Optimum Combination of Spectral Variables for Crop Mapping in Heterogeneous Landscapes based on Sentinel-2 Time Series and Machine Learning by J. G. de Oliveira Júnior, J. C. D. M. Esquerdo, J. C. D. M. Esquerdo, R. A. C. Lamparelli, R. A. C. Lamparelli

“…This article aimed to determine a workflow for more efficient large-scale crop mapping using a time series of images from the Sentinel-2 Satellite, statistical methods of attribute selection, and machine learning. The proposed methodology explores the best possible combination of spectral variables related to vegetation (16 vegetation indices in the RGB, NIR, SWIR, and Red Edge regions) to characterize different spectro-temporal profiles of Land Use and Land Cover (LULC) in spatially heterogeneous landscapes. …”
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Bearing Response Prediction in Hydrothermal Aged Carbon Fiber Reinforced Epoxy Composite Joints Using Machine Learning Techniques by Mohit Kumar, Govind Vashishtha, Babita Dhiman, Sumika Chauhan

“…The machine learning technique, support vector regression is trained and evaluated to assess their accuracy and reliability in predicting bearing response. …”
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Optimizing Academic Certificate Management With Blockchain and Machine Learning: A Novel Approach Using Optimistic Rollups and Fraud Detection by Khoa Tan-Vo, Khanh Pham, Phu Huynh, Mong-Thy Nguyen Thi, Thu-Thuy Ta, Thu Nguyen, Tu-Anh Nguyen-Hoang, Ngoc-Thanh Dinh, Hong-Tri Nguyen

“…The experimental outcomes validate the effectiveness of Optimistic Rollups in certificate revocation, showing a notable approximately 61.92% reduction in both transaction costs and latency. Moreover, the machine learning model displays impressive performance, achieving high accuracy in detecting fraudulent users, with an average F1-score of 99.42% and an AUC score nearing perfection. …”
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Computer Science Integrations with Laser Processing for Advanced Solutions by Serguei P. Murzin

“…The role of intelligent control systems, driven by machine learning and artificial intelligence, was examined, showcasing how a real-time data analysis and adjustments lead to improved process reliability and quality. …”
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Integrated machine learning and population attributable fraction analysis of systemic inflammatory indices for mortality risk prediction in diabetes and prediabetes by Zixi Zhang, Chenyang Li, Yichao Xiao, Chan Liu, Xiaoqin Luo, Cancan Wang, Yongguo Dai, Qiuzhen Lin, Zeying Zhang, Cheng Zheng, Jiafeng Lin, Tao Tu, Qiming Liu

“…Their integration into machine learning models enhances risk prediction and may inform population-level strategies for cardiometabolic risk stratification.…”
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Predicting Flood Inundation after a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network by Leon S. Besseling, Anouk Bomers, Suzanne J. M. H. Hulscher

“…However, their high computational cost makes them unsuitable for real-time flood forecasting. Machine learning models are a promising alternative, as they offer reasonable accuracy at a significant reduction in computation time. …”
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Thermal performance, entropy generation, and machine learning insights of Al₂O₃-TiO₂ hybrid nanofluids in turbulent flow by Praveen Kumar Kanti, V. Vicki Wanatasanappan, Nejla Mahjoub Said, Suman Saini, Vijayalaxmi Mishra, Prabhu Paramasivam, Mohamed Yusuf

“…Furthermore, employing machine learning techniques, highly accurate models are developed. …”
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Reaching machine learning leverage to advance performance of electrocatalytic CO2 conversion in non-aqueous deep eutectic electrolytes by Ahmed Halilu, Mohamed Kamel Hadj-Kali, Hanee Farzana Hizaddin, Mohd Ali Hashim, Emad M. Ali, Suresh Bhargava

“…Abstract Deep eutectic electrolytes (DEEs) show promise for future electrochemical systems due to their adjustable buffer capacities. This study utilizes machine learning algorithms to analyse the carbon dioxide reduction reaction (CO2RR) in DEEs with a buffer capacity of approximately 10.21 mol/pH. …”
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Prediction of the Non-Reducing Biomineralization of Nuclide–Microbial Interactions by Machine Learning: The Case of Uranium and <i>Bacillus subtilis</i> by Shirong Qiang, Leijin Liu, Siqi Li, Shuang Wang, Xinyang Huang, Jiaxin Yang, Jiayu Song, Yue Zhang, Yongxiang Huang, Qiaohui Fan

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Genetic diversity insights from population genomics and machine learning tools for Nordic Arctic charr (Salvelinus alpinus) populations by Christos Palaiokostas, Khrystyna Kurta, Fotis Pappas, Henrik Jeuthe, Ørjan Hagen, José Beirão, Matti Janhunen, Antti Kause

“…In addition, unsupervised machine learning models such as K-means, Gaussian and Bayesian Gaussian mixtures were assessed for their ability to detect genetic clusters. …”
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Anomaly Detection Utilizing One-Class Classification—A Machine Learning Approach for the Analysis of Plant Fast Fluorescence Kinetics by Nam Trung Tran

“…This study proposes a Machine Learning-based approach using a One-Class Support Vector Machine anomaly detection model to effectively categorize OJIP measurements into “normal”, representing healthy plants, and “anomalies”. …”
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Near-Infrared Spectroscopy Machine-Learning Spectral Analysis Tool for Blueberries (<i>Vaccinium corymbosum</i>) Cultivar Discrimination by Pedro Ribeiro, Maria Inês Barbosa, Clara Sousa, Pedro Miguel Rodrigues

“…Spectra were acquired from fresh blueberry leaves collected from two geographic regions and across three seasons. Machine-learning-based models, selected from a pool of 10 classifiers based on their discrimination power under a twofold stratified cross-validation process, were trained/tested with 1 to 20 components obtained by the application of data dimensionality reduction (DDR) techniques (dictionary learning, factor analysis, fast individual component analysis, and principal component analysis) to different near-infrared (NIR) spectra regions’ data, to either analyze a single spectral region and season or combine spectral regions and/or seasons for each side of the blueberry leaf. …”
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Optimized SVR model for predicting dissolved oxygen levels using wavelet denoising and variable reduction: Taking the Minjiang River estuary as an example by Peng Zhang, Xinyang Liu, Huiru Zhang, Chengchun Shi, Gangfu Song, Lei Tang, Ruihua Li

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Buried No longer: recent computational advances in explicit interfacial modeling of lithium-based all-solid-state battery materials by Julia H. Yang, Xinqiang Rao, Amanda Whai Shin Ooi

“…Lastly, we highlight universal machine learning potentials, challenging datasets, and opportunities for tighter integration with experiments, all of which broaden the scope of modeling. …”
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Designing an explainable bio-inspired model for suspended sediment load estimation: eXtreme Gradient Boosting coupled with Marine Predators Algorithm by Roozbeh Moazenzadeh, Okan Mert Katipoğlu, Ahmadreza Shateri, Hamid Nasiri, Mohammed Abdallah

“…While Xtreme Gradient Boosting (XGB), a powerful ensemble machine learning (ML) model, has been employed in previous studies, the novelty of this research lies in the introduction of a hybrid approach that synergistically combines XGB with the bio-inspired Marine Predators Algorithm (XGB-MPA) to estimate SL in the Yeşilirmak River (Turkey). …”
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Estimation of microbial load in Ganoderma lucidum using a solar-electric hybrid dryer enhanced by machine learning and IoT by Pinit Nuangpirom, Siwasit Pitjamit, Weerin Pheerathamrongrat, Wasawat Nakkiew, Parida Jewpanya

“…The results indicated that higher temperatures, particularly 80 °C, were most effective in reducing microbial counts, achieving near-zero levels after 240 to 480 min. Machine learning (ML) models random forest regression (RFR), decision tree regression (DTR), and multiple linear regression (MLR) were trained to estimate microbial levels based on input variables such as time, temperature, and weight. …”
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Comparison of simulating visibility using XGBoost and IMPROVE method: a case study in East China by Xin Zhang, Yue Wang, Zibo Zhuang, Yuxi Liu, Chengduo Yuan, Lei Su, Jingyuan Shao, Pak-Wai Chan

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Comprehensive multi-omics integration uncovers mitochondrial gene signatures for prognosis and personalized therapy in lung adenocarcinoma by Wenjia Zhang, Lei Zhao, Tiansheng Zheng, Lihong Fan, Kai Wang, Guoshu Li

“…By leveraging an ensemble of machine learning algorithms, we developed an Artificial Intelligence-Derived Prognostic Signature (AIDPS) model based on mitochondrial-related genes and validated its prognostic accuracy across multiple independent datasets. …”
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Development of a novel deep learning method that transforms tabular input variables into images for the prediction of SLD by Gabriel Cubillos, Javier Perez-Valenzuela, Herman Aguirre, Luz Martínez, Lorena Castro, Gabriel Mezzano, Claudio A. Perez

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Simulation and explanatory analysis of dissolved oxygen dynamics in Lake Ulansuhai, China by Fan Zhang, Xiaohong Shi, Shengnan Zhao, Ruonan Hao, Biao Sun, Guohua Li, Shihuan Wang, Hao Zhang

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