tao Wang, Jianyao Tu, Bozhezi Peng, Zhuoyu Wang

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On the use of kolmogorov–arnold networks for adapting wind numerical weather forecasts with explainability and interpretability: application to madeira international airport by Décio Alves, Fábio Mendonça, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias

“…A key outcome of this study comes from the model’s ability to generate mathematical formulas that provide insights into the physical and mathematical dynamics influencing local wind patterns and improve the transparency, explainability, and interpretability of the employed machine learning models for atmosphere modeling.…”
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A deep fusion‐based vision transformer for breast cancer classification by Ahsan Fiaz, Basit Raza, Muhammad Faheem, Aadil Raza

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Decoding Subjective Understanding: Using Biometric Signals to Classify Phases of Understanding by Milan Lazic, Earl Woodruff, Jenny Jun

“…Distinct AU patterns were found for all five phases, with gradient boosting machine and random forest models achieving the highest predictive accuracy. …”
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Chlorophyll-a Outliers in the Banda Sea and its surroundings: Implications for Ecosystem Dynamics by Sunarwan Asuhadi, Mukti Zainuddin, Safruddin Safruddin, Musbir Musbir, Achmad Fachruddin Syah

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Unveiling the drivers contributing to global wheat yield shocks through quantile regression by Srishti Vishwakarma, Xin Zhang, Vyacheslav Lyubchich

“…Here, we study the spatiotemporal patterns of wheat yield shocks, quantified by the lower quantiles of yield fluctuations, in 86 countries over 30 years. …”
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Enhancing agricultural commodity price forecasting with deep learning by R. L. Manogna, Vijay Dharmaji, S. Sarang

“…Results show that deep learning models, particularly Long Short-Term Memory and Gated Recurrent Units, outperform others in capturing complex temporal patterns, achieving superior accuracy across error metrics. …”
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