Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands Based on In Situ Internet of Things Monitoring by Jie Song, Yujun Yi

“…The elucidation of transport pattern and prediction of water and salt in estuarine wetland soils remain significant challenges. …”
Get full text

Change in Fractional Vegetation Cover and Its Prediction during the Growing Season Based on Machine Learning in Southwest China by Xiehui Li, Yuting Liu, Lei Wang

“…Next, we constructed four machine learning models—light gradient boosting machine (LightGBM), support vector regression (SVR), <i>k</i>-nearest neighbor (KNN), and ridge regression (RR)—along with a weighted average heterogeneous ensemble model (WAHEM) to predict growing-season FVC in SWC from 2000 to 2023. …”
Get full text

Calculating the High‐Latitude Ionospheric Electrodynamics Using a Machine Learning‐Based Field‐Aligned Current Model by V. Sai Gowtam, Hyunju Connor, Bharat S. R. Kunduri, Joachim Raeder, Karl M. Laundal, S. Tulasi Ram, Dogacan S. Ozturk, Donald Hampton, Shibaji Chakraborty, Charles Owolabi, Amy Keesee

“…Abstract We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML‐AIM). …”
Get full text

Combining the SHAP Method and Machine Learning Algorithm for Desert Type Extraction and Change Analysis on the Qinghai–Tibetan Plateau by Ruijie Lu, Shulin Liu, Hanchen Duan, Wenping Kang, Ying Zhi

“…For regional desertification control and sustainable development, it is critical to quickly and accurately understand the distribution pattern and spatial and temporal changes of deserts. …”
Get full text

Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies by Renata Rozovsky, Maria Wolfe, Halimah Abdul‐waalee, Mariah Chobany, Greeshma Malgireddy, Jonathan A. Hart, Brianna Lepore, Farzan Vahedifard, Mary L. Phillips, Boris Birmaher, Alex Skeba, Rasim S. Diler, Michele A. Bertocci

“…We employed a whole‐brain machine learning approach focusing on gray matter volumes (GMVs) to contribute to defining objective biomarkers of BD and discriminating it from other forms of psychopathology, including subthreshold manic presentations without a BD Type I/II diagnosis. …”
Get full text

Urban growth simulation using cellular automata model and machine learning algorithms (case study: Tabriz metropolis) by Omid Ashkriz, Babak Mirbagheri, Ali Akbar Matkan, Alireza Shakiba

“…Introduction: Urban growth has accelerated in recent decades, therefore, predicting the future growth pattern of the city is very important to prevent environmental, economic, and social problems. …”
Get full text

Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy by Tobias Schraut, Anne Schützenberger, Tomás Arias-Vergara, Melda Kunduk, Matthias Echternach, Stephan Dürr, Julia Werz, Michael Döllinger

Get full text

Integrating proteomics and machine learning reveals characteristics and risks of lymph node-independent distant metastasis in colorectal cancer by Chenxiao Zheng, Baiwang Zhu, Yanyu Chen, Numan Shahid, Yiwang Hu, Hajar Mansoor Ahmed Ali Husain, Binbin Ou, Qiongying Zhang, Haobo Jin, Yating Zheng, Peng Li, Yifei Pan, Xiaodong Zhang, Xiaodong Zhang

“…Immunohistochemistry (IHC) confirmed its expression pattern, while wound healing and transwell assays elucidated the role of ITGA11 in CRC metastasis.ResultsThe LIMGs signature demonstrated strong predictive performance of lymph node-independent synchronous metastasis across cohorts. …”
Get full text

Uncovering abnormal gray and white matter connectivity patterns in Alzheimer’s disease spectrum: a dynamic graph theory analysis for early detection by Juanjuan Jiang, Tao Kang, Ronghua Ling, Yingqian Liu, Jiuai Sun, Yiming Li, Xiaoou Li, Hui Yang, Bingcang Huang, the Alzheimer’s Disease Neuroimaging Initiative

Get full text

Big team science reveals promises and limitations of machine learning efforts to model physiological markers of affective experience by Nicholas A. Coles, Bartosz Perz, Maciej Behnke, Johannes C. Eichstaedt, Soo Hyung Kim, Tu N. Vu, Chirag Raman, Julian Tejada, Van-Thong Huynh, Guangyi Zhang, Tanming Cui, Sharanyak Podder, Rushi Chavda, Shubham Pandey, Arpit Upadhyay, Jorge I. Padilla-Buritica, Carlos J. Barrera Causil, Linying Ji, Felix Dollack, Kiyoshi Kiyokawa, Huakun Liu, Monica Perusquia-Hernandez, Hideaki Uchiyama, Xin Wei, Houwei Cao, Ziqing Yang, Alessia Iancarelli, Kieran McVeigh, Yiyu Wang, Isabel M. Berwian, Jamie C. Chiu, Dan-Mircea Mirea, Erik C. Nook, Henna I. Vartiainen, Claire Whiting, Young Won Cho, Sy-Miin Chow, Zachary F. Fisher, Yanling Li, Xiaoyue Xiong, Yuqi Shen, Enzo Tagliazucchi, Leandro A. Bugnon, Raydonal Ospina, Nicolas M. Bruno, Tomas A. D'Amelio, Federico Zamberlan, Luis R. Mercado Diaz, Javier O. Pinzon-Arenas, Hugo F. Posada-Quintero, Maneesh Bilalpur, Saurabh Hinduja, Fernando Marmolejo-Ramos, Shaun Canavan, Liza Jivnani, Stanisław Saganowski

“…However, such conclusions would be premature because other teams exhibited the opposite pattern. Taken together, results illustrate how big team science can be leveraged to understand the promises and limitations of machine learning methods in affective science and beyond.…”
Get full text

Advancing Hydrogel-Based 3D Cell Culture Systems: Histological Image Analysis and AI-Driven Filament Characterization by Lucio Assis Araujo Neto, Alessandra Maia Freire, Luciano Paulino Silva

“…<b>Background:</b> Machine learning is used to analyze images by training algorithms on data to recognize patterns and identify objects, with applications in various fields, such as medicine, security, and automation. …”
Get full text

Mapping distribution patterns of invasive alien species in the Santa Monica Mountains using airborne imaging spectroscopy and line-point transect data by Nicoletta Stork, Amy Mainzer, Brooke Carruthers, Mark Mendelsohn, Gregory Asner, Roberta Martin

“…These results emphasize the value of fine-spatial and spectral resolution data for detecting IPS patterns in complex environments and demonstrate how combining airborne imaging spectroscopy with field-based methods can inform IPS management.…”
Get full text

Characterising physical activity patterns in community-dwelling older adults using digital phenotyping: a 2-week observational study protocol by Bruno Bonnechère, Dominique Hansen, Kim Daniels, Sharona Vonck, Jolien Robijns, Annemie Spooren

“…All measurements align with the Behaviour Change Wheel framework, assessing capability, opportunity and motivation. Machine learning will analyse data, employing unsupervised learning (eg, hierarchical clustering) to identify PA behaviour patterns and supervised learning (eg, recurrent neural networks) to predict behavioural influences. …”
Get full text

Quantitative assessment of factors that influence heat vulnerability in residential areas using machine learning and unmanned aerial vehicle by Jawoon Gu, Dongwoo Kim, Chulmin Jun, Seungwoo Son

“…High-resolution thermal imagery captured by unmanned aerial vehicles (UAVs) and interpretable machine learning (ML) techniques were used to model and analyze thermal patterns at the microscale. …”
Get full text

Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

Get full text

Mathematical Modeling as a Aspect for Designing Agricultural Machines and Units (Development History Of Southern Urals Scientific School) by Yu. S. Tsench, E. V. Godlevskaya

“…(Research purpose) The objective of the study is to identify the patterns in designing tillage machines and units and, based on these patterns forecast their potential enhancements using mathematical modeling. …”
Get full text

Automated long-term monitoring of stereotypical movement in polar bears under human care using machine learning by Matthias Zuerl, Philip Stoll, Ingrid Brehm, Jonas Sueskind, René Raab, Jan Petermann, Dario Zanca, Ralph Simon, Lorenzo von Fersen, Bjoern Eskofier

“…Finally, we classified the trajectory patterns as SB or normal behaviour using a lightweight random forest approach with an accuracy of 94.9 %. …”
Get full text

Understanding household VMT generation: A comparative analysis with traditional statistical models and a machine-learning approach by Guang Tian, Bob Danton, Bin Li, Vijaya Gopu, Julius A. Codjoe

“…This study is the first to compare these methods’ indications of the impacts of land-use patterns on VMT generation using a large multiregional dataset. …”
Get full text

Synergistic bioinformatics and sophisticated machine learning unveil ferroptosis-driven regulatory pathways and immunotherapy potential in breast carcinoma by Lei Xia, Zhen Ye, Man Zheng, Zhaofeng Tan

“…Conclusions The integration of bioinformatics and machine learning in this study underscores a strong correlation between FRG expression patterns and BRCA prognosis, affirming their potential as precise biomarkers for personalized immunotherapy.…”
Get full text

Street View-Enabled Explainable Machine Learning for Spatial Optimization of Non-Motorized Transportation-Oriented Urban Design by Yichen Ruan, Xiaoyi Zhang, Shaohua Wang, Xiuxiu Chen, Qiuxiao Chen

“…To advance evidence-based urban design prioritizing non-motorized mobility, this study proposes a street view-enabled explainable machine learning framework that systematically links built environment semantics to non-motorized transportation vitality optimization. …”
Get full text