Research on the generation and annotation method of thin section images of tight oil reservoir based on deep learning by Tao Liu, Zongbao Liu, Kejia Zhang, Chunsheng Li, Yan Zhang, Zihao Mu, Mengning Mu, Mengting Xu, Yue Zhang, Xue Li

“…However, the difficulty of making cast thin sections, the complexity of the making process and the high cost of thin section annotation have led to a lack of cast thin section images, which cannot meet the training requirements of deep learning image recognition models. In order to increase the sample size and improve the training effect of deep learning model, we proposed a generation and annotation method of thin section images of tight oil reservoir based on deep learning, by taking Fuyu reservoir in Sanzhao Sag as the target area. …”
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The Impact of Radiation Dose on CT‐Based Body Composition Analysis: A Large‐Animal Study by Luca Salhöfer, Gregor Jost, Mathias Meetschen, Daniel vanLandeghem, Michael Forsting, Denise Bos, Christian Bojahr, Rene Hosch, Felix Nensa, Hubertus Pietsch, Johannes Haubold

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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

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MCCA-VNet: A Vit-Based Deep Learning Approach for Micro-Expression Recognition Based on Facial Coding by Dehao Zhang, Tao Zhang, Haijiang Sun, Yanhui Tang, Qiaoyuan Liu

“…In the past few years, deep learning methods based on optical flow and Transformer have achieved excellent results in this field, but most of the current algorithms are mainly concentrated on establishing a serialized token through the self-attention model, and they do not take into account the spatial relationship between facial landmarks. …”
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Machine learning prediction of metabolic dysfunction-associated fatty liver disease risk in American adults using body composition: explainable analysis based on SHapley Additive e... by Yan Hong, Xinrong Chen, Ling Wang, Fan Zhang, ZiYing Zeng, Weining Xie

“…This study aimed to develop and validate machine learning (ML) models for predicting MAFLD using detailed body composition metrics and to explore the relative contributions of adipose tissue features through explainable ML techniques.MethodsData from the 2017–2018 National Health and Nutrition Examination Survey (NHANES) were used to construct predictive models based on anthropometric, demographic, lifestyle, and clinical variables. …”
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Deep Learning Autoencoders for Fast Fourier Transform-Based Clustering and Temporal Damage Evolution in Acoustic Emission Data from Composite Materials by Serafeim Moustakidis, Konstantinos Stergiou, Matthew Gee, Sanaz Roshanmanesh, Farzad Hayati, Patrik Karlsson, Mayorkinos Papaelias

“…Temporal damage evolution is modeled using Markov chain analysis to provide insights into how damage progresses over time. The proposed method achieves a reconstruction error of 0.0017 and a high R-squared value of 0.95, indicating the autoencoder’s effectiveness in learning compact representations while minimizing information loss. …”
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A novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests. by Yuqi Cheng, Dawei Wu, Ying Wu, Youcai Guo, Xinze Cui, Pengquan Zhang, Jie Gao, Yanming Fu, Xin Wang

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An Effective Temporal Convolutional Networks-Based Method for Detecting Android Malware Using Dynamic Extracted Features by Abdurraheem Joomye, Mee Hong Ling, Muhammed Basheer Jasser, Athirah Mohd Ramly, Kok-Lim Alvin Yau

“…Therefore, it is necessary to continue proposing and investigating new methods for Android malware detection, including machine learning (ML) and deep learning (DL) based ones. …”
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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

“…A comparative analysis of author keywords, index keywords, sources, authors, and countries confirmed strong thematic consistency between databases, thereby enhancing the credibility of the cluster-based bibliometric analysis. The PRISMA framework was used to ensure the structured literature search, eligibility assessment, and documentation of the selection process. 185 peer-reviewed articles (2015–2025) were analyzed and organized into seven refined thematic clusters: ‘mechanical behavior modeling using neural networks’, ‘AI-driven alloy design and compositional optimization’, ‘atomic-scale modeling and physics-guided learning’, ‘AI applications in welding and thermomechanical processing’, ‘biomaterials and microstructural optimization’, ‘corrosion modeling and degradation prediction’, ‘data-driven design and integrated optimization frameworks’. …”
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UAV Spiral Maneuvering Trajectory Intelligent Generation Method Based on Virtual Trajectory by Tao Chen, Shaopeng Li, Yong Xian, Leliang Ren, Zhenyu Liu

“…Ultimately, the relative spiral motion is superimposed onto the generated virtual trajectory to synthesize a composite spiral maneuvering trajectory. The simulation results demonstrate that the proposed method achieves expansive spiral maneuvering ranges while ensuring precise target strikes.…”
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Structural Health Monitoring of Laminated Composites Using Lightweight Transfer Learning by Muhammad Muzammil Azad, Izaz Raouf, Muhammad Sohail, Heung Soo Kim

“…Therefore, deep learning-based methods that use sensor data to conduct autonomous health monitoring have drawn much interest in structural health monitoring (SHM). …”
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Physics-constrained machine learning for reduced composition space chemical kinetics by Anuj Kumar, Tarek Echekki

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A Deep Learning Framework for Damage Assessment of Composite Sandwich Structures by Viviana Meruane, Diego Aichele, Rafael Ruiz, Enrique López Droguett

“…This article presents a damage-assessment algorithm for composite sandwich structures that uses full-field vibration mode shapes and deep learning. …”
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Machine learning enhanced ultra-high vacuum system for predicting field emission performance in graphene reinforced aluminium based metal matrix composites by Sunil Kumar Pradhan, Subhayu Kabiraj, Shivin Kumar Gupta, Abhishek Singh, Padmakar G. Chavan, Shubham S. Patil, Trilok Nath Pandey

“…In stage 2, the top models were further refined using advanced techniques, including Gradient-Based Methods and Ensemble Methods. Among the studied compositions, AlGr 2, containing 2 wt% graphene, exhibits the lowest turn-on electric field, whereas other compositions, including 1.25, 1.5, and 1.75 wt%, show comparatively higher values. …”
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Springback Prediction of Sandwich Panel Using Machine Learning Methods by Raja Ouled Ahmed Ben Ali, Sami Chatti

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Advanced computational tools, artificial intelligence and machine-learning approaches in gut microbiota and biomarker identification by Tikam Chand Dakal, Caiming Xu, Caiming Xu, Abhishek Kumar, Abhishek Kumar

“…This review hunts through the cutting-edge computational methodologies that integrate multi-omics data—such as metagenomics, metaproteomics, and metabolomics—providing a comprehensive understanding of the gut microbiome's composition and function. Additionally, machine learning (ML) approaches, including deep learning and network-based methods, are explored for their ability to uncover complex patterns within microbiome data, offering unprecedented insights into microbial interactions and their link to host health. …”
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Comprehensive Quantitative Analysis of Coal-Based Liquids by Mask R-CNN-Assisted Two-Dimensional Gas Chromatography by Huan-Huan Fan, Xiang-Ling Wang, Jie Feng, Wen-Ying Li

“…Therefore, the development of a method for accurately and rapidly analyzing chromatograms is crucial for understanding the chemical compositions and structures of coal-based liquids, such as direct coal liquefaction (DCL) oils and coal tar. …”
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Using Convolution Neural Network methods for the ultrasound characterization of porosity across carbon fiber reinforced polymer layers by Mihai-Mircea Vasilache, Iryna Tretiak, Rostand B. Tayong, Vladan Velisavljevic

“…Traditional non-destructive testing (NDT) methods face some challenges in accurately detecting and characterizing porosity. …”
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Prediction of Rheological Parameters of Polymers by Machine Learning Methods by T. N. Kondratieva, A. S. Chepurnenko

“…Machine learning methods open up great opportunities in predicting the rheological parameters of polymers. …”
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Preparation of land subsidence susceptibility map using machine learning methods based on decision tree (case study: Isfahan–Borkhar) by Negar Ghasemi, Iman Khosravi, Ali Bahrami

“…Using remote sensing techniques, particularly radar interferometry (InSAR), this study investigates subsidence rates over the 2019–2023 period. Advanced machine learning methods, namely Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) are employed to develop a susceptibility map divided into five probability classes: very high, high, medium, low, and very low. …”
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