Shortcutting from self-motion signals reveals a cognitive map in mice by Jiayun Xu, Mauricio Girardi-Schappo, Jean-Claude Beique, André Longtin, Leonard Maler

“…The TEV required learning both the direction and distance of the start to food vector, and our data suggests that different learning dynamics underlie these estimates. …”
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Cross-Attention U-Net for Elastic Wavefield Decomposition in Anisotropic Media by Youngjae Shin

“…This study proposes a deep learning-based approach using a cross-attention U-Net architecture to achieve efficient vector decomposition of elastic wavefields. …”
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RGB imaging-based detection of rice leaf blast spot and resistance evaluation at the canopy scale by XIE Pengyao, FU Haowei, TANG Zheng, MA Zhihong, CEN Haiyan

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Artificial Intelligence for data modeling in triboelectric nanogenerators by Chenjia Li, Ali Matin Nazar

“…Application cases discussed include AI-powered triboelectric smart socks achieving 96.67 % activity recognition accuracy, soft robotic grippers with 98.1 % object identification precision, and wearable pulse sensors for continuous blood pressure monitoring using personalized machine learning algorithms. Quantitative analyses of machine learning frameworks are presented, with CNNs and ANNs demonstrating up to 99.32 % accuracy in TENG signal processing tasks. …”
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Social Determinants Influencing Internet‐Based Service Adoption Among Female Family Caregivers in Bangladesh: A Sociodemographic and Technological Analysis by Mohammad Ishtiaque Rahman, Jahangir Alam, Khadija Khanom, Forhan Bin Emdad

“…Data analysis employed advanced machine learning models, including Random Forest, Logistic Regression, Gradient Boosting, and Support Vector Regression. …”
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Nomogram combining dual-energy computed tomography features and radiomics for differentiating parotid warthin tumor from pleomorphic adenoma: a retrospective study by Zhiwei Gong, Jianying Li, Yilin Han, Shiyu Chen, Lijun Wang

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Construction and Application of Feature Recommendation Model for Remote Sensing Interpretation of Rock Strata Based on Knowledge Graph by Liufeng Tao, Qirui Wu, Miao Tian, Zhong Xie, Jianguo Chen, Yueyu Wu, Qinjun Qiu

“…A knowledge graph constructed using the TransE algorithm consists of 766 entity nodes and 1008 relationships, enabling a quantitative evaluation of feature correlations based on semantic similarity. …”
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Artificial intelligence in environmental research: bibliometric, text mining and content analysis by Chukwuebuka C. Okafor, Festus A. Otunomo, Valentine E. Nnadi, Chinelo A. Nzekwe, Adaobi V. Nwoye, Charles C. Ajaero

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Geographic origin discrimination and quantification of phenolic compounds and moisture in Artemisia argyi folium using NIRS and chemometrics by Lifei Hu, Yifan Wang, Xin Wu, Yuanyuan Shan, Fengxiao Zhu, Fan Zhang, Qiang Yang, Mingxing Liu

“…Spectral preprocessing methods (Savitzky-Golay smoothing, normalization, standard normal variate, and multiplicative scatter correction) enhanced machine learning performance, with support vector machine (SVM), radial basis function (RBF), and convolutional neural network (CNN) models achieving scores of 1.0000 across performance metrics, indicating strong generalization and robustness. …”
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Target Recognition Method Based on Graph Structure Perception of Invariant Features for SAR Images by Jingyi CAO, Yang ZHANG, Ya’nan YOU, Yamin WANG, Feng YANG, Weijia REN, Jun LIU

“…Outputs are analyzed with a graph neural network and constructed into a topological representation of essential features, resulting in a target category vector. The t-Distributed Stochastic Neighbor Embedding (t-SNE) method is used to qualitatively evaluate the algorithm’s classification ability, while metrics like accuracy, recall, and F1 score are used to quantitatively analyze key units and overall network performance. …”
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A Composite Network for CS ISAR Integrating Deep Adaptive Sampling and Imaging by Lianzi Wang, Ling Wang, Miguel Heredia Conde, DaiYin Zhu

“…The CS ISAR imaging with adaptive sampling consists of sampling and reconstruction networks, where the sampling network compresses the radar data by a convolutional neural network, and the reconstruction network mainly performs the image reconstruction by convolutional dictionary learning. In addition, we adopt the block-based CS method in the sampling network to alleviate the computational burden caused by vectorizing and stacking the data and introduce a nonlocal self-similarity model into the reconstruction network to improve the imaging quality. …”
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A new integrated neurosymbolic approach for crop-yield prediction using environmental data and satellite imagery at field scale by Khadija Meghraoui, Teeradaj Racharak, Kenza Ait El Kadi, Saloua Bensiali, Imane Sebari

“…This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods, specifically representation learning techniques, along with predictions derived from remote sensing imagery. …”
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A Data-Driven Framework for Accelerated Modeling of Stacking Fault Energy from Density of States Spectra by Md Tohidul Islam, Scott R. Broderick

“…Using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP), we identified key features from high-resolution vector representation of DOS data and explored their correlation with SFE. …”
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Implementation of a Conditional Latent Diffusion-Based Generative Model to Synthetically Create Unlabeled Histopathological Images by Mahfujul Islam Rumman, Naoaki Ono, Kenoki Ohuchida, Ahmad Kamal Nasution, Muhammad Alqaaf, Md. Altaf-Ul-Amin, Shigehiko Kanaya

“…These models utilize deep learning techniques to learn complex data distributions and generate novel images that closely resemble the training dataset. …”
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Multi-modal MRI for objective diagnosis and outcome prediction in depression by Jesper Pilmeyer, Rolf Lamerichs, Sjir Schielen, Faroeq Ramsaransing, Vivianne van Kranen-Mastenbroek, Jacobus F.A. Jansen, Marcel Breeuwer, Svitlana Zinger

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Diagnosis of non-puerperal mastitis based on “whole tongue” features: non-invasive biomarker mining and diagnostic model construction by Siyuan Tu, Yulian Yin, Lina Ma, Hongfeng Chen, Meina Ye

“…Features of tongue images (detection, segmentation, and classification) were quantitated and extracted via deep learning. The microbiota composition was assessed using 16S rRNA gene sequencing (V3–V4 region) and bioinformatic pipelines (QIIME2, DADA2). …”
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Resolution enhancement of scanning electron micrographs using artificial intelligence by T. Reclik, S. Medghalchi, P. Schumacher, M.A. Wollenweber, T. Al-Samman, S. Korte-Kerzel, U. Kerzel

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Region-wise landmarks-based feature extraction employing SIFT, SURF, and ORB feature descriptors to recognize Monozygotic twins from 2D/3D Facial Images [version 2; peer review: 2... by Srikanth Prabhu, Vinod Nayak, Aparna Jayakala, Krishna Prakasha K, Gangothri Sanil

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Improved identification and tracking of three-dimensional eddies in the Southern Ocean utilizing 3D-U-Res-Net by Chang Liu, Xiayan Lin, Xiayan Lin, Guangjun Xu, Guangjun Xu, Guoqing Han, Yu Liu

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Evolution Pattern of Flash Drought in Hanjiang River Basin and Its Response to Middle Route of South-to-North Water Transfer Project by HUANG Kai, DUAN Kai

“…Moreover, three machine learning methods, partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF), were employed to establish regression models for the duration of flash droughts, and the impact of the Middle Route of the South-to-North Water Transfer Project on the duration of flash droughts was quantitatively analyzed. …”
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