A Lightweight Single-Image Super-Resolution Method Based on the Parallel Connection of Convolution and Swin Transformer Blocks by Tengyun Jing, Cuiyin Liu, Yuanshuai Chen

“…To address these problems and better leverage both local and global information, this paper proposes a super-resolution reconstruction network based on the Parallel Connection of Convolution and Swin Transformer Block (PCCSTB) to model the local and global features of an image. …”
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Cross-Modal Collaboration and Robust Feature Classifier for Open-Vocabulary 3D Object Detection by Hengsong Liu, Tongle Duan

“…However, traditional 3D detection models are limited to recognizing predefined categories and struggle with unknown or novel objects. …”
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Geometric Positioning Verification of Spaceborne Photon-Counting Lidar Data Based on Terrain Feature Identification by Cheng Wu, Qifan Yu, Shaoning Li, Anmin Fu, Mengguang Liao, Lelin Li

“…To improve data quality for enhanced performance in scientific applications, this study proposes a photon correction method based on terrain feature identification, specifically for the photon-counting spaceborne lidar. …”
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African water body segmentation with cross-layer information separability based feature decoupling transformer by Binghao Liu, Qi Zhao, Chunlei Wang, Meng Li, Hongbo Xie, Lijiang Chen

“…Third, we design an asymmetric cross-layer input-dependent Feature Decoupling Transformer (FDTran), to extract water features from information mixed high-level features, improving water segmentation performance. …”
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Radar-Based Activity Recognition in Strictly Privacy-Sensitive Settings Through Deep Feature Learning by Giovanni Diraco, Gabriele Rescio, Alessandro Leone

“…Deep learning models based on pre-trained feature extractors combined with bidirectional long short-term memory networks were employed for classification. …”
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Enhancing agricultural data interpretability and visualization with TabNet-driven feature extraction and Local Biplots by J. Triana-Martinez, A. Álvarez-Meza, G. Castellanos-Dominguez

“…Quantitative results demonstrate that our framework achieved an R2=0.79±0.01 for LAI and delivered more consistent performance for breeder scores (R2=0.77±0.01) relative to standard machine learning models. …”
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Sleep features and the risk of type 2 diabetes mellitus: a systematic review and meta-analysis by Hongyi Liu, Hui Zhu, Qinkang Lu, Wen Ye, Tao Huang, Yuqiong Li, Bingqi Li, Yingxin Wu, Penghao Wang, Tao Chen, Jin Xu, Lindan Ji

“…If I2 < 50%, a combined analysis was performed based on a fixed-effects model, and vice versa, using a random-effects model.Results Our analysis revealed that a nighttime sleep duration of less than 7 h (odds ratio [OR] = 1.18; 95% CI = 1.13, 1.23) or more than 8 h (OR = 1.13; 95% CI = 1.09, 1.18) significantly increased the risk of T2DM. …”
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Joint feature representation optimization and anti-occlusion for robust multi-vessel tracking in inland waterways by Shenjie Zou, Jin Liu, Xiliang Zhang, Zhongdai Wu, Jing Liu, Bing Han

“…Moreover, traditional models encounter difficulties in accurately capturing the global appearance features of the vessels in images, which leads to a decline in vessel detection performance. …”
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Credit card fraud Detection using Feature select method and improved machine learning algorithm by Mohammed AL-Hammadi

“…The SVM classification model then performs the final classification, with its hyperparameters optimized through the particle swarm optimization (PSO) technique. …”
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Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis by Daniel Nasef, Demarcus Nasef, Viola Sawiris, Peter Girgis, Milan Toma

“…These models are trained on two open-source datasets, using the PyCaret library in Python. (3) <b>Results</b>: The findings suggest that an ensemble of Random Forest and Logistic Regression models performs best for the 2C classification, while the Extra Trees classifier performs best for the 3C classification. …”
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A Dual-Feature Framework for Enhanced Diagnosis of Myeloproliferative Neoplasm Subtypes Using Artificial Intelligence by Amna Bamaqa, N. S. Labeeb, Eman M. El-Gendy, Hani M. Ibrahim, Mohamed Farsi, Hossam Magdy Balaha, Mahmoud Badawy, Mostafa A. Elhosseini

“…The extracted features were used to train machine learning models, with hyperparameter optimization performed using Optuna. …”
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EKNet: Graph Structure Feature Extraction and Registration for Collaborative 3D Reconstruction in Architectural Scenes by Changyu Qian, Hanqiang Deng, Xiangrong Ni, Dong Wang, Bangqi Wei, Hao Chen, Jian Huang

“…Next, we construct a lightweight graph neural network, named EKNet, to enhance feature representation capabilities, enabling improved performance in low-overlap registration scenarios. …”
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Who is WithMe? EEG features for attention in a visual task, with auditory and rhythmic support by Renata Turkeš, Steven Mortier, Jorg De Winne, Jorg De Winne, Dick Botteldooren, Paul Devos, Steven Latré, Tim Verdonck

“…The performance of the different EEG representations is evaluated with the Support Vector Machine (SVM) accuracy on the WithMe data derived from a modified digit span experiment, and is benchmarked against baseline EEG-specific models, including a deep learning architecture known for effectively learning task-specific features.ResultsThe raw EEG time series outperform each of the considered data representations, but can fall short in comparison with the black-box deep learning approach that learns the best features.DiscussionThe findings are limited to the WithMe experimental paradigm, highlighting the need for further studies on diverse tasks to provide a more comprehensive understanding of their utility in the analysis of EEG data.…”
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ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering by Maryam Shahabikargar, Amin Beheshti, Wathiq Mansoor, Xuyun Zhang, Eu Jin Foo, Alireza Jolfaei, Ambreen Hanif, Nasrin Shabani

“…Additionally, feedback loops are incorporated to validate and enhance the effectiveness of ChurnKB.Integrating knowledge-based features into machine learning models (e.g., Random Forest, Logistic Regression, Multilayer Perceptron, and XGBoost) improves predictive performance of ML models compared to the baseline, with XGBoost’s F1 score increasing from 0.5752 to 0.7891. …”
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Multi-Source Causal Invariance for Cuffless Blood Pressure Estimation Based on Photoplethysmography Signal Features by Yiliu Xu, Zhaoming He, Hao Wang

“…BP estimation was then performed using four machine learning models. The MDSFS-EMB algorithm integrated PPFS and HITON-MB, enabling adaptability to different data scales and distribution scenarios. …”
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Quasiperiodic Oscillations and Reflection Feature Evolution in 4U 1630-47 Observed with Insight-HXMT by Jiashi Chen, Wei Wang

“…This is consistent with the prediction of the precessing inner flow model and provides evidence for a geometrical origin of QPOs. …”
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Advanced Human Pose Estimation and Event Classification Using Context-Aware Features and XGBoost Classifier by Wasim Wahid, Aisha Ahmed AlArfaj, Ebtisam Abdullah Alabdulqader, Touseef Sadiq, Hameedur Rahman, Ahmad Jalal

“…This paper presents an advanced approach to Human Pose Estimation (HPE) and Semantic Event Classification (SEC), emphasizing the need for sophisticated human skeleton models, context-aware feature extraction, and machine learning techniques for precise event recognition in daily life logs. …”
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Heart rate lowering treatment leads to a reduction in vulnerable plaque features in atherosclerotic rabbits. by Raf H M van Hoof, Evelien Hermeling, Judith C Sluimer, Julie Salzmann, Arnold P G Hoeks, Jérôme Roussel, Mat J A P Daemen, Harry Struijker-Boudier, Joachim E Wildberger, Sylvia Heeneman, M Eline Kooi

“…<h4>Conclusions</h4>HR lowering treatment with Ivabradine in an atherosclerotic rabbit model is associated with a reduction in vulnerable plaque features. …”
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Comparative analysis of acute and chronic painful temporomandibular disorders: Insights into pain, behavioral, and psychosocial features. by Adrian Ujin Yap, Jung Hwan Jo, Sunghae Kim, Byeong-Min Lee, Ji Woon Park

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Sydney’s residential relocation landscape: Machine learning and feature selection methods unpack the whys and whens by Maryam Bostanara, Amarin Siripanich, Milad Ghasri, Taha Hossein Rashidi

“…Notably, the GBM, XGBoost, and Random Forest models emerge as standout performers. The study provides a comprehensive comparison between automatic and manual feature selection, shedding light on variables influencing households’ duration of stay. …”
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