AI-based estimation of forest plant community composition from UAV imagery by Lindo Nepi, Giacomo Quattrini, Simone Pesaresi, Adriano Mancini, Roberto Pierdicca

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Composition pattern-aware web service recommendation based on depth factorisation machine by Bing Tang, Mingdong Tang, Yanmin Xia, Meng-Yen Hsieh

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YOLO-DAFS: A Composite-Enhanced Underwater Object Detection Algorithm by Shengfu Luo, Chao Dong, Guixin Dong, Rongmin Chen, Bing Zheng, Ming Xiang, Peng Zhang, Zhanwei Li

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Complete Object-Compositional Neural Implicit Surfaces With 3D Pseudo Supervision by Wongyeom Kim, Jisun Park, Kyungeun Cho

“…Neural implicit surface reconstruction has recently emerged as a prominent paradigm in multi-view 3D reconstruction using deep learning. In contrast to traditional multi-view stereo methods, signed distance function (SDF)-based approaches leverage neural networks to effectively represent 3D scenes. …”
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Machine learning for discrimination of phase‐change chalcogenide glasses by Qundao Xu, Meng Xu, Siqi Tang, Shaojie Yuan, Ming Xu, Wei Zhang, Xian‐Bin Li, Zhongrui Wang, Xiangshui Miao, Chengliang Wang, Matthias Wuttig

“…Particularly in electronic phase‐change memory applications, distinguishing these glasses from neighboring compositions that do not possess memory capabilities is inherently difficult when employing traditional analytical methods. …”
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Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms by Huifeng Ning, Faqiang Chen, Yunfeng Su, Hongbin Li, Hengzhong Fan, Junjie Song, Yongsheng Zhang, Litian Hu

“…Results demonstrated that ML models could satisfactorily predict friction coefficient and wear rate from the material properties and testing method variables data. Herein, the LSBoost model based on the integrated learning algorithm presented the best prediction performance for friction coefficients and wear rates, with R 2 of 0.9219 and 0.9243, respectively. …”
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Research on predicting the thermocompression deformation behavior of Mg–Li matrix composite using machine learning and traditional techniques by Dandan Li, Xiaoyu Hou, Yangfan Liu, Linhao Gu, Jinhui Wang, Jiaxuan Ma, Xiaoqiang Li, Zhi Jia, Qichi Le, Dexue Liu, Xincheng Yin

“…Then, the thermal compression flow behavior of the as-cast composite was comparatively researched using a traditional Arrhenius model and advanced machine learning methods (Linear Regression, AdaBoost, Random Forest, and XGBoost). …”
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Harnessing machine learning approach for hardness optimization of Al-Si alloy composites reinforced with coconut shell ash by M Poornesh, Shreeranga Bhat, Mithun Kanchan

“…The findings have significant implications for industries such as automotive, aerospace, and defense, where lightweight, high-strength materials are critical. The ML-based approach used in this study can reduce the need for extensive experimental testing, offering a practical and efficient method for optimizing composite materials. …”
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Machine Learning‐Accelerated Reconstruction of Periodic Nanostructures with X‐ray Fluorescence Spectroscopy Methods by Vinh‐Binh Truong, Analía Fernández Herrero, Kas Andrle, Victor Soltwisch, Philipp Hönicke

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A deep learning strategy for accurate identification of purebred and hybrid pigs across SNP chips by Zipeng Zhang, Zhengwen Fang, Yongwang Du, Yilin He, Changsong Qian, Weijian Ye, Ning Zhang, Jianan Zhang, Xiangdong Ding

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Absorbent material composition prediction based on multi-objective regression with value stacking and selection by Shi He, Jiaying Chen, Kai Huang, Jian Mao, Kexun Li, Taikang Liu

“…IntroductionElectromagnetic wave absorption materials reduce incoming wave energy, with machine learning focusing on data-driven design methods. Traditional multi-objective regression methods often fail to provide accurate component predictions, limiting their performance.MethodWe propose a multi-objective predictive model for absorbent compositions. …”
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Data Reconciliation-Based Hierarchical Fusion of Machine Learning Models by Pál Péter Hanzelik, Alex Kummer, János Abonyi

“…The third method is based on directly fine-tuning the machine learning predictions based on the prediction errors of each model. …”
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A Comparative Analysis of Buckling Pressure Prediction in Composite Cylindrical Shells Under External Loads Using Machine Learning by Hyung Gi Lee, Jung Min Sohn

“…This study addressed this challenge by integrating advanced machine learning techniques with simulation-based data generation through finite element analysis (FEA). …”
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Comparison of cardiorespiratory endurance, body mass index, and learning achievement of students Junior High Schools by Keysha Azhalia Wahono, Oce Wiriawan, Taufiq Hidayat, Sapto Wibowo, Heryanto Nur Muhammad, Mochamad Ridwan

“…Conclusion: The better the BMI category, physical education learning outcomes will turn out. Meanwhile, only physical education learning outcomes who influenced based on students’ school also this research that schools and teachers should promote healthy lifestyles and encourage students to always be active in supporting student achievement.…”
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Deep Learning-Based Identification of Echocardiographic Abnormalities From Electrocardiograms by Goro Fujiki, MD, Satoshi Kodera, MD, PhD, Naoto Setoguchi, MD, Kengo Tanabe, MD, PhD, Kotaro Miyaji, MD, PhD, Shunichi Kushida, MD, PhD, Mike Saji, MD, PhD, Mamoru Nanasato, MD, PhD, Hisataka Maki, MD, PhD, Hideo Fujita, MD, PhD, Nahoko Kato, MD, PhD, Hiroyuki Watanabe, MD, PhD, Minami Suzuki, MD, Masao Takahashi, MD, PhD, Naoko Sawada, MD, PhD, Jiro Ando, MD, Masataka Sato, MD, Shinnosuke Sawano, MD, PhD, Susumu Katsushika, MD, PhD, Hiroki Shinohara, MD, PhD, Norifumi Takeda, MD, PhD, Katsuhito Fujiu, MD, PhD, Hiroshi Akazawa, MD, PhD, Hiroyuki Morita, MD, PhD, Issei Komuro, MD, PhD

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Research on Mount Wilson Magnetic Classification Based on Deep Learning by Yuanbo He, Yunfei Yang, Xianyong Bai, Song Feng, Bo Liang, Wei Dai

“…In this paper, we adopt a deep learning method, CornerNet-Saccade, to perform the Mount Wilson magnetic classification of sunspot groups. …”
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Classification and spectrum optimization method of grease based on infrared spectrum by Xin Feng, Yanqiu Xia, Peiyuan Xie, Xiaohe Li

“…The model achieved recognition accuracy of 100.00%, 96.08%, 94.87%, 100.00%, and 87.50% for polyurea grease, calcium sulfonate composite grease, aluminum (Al)-based grease, bentonite grease, and lithium-based grease, respectively. …”
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Robust biochar yield and composition prediction via uncertainty-aware ResNet-based autoencoder by Yali Zhang, Bowen Lei, Amirhossein Mahdaviarab, Xiao Wang, Zong Liu

“…This paper presents a ResNet-based autoencoder model that utilizes biomass properties and pyrolysis conditions to more accurately and robustly predict biochar yield and composition. …”
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Cloud service composition optimization based on service association impact and improved NSGA-II algorithm by Chong Zhang, Longge Wang, Ketai He

“…Extensive experiments on both benchmark problems and cloud service composition scenarios demonstrate that the proposed algorithm outperforms conventional multi-objective optimization methods in terms of convergence, diversity, and robustness. …”
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Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium by Seungbaek Lee, Riikka K. Arffman, Elina K. Komsi, Outi Lindgren, Janette Kemppainen, Keiu Kask, Merli Saare, Andres Salumets, Terhi T. Piltonen

“…Methods: We utilized a deep-learning artificial intelligence (AI) model, created on a cloud-based platform and developed in our previous study. …”
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