Web-based graphical user interface for automated materials feature engineering for machine learning by Yoshiki Hasukawa, Mikael Kuwahara, Fernando Garcia-Escobar, Lauren Takahashi, Toshiaki Taniike, Keisuke Takahashi

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Deep learning-assisted attribute prediction of chalcogenide glasses based on graph classification by Hui Li, Pan Liu, Shaoyun Liu, Bin Yang

“…With the rapid development of artificial intelligence technologies in the field of materials science, researchers have increasingly incorporated machine learning (ML) methods to accelerate the exploration of composition–structure–property relationships in chalcogenide glasses. …”
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Machine learning-based prediction of speed of sound in fatty acid ethyl esters by Kusum Yadav, Shahad Almansour, Lulwah M. Alkwai, Anupam Yadav, Mehrdad Mottaghi

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A generalized machine learning framework to estimate fatigue life across materials with minimal data by Dharun Vadugappatty Srinivasan, Morteza Moradi, Panagiotis Komninos, Dimitrios Zarouchas, Anastasios P. Vassilopoulos

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Soil organic carbon estimation using spaceborne hyperspectral composites on a large scale by Xiangyu Zhao, Zhitong Xiong, Paul Karlshöfer, Nikolaos Tziolas, Martin Wiesmeier, Uta Heiden, Xiao Xiang Zhu

“…Besides hyperspectral input, the digital elevation model (DEM) was also included as an auxiliary input as the measured spectrum has inter-variability dependent on the elevation and the generated topographical features are also relevant with SOC distribution. Based on the regression results evaluated by RMSE, R2, and RPIQ, the deep learning models showed much better performance than machine learning methods. …”
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Deep-learning-based detection of underwater fluids in multiple multibeam echosounder data by Tyméa Perret, Gilles Le Chenadec, Arnaud Gaillot, Yoann Ladroit, Stéphanie Dupré

“…Additionally, we thoroughly analyzed the composition of the training dataset and evaluated the detection performance based on various training configurations. …”
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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

“…Assessing sow posture is essential for understanding their physiological condition and helping farmers improve herd productivity. Deep learning-based techniques have proven effective for image interpretation, offering a better alternative to traditional image processing methods. …”
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Prediction of Propellant Electrostatic Sensitivity Based on Small-Sample Machine Learning Models by Fei Wang, Kai Cui, Jinxiang Liu, Wenhai He, Qiuyu Zhang, Weihai Zhang, Tianshuai Wang

“…Hydroxyl-terminated-polybutadiene (HTPB)-based composite solid propellants are extensively used in aerospace and defense applications due to their high energy density, thermal stability, and processability. …”
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Identification of varietal and geographical origin of wines using artificial intelligence methods by Alexan Khalafyan, Zaual Temerdashev, Aleksey Abakumov, Evgeniy Gipich

“…The purpose of this study was to build models for identifying the varietal and geographical origin of wines using machine learning methods based on the mineral composition of 426 wine samples. …”
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PathoGraph: A Graph-Based Method for Standardized Representation of Pathology Knowledge by Peiliang Lou, Yuxin Dong, Caixia Ding, Chunbao Wang, Ruifeng Guo, XiaoBo Pang, Chen Wang, Chen Li

“…To systematically organize pathology knowledge for its computational use, we propose PathoGraph, a knowledge representation method that describes pathology knowledge in a graph-based format. …”
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Spatial Quality Control Method for Surface Temperature Observations Based on Multiple Elements by Xiaoling Ye, Xing Yang, Xiong Xiong, Shuai Yang, Yang Chen

“…Therefore, a Random Forest quality control algorithm based on the Principal Component Analysis (PCA-RF) is proposed in this paper. …”
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Algorithm for Cloud Particle Phase Identification Based on Bayesian Random Forest Method by Fu Tao, Yang Zhipeng, Tao Fa, Hu Shuzhen, Lu Yuxiang, Fu Changqing

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A Novel THz Differential Spectral Clustering Recognition Method Based on t-SNE by Tie-Jun Li, Chih-Cheng Chen, Jian-jun Liu, Gui-fang Shao, Christopher Chun Ki Chan

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Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms by Abdul Aabid, Md Abdul Raheman, Meftah Hrairi, Muneer Baig

“…This study is particularly important for designing the single-sided composite patch repair method based on analytical modelling. …”
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Exploration of Fully‐Automated Body Composition Analysis Using Routine CT‐Staging of Lung Cancer Patients for Survival Prognosis by Marc‐David Künnemann, Christian Römer, Anne Helfen, Annalen Bleckmann, Marcel Kemper, Walter Heindel, Tobias J. Brix, Michael Forsting, Johannes Haubold, Marcel Opitz, Martin Schuler, Felix Nensa, Katarzyna Borys, René Hosch

“…Kaplan–Meier survival analysis, Cox proportional hazards modelling and machine learning‐based survival prediction were performed. …”
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Time series analysis and prediction of the trends of COVID-19 epidemic in Singapore based on machine learning by Wenbin Yang, Xin Chang

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Optimising team dynamics: The role of AI in enhancing challenge-based learning participation experience and outcomes by Athina Georgara, Marc Santolini, Olga Kokshagina, Camila Justine Jacinta Haux, Desmé Jacobs, Gloria Biwott, Marcela Correa, Carles Sierra, Jose Luis Fernandez-Marquez, Juan A. Rodriguez-Aguilar

“…Often called Challenge-Based Learning (CBL), this educational approach emphasises developing collaborative and problem-solving skills, with significant learning occurring within team settings. …”
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Intelligent multi-modeling reveals biological relationships and adaptive phenotypes for dairy cow adaptation to climate change by Robson Mateus Freitas Silveira, Angela Maria de Vasconcelos, Concepta McManus, Luiz Paulo Fávero, Iran José Oliveira da Silva

“…Applying the random forest method to classify predictions of adaptive responses based on climatic variables showed that all thermoregulatory, hormonal, biochemical, and hematological responses are important, except for urea and T₃ concentrations, which had negative importance values. …”
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Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning by Longfei Zhu, Qun Luo, Qiaochuan Chen, Yu Zhang, Lijun Zhang, Bin Hu, Yuexing Han, Qian Li

“…However, complex interactions make this area of research challenging. Based on the image processing and machine learning techniques, this paper proposes a multimodal fusion learning framework that comprehensively considers both composition and microstructure in prediction of the ultimate tensile strength (UTS) of Al‐Si alloys. …”
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Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning by Ranran Kou, Cong Wang, Jinxia Liu, Ran Wan, Zhe Jin, Le Zhao, Youjie Liu, Junwei Guo, Feng Li, Hongbo Wang, Song Yang, Cong Nie

“…However, the acquisition of chemical components relies on traditional instrumental analytical methods. As a result, the acquisition of chemical composition data is time-consuming and labor-intensive, involving only a limited number of compounds. …”
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