Improving chlorophyll-a estimation using Sentinel-2 data: a comparative analysis of augmented datasets by Jinmyeong Lee, Do Hyuck Kwon, Heewon Jeong, Gibeom Nam, Euiho Hwang, Jin Hwi Kim, Kyung Hwa Cho, Hyo Gyeom Kim

“…The synergetic impacts of rapid urbanization and climate change contribute to the unprecedented occurrence of severe algal blooms, which require sufficient high-concentration data for successful model training. In this study, we evaluated the feasibility of integrating datasets from two different watersheds to estimate chlorophyll-a (Chl-a) concentrations using machine learning models with Sentinel-2 imagery. …”
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Integrating Low-Altitude Remote Sensing and Variable-Rate Sprayer Systems for Enhanced Cassava Crop Management by Pongpith Tuenpusa, Grianggai Samseemoung, Peeyush Soni, Thirapong Kuankhamnuan, Waraphan Sarasureeporn, Warinthon Poonsri, Apirat Pinthong

“…The image processing algorithms and calibration methods used were deemed acceptable, with drones equipped with variable-rate sprayer systems outperforming RC helicopters in overall quality. …”
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Distilling knowledge from graph neural networks trained on cell graphs to non-neural student models by Vasundhara Acharya, Bülent Yener, Gillian Beamer

“…Even non-neural models can learn from a neural network teacher. We evaluated our approach across varying dataset complexities, including the presence or absence of distribution shifts, varying degrees of imbalance, and different levels of graph complexity for training GNNs. …”
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Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

“…These methods depend on empirical relationships and constants that can differ between basins. This study proposes an effective data-driven approach that utilizes machine learning algorithms to forecast reservoir pore pressure. …”
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A machine-learning approach for predicting butyrate production by microbial consortia using metabolic network information by Claudia Silva-Andrade, Sergio Hernández, Pedro Saa, Ernesto Perez-Rueda, Daniel Garrido, Alberto J. Martin

“…The performance of the algorithms was evaluated using k-fold cross-validation and new experimental data, displaying a Pearson correlation coefficient exceeding 0.75 for the predicted and observed butyrate production in two bacteria consortia. …”
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SPA-Net: An Offset-Free Proposal Network for Individual Tree Segmentation from TLS Data by Yunjie Zhu, Zhihao Wang, Qiaolin Ye, Lifeng Pang, Qian Wang, Xiaolong Zheng, Chunhua Hu

“…SPA-Net was rigorously validated on two different forest datasets. On both BaiMa and Hong-Tes Lake datasets, the approach demonstrated superior performance compared to several contemporary segmentation approaches evaluated under the same conditions. …”
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Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction by Zhengbiao Hu, Shuangshan Bu, Kai Wang, Qianqian Cao, Huanhuan Zheng, Jie Yang, Shanshan Chen, Yuemeng Wu, Wenkai Ren, Chenlei He

“…In this study, multiple machine learning algorithms were used to develop a non-invasive and improved model for the diagnosis of IgAN. …”
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Advancing road maintenance with EfficientDet-based pothole monitoring by Jenefa Archpaul, Taurshia Antony, Kuriakose Bessy Mani, Kumar Edward Naveen Vijaya, Lincy Archpaul

“…This diversity enables the model to generalize effectively across different conditions. Our experimental evaluations demonstrate that the EfficientDet-based model achieves an impressive average precision of 0.90 and a robust recall of 0.92, highlighting its capacity for accurate and swift pothole detection-an essential component for improving road maintenance. …”
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The Comprehensive Analysis of Weighted Gene Co-Expression Network Analysis and Machine Learning Revealed Diagnostic Biomarkers for Breast Implant Illness Complicated with Breast Ca... by Huang Z, Wang H, Pang H, Zeng M, Zhang G, Liu F

“…Enrichment analysis, the protein–protein interaction network (PPI), and machine learning algorithms were performed to explore the hub genes. …”
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Comparison of Raman spectroscopy with mass spectrometry for sequence typing of Acinetobacter baumannii strains: a single-center study by Suling Liu, Ni Zhang, Jiawei Tang, Chong Chen, Weisha Wang, Jingfang Zhou, Long Ye, Xiaoli Chen, ZhengKang Li, Liang Wang

“…This study developed a novel approach, combining surface-enhanced Raman spectroscopy (SERS) with machine-learning (ML) algorithms, to construct predictive models for A. baumannii sequence typing based on SERS spectra. …”
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Prognostic and therapeutic relevance of IL2RG-related LncRNAs in clear cell renal cell carcinoma by Weijing Hu, Bo Wu, Yongquan Chen, Xiaoling Guo, Xiaosong Wang, Dongwen Wang

“…Gene set enrichment analysis (GSEA), single-sample GSEA (ssGSEA), CIBERSORT algorithms, and TISCH2 database were applied to analyze immune function and immune cell infiltration in different risk groups. …”
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Estimation of Reference Crop Evapotranspiration in the Yellow River Basin Based on Machine Learning and Its Regional and Drought Adaptability Analysis by Jun Zhao, Huayu Zhong, Congfeng Wang

“…The objective was to comprehensively evaluate their performance and robustness in estimating ET₀ under different climatic zones and drought conditions and to compare them with traditional empirical formulas. …”
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Deciphering the role of miR-71 in Echinococcus multilocularis early development in vitro. by Matías Gastón Pérez, Markus Spiliotis, Natalia Rego, Natalia Macchiaroli, Laura Kamenetzky, Nancy Holroyd, Marcela Alejandra Cucher, Klaus Brehm, Mara Cecilia Rosenzvit

“…Using genomic information and bioinformatic algorithms for miRNA binding prediction, we found a high number of potential miR-71 targets in E. multilocularis. …”
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SkelETT—Skeleton-to-Emotion Transfer Transformer by Pedro Victor Vieira Paiva, Josue Junior Guimaraes Ramos, Marina Gavrilova, Marco Antonio Garcia de Carvalho

“…Comprising a series of encoder layers, SkelETT patches 2D body pose representations, it also includes multi-head self-attention mechanisms and position-wise feed-forward networks, providing a powerful framework for extracting hierarchical features from sequential body pose data. We propose and evaluate the impact of different fine-tuning strategies on pose data using the MPOSE action recognition dataset as a pre-training source. …”
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FrameBoost: Advanced Video Analytics With Inference Trigger Frame Selection via Tracking Error Estimation by Jin Mo Yang, Sunwook Hwang, Jeongjun Park, Saewoong Bahk

“…Through comprehensive evaluations across diverse scenarios, we demonstrate that <inline-formula> <tex-math notation="LaTeX">$\mathsf{FrameBoost}$ </tex-math></inline-formula> achieves superior accuracy with comparable or lower computational overhead than existing approaches, making it ideal for various video analytics systems with different system-level requirements.…”
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Retrieval of Vegetation Indices and Vegetation Fraction in Highly Compact Urban Areas: A 3D Radiative Transfer Approach by Wenya Xue, Liping Feng, Jinxin Yang, Yong Xu, Hung Chak Ho, Renbo Luo, Massimo Menenti, Man Sing Wong

“…The effects of the urban material heterogeneity and 3D structure on the NDVI, and the performance of three NDVI-based fractional vegetation cover (FVC) inversion algorithms, are evaluated. The results show that the effects of the building material heterogeneity on the NDVI are negligible under sunlit conditions but not negligible under shaded conditions. …”
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Predicting patient outcomes and risk for revision surgery after hip and knee replacement surgery: study protocol for a comparison of modelling approaches using the Swiss National J... by Léonie Hofstetter, Nathalie Schweyckart, Christof Seiler, Christian Brand, Laura C. Rosella, Mazda Farshad, Milo A. Puhan, Cesar A. Hincapié

“…Machine learning (ML) algorithms are increasingly used as an alternative to traditional logistic regression (LR) prediction, but there is uncertainty about their superiority in overall model performance. …”
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Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis by Daniel Nasef, Demarcus Nasef, Viola Sawiris, Peter Girgis, Milan Toma

“…(1) <b>Background</b>: The exploration of various machine learning (ML) algorithms for classifying the state of Lumbar Intervertebral Discs (IVD) in orthopedic patients is the focus of this study. …”
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Integrative machine learning model of RNA modifications predict prognosis and treatment response in patients with breast cancer by Tao Wang, Shu Wang, Zhuolin Li, Jie Xie, Qi Jia, Jing Hou

“…In addition, immune infiltration levels were evaluated via six different algorithms, and immune checkpoint inhibitor responsiveness was predicted. …”
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Petrographic image classification of complex carbonate rocks from the Brazilian pre-salt using convolutional neural networks by Mateus Basso, João Paulo da Ponte Souza, Guilherme Furlan Chinelatto, Luis Augusto Antoniossi Mansini, Alexandre Campane Vidal

“…Abstract Machine learning (ML) algorithms have been widely applied across geosciences for tasks such as data conditioning, resolution enhancement, and image classification. …”
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