Cotton Crop Classification using Optical and Microwave Remote Sensing Datasets in Google Earth Engine by B. Meerasha, M. Sagayam

“…Next, cotton crops through RF classifiers were applied on median composites of Sentinel-1 and Sentinel-2 data for each pixel in the region. …”
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A First Look at River Discharge Estimation From SWOT Satellite Observations by Konstantinos M. Andreadis, Steve P. Coss, Michael Durand, Colin J. Gleason, Travis T. Simmons, Nikki Tebaldi, David M. Bjerklie, Craig Brinkerhoff, Robert W. Dudley, Igor Gejadze, Kevin Larnier, Pierre‐Olivier Malaterre, Hind Oubanas, George H. Allen, Paul D. Bates, Cédric H. David, Alessio Domeneghetti, Omid Elmi, Luciana Fenoglio Marc, Renato Prata de Moraes Frasson, Elisa Friedmann, Pierre‐André Garambois, Jaclyn Gehring, Augusto Getirana, Marissa Hughes, Jonghyun Lee, Pascal Matte, J. Toby Minear, Jérôme Monnier, Aggrey Muhebwa, Mohammad J. Tourian, Tamlin M. Pavelsky, Ryan M. Riggs, Ernesto Rodríguez, Md Safat Sikder, Laurence C. Smith, Cassie Stuurman, Jay Taneja, Angelica Tarpanelli, Jida Wang, Brent A. Williams, Bidhyananda Yadav

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Machine learning and response surface methodology forecasting comparison for improved spray dry scrubber performance with brine sludge-derived sorbent by B.J. Chepkonga, L. Koech, R.S. Makomere, H.L. Rutto

“…The response surface methodology (RSM) model also proved to be a reliable forecasting tool, indicating its potential as a practical alternative to complex algorithmic computations in scenarios with limited raw data.…”
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Deep learning-based sow posture classifier using colour and depth images by Verônica Madeira Pacheco, Tami M. Brown-Brandl, Rafael Vieira de Sousa, Gary A. Rohrer, Sudhendu Raj Sharma, Luciane Silva Martello

“…Different deep learning algorithms were developed to detect sow postures from three types of images: colour (RGB), depth (depth image transformed into greyscale), and fused (colour-depth composite images). …”
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Deep Residual Transfer Ensemble Model for mRNA Gene-Expression-Based Breast Cancer by Job Prasanth Kumar Chinta Kunta, Vijayalakshmi A. Lepakshi

“…The E2E ensemble learning method used bagging, AdaBoost, Random Forest, Extra Tree Classifier and XGBoost algorithms as base classifier to perform maximum voting-based prediction. …”
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Spatiotemporal prediction of alpine wetlands under multi-climate scenarios in the west of Sichuan, China by Haijun Wang, Xiangdong Kong, Onanong Phewnil, Ji Luo, Pengju Li, Xiyong Chen, Tianhui Xie

“…Methods We employed a large sample and composite supervised classification algorithms to classify alpine wetlands and generate wetland maps, based on the Google Earth Engine cloud computing platform. …”
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Exploring the capabilities of hyperspectral remote sensing for soil texture evaluation by Mohammad Hosseinpour-Zarnaq, Mahmoud Omid, Fereydoon Sarmadian, Hassan Ghasemi-Mobtaker, Reza Alimardani, Pouya Bohlol

“…In this study, we modeled and generated a composite map of soil texture variability using PRISMA images collected from agricultural areas in Iran's dry and semi-arid regions. …”
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Computational methods and artificial intelligence-based modeling of magnesium alloys: a systematic review of machine learning, deep learning, and data-driven design and optimizatio... by Hanxuan Wang, Raman Kumar, Raman Kumar, Ashutosh Pattanaik, Rajender Kumar, Rajender Kumar, Ali Saeed Owayez Khawaf Aljaberi, Mayada Ahmed Abass

“…The review highlights the extensive application of models, including Artificial Neural Networks, Convolutional Neural Networks, and hybrid frameworks that combine ML with optimization algorithms or physical simulations. These approaches enhance predictions on mechanical properties, microstructural changes, corrosion behavior, and processing results of Mg alloys. …”
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Unveiling psychobiological correlates in primary Sjögren’s syndrome: a machine learning approach to determinants of disease burden by László V. Módis, László V. Módis, András Matuz, András Matuz, Zsófia Aradi, Ildikó Fanny Horváth, Antónia Szántó, Antal Bugán

“…Three machine learning algorithms were trained to predict outcome variables, first by each measure category, then on the entire set of predictor variables. …”
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Integrating deep learning in public health: a novel approach to PICC-RVT risk assessment by Yue Li, Yue Li, Shengxiao Nie, Lei Wang, Dongsheng Li, Shengmiao Ma, Ting Li, Hong Sun

“…Existing models often assess PICC-RVT risk as static and discrete outcomes, which may limit their practical application.ObjectivesThis study aims to evaluate the effectiveness of seven diverse machine learning algorithms, including three deep learning and four traditional machine learning models, that incorporate time-series data to assess PICC-RVT risk. …”
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Oxidative balance score predicts chronic kidney disease risk in overweight adults: a NHANES-based machine learning study by Leying Zhao, Leying Zhao, Cong Zhao, Cong Zhao, Yuchen Fu, Yuchen Fu, Xiaochang Wu, Xiaochang Wu, Xuezhe Wang, Xuezhe Wang, Yaoxian Wang, Yaoxian Wang, Yaoxian Wang, Huijuan Zheng

“…Additionally, 14 machine learning algorithms were trained and validated using SMOTE-balanced data and five-fold cross-validation. …”
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Comparative assessment of line probe assays and targeted next-generation sequencing in drug-resistant tuberculosis diagnosisResearch in context by Giovanna Carpi, Marva Seifert, Andres De la Rossa, Swapna Uplekar, Camilla Rodrigues, Nestani Tukvadze, Shaheed V. Omar, Anita Suresh, Timothy C. Rodwell, Rebecca E. Colman

“…Interpretation: LPAs demonstrated lower sensitivity and more limited drug resistance detection compared to tNGS workflows, underscoring the advantages of tNGS for improving DR-TB diagnostic algorithms. These findings provide critical evidence to guide updates in DR-TB diagnostic programs. …”
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Design, Construction, and Evaluation of an Automatic Feeder Control System for Sugarcane Billet Planters by B. Abbasian, M. E. Khorasani Ferdavani, H. Zaki Dizaji

“…Additional research could explore advancements in sensor technology and control algorithms to further enhance the performance of automatic feeder control systems.AcknowledgmentThe authors would like to express their gratitude to the Managing Director of Farabi Agro-Industrial Company and its staff, as well as the technical staff of Poya Sazan Sabz Avane Company, who cooperated in the preparation and evaluation stages of the system. …”
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Home-Based Intervention Tool for Cardiac Telerehabilitation: Protocol for a Controlled Trial by Francesca Mastorci, Maria Francesca Lodovica Lazzeri, Lamia Ait-Ali, Paolo Marcheschi, Paola Quadrelli, Massimiliano Mariani, Rafik Margaryan, Wanda Pennè, Marco Savino, Giuseppe Prencipe, Alina Sirbu, Paolo Ferragina, Corrado Priami, Alessandro Tommasi, Cesare Zavattari, Pierluigi Festa, Stefano Dalmiani, Alessandro Pingitore

“…The secondary aims are to implement the system in a “real-life” context of postcardiac surgical rehabilitation, and to create a data set and a data collection methodology to prototype data analytics algorithms and artificial intelligence techniques for customizing the rehabilitation pathway. …”
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Accuracy and consequences of using trial-of-antibiotics for TB diagnosis (ACT-TB study): protocol for a randomised controlled clinical trial by Neil French, Peter MacPherson, Titus Henry Divala, Katherine L Fielding, Marriott Nliwasa, Chikondi Charity Kandulu, Lingstone Chiume, Sanderson Chilanga, Masiye John Ndaferankhande

“…Standard diagnostic algorithms include ‘trial-of-antibiotics’—empirical antibiotic treatment given to mycobacteriology-negative individuals to treat infectious causes of symptoms other than tuberculosis, as a ‘rule-out’ diagnostic test for tuberculosis. …”
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Study protocol: diagnosis of atrial fibrillation in postoperative thoracic surgery using a smartwatch, an open-label randomised controlled study (THOFAWATCH trial) by Osama Abou-Arab, Pierre Huette, Guillaume Haye, Mathieu Guilbart, Momar Diouf, Christophe Beyls, Herve Dupont, Yazine Mahjoub, Thomas Lefebvre, Azrat Ibrahima, Guillaume Bayart, Franck Lhotellier, Michael Radji, Katy-Anne Walczak, Matthieu Caboche, Florence De Dominicis, Olivier Georges, Pascal Berna, Geonie Merlusca, Alexis Hermida, Sarah Traullé, Michaël Bernasinski, Adrien Coupez, Patricia Besserve, Alice Henry, Camille Daumin, Ammar BenAmmar, Florent Leviel, Pierre-Yves Macq, Beatris Alina Labont, Gilles David, Julien Epailly, Paul-Emmanuel Esmard, Agathe Vernier, Pauline Bartoli, Jerôme Delmas, Sophie Romby, Simon Marx, Mohammed Fikri, Hélène Benoit-Fallet, Eric Nam, Jérome Zakine, Amandine Zaatar, Thomas Flet

“…Smartwatches equipped with single-lead ECG and atrial fibrillation (AF) detection algorithms offer a novel approach for early POAF detection. …”
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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

“…To approach the common condition of sparsely sampled training data, we transformed the prediction problem into an overdetermined one by applying composite fitting to both capillary pressure and pore throat size distribution, and Gaussian cumulative distribution fitting to the NMR T2 measurements, generating evenly sampled data points. …”
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Clinical characteristics, prognosis, and predictive modeling in class IV ± V lupus nephritis by Anjing Wang, Anjing Wang, Yunlong Qin, Yunlong Qin, Yan Xing, Zixian Yu, Liuyifei Huang, Jinguo Yuan, Yueqing Hui, Mei Han, Guoshuang Xu, Jin Zhao, Shiren Sun

“…The prognostic model was developed using machine learning algorithms and Cox regression. The model’s performance was evaluated in terms of discrimination, calibration, and risk classification using the concordance index (C-index), integrated brier score (IBS), net reclassification index (NRI), and integrated discrimination improvement (IDI), respectively.ResultsA total of 313 patients were enrolled for this study, including 156 class IV and 157 class IV+V LN. …”
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Research Status and Prospects of Key Technologies for Rice Smart Unmanned Farms by YU Fenghua, XU Tongyu, GUO Zhonghui, BAI Juchi, XIANG Shuang, GUO Sien, JIN Zhongyu, LI Shilong, WANG Shikuan, LIU Meihan, HUI Yinxuan

“…Rice yield estimation technology is mainly used to predict yield by combining multi-source data and algorithms, but there are still problems such as the difficulty of integrating multi-source data, which requires further research. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…The dataset consists of radar composite reflectivity mosaics over North China within the range of 116.10–117.50°E and 37.77–38.77°N, collected from June to September during 2018–2022. …”
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