Coherent Spectral Feature Extraction Using Symmetric Autoencoders by Archisman Bhattacharjee, Pawan Bharadwaj

“…Furthermore, we leverage these coherent features to enhance the performance of some leading spectral–spatial hyperspectral image (HSI) classification methods. …”
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Caving Depth Classification by Feature Extraction in Cuttings Images by Laura Viviana Galvis, Reinel Corzo Rueda, Henry Arguello

“…Several simulations illustrate the performance of the proposed model using real images from a wellbore in a Colombian basin. …”
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Novel hybrid intelligence model for early Alzheimer's diagnosis utilizing multimodal biomarker fusion by Shehu Mohammed, Neha Malhotra, Arun Singh, Awad M. Awadelkarim, Shakeel Ahmed, Saiprasad Potharaju

“…The database comprises 35 demographic, behavioral, and clinical features. Feature selection procedures produced key predicting variables (i.e., MMSE scores, performance in Activities of Daily Living (ADL), cholesterol level, and behavior problems). …”
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Feature Enhanced Spatial–Temporal Trajectory Similarity Computation by Silin Zhou, Chengrui Huang, Yuntao Wen, Lisi Chen

“…To solve this problem, we propose a Feature Enhanced Spatial–Temporal trajectory similarity computation framework FEST, which is a graph neural network (GNN) and sequence model pipeline. …”
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Implementing large language model and retrieval augmented generation to extract geographic locations of illicit transnational kidney trade by Zifu Wang, Meng-Hao Li, Patrick Baxter, Olzhas Zhorayev, Jiaxin Wei, Valerie Kovacs, Qiuhan Zhao, Chaowei Yang, Naoru Koizumi

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Features of Development of Communicative Competence in Children with Mental Retardation by Zarechnaya A.A.

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Local feature enhancement transformer for image super-resolution by Huang Weijie, Huang Detian

“…Abstract Transformers have demonstrated remarkable success in image super-resolution (SR) owing to their powerful long-range dependency modeling capability. Although increasing the sliding window size of transformer-based models (e.g., SwinIR) can improve SR performance, this weakens the learning of the fine-level local features, resulting in blurry details in the reconstructed images. …”
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Technological Features of Forming Flat Tool for Processing Axicons by A. S. Kozeruk, R. O. Dias Gonzalez, A. A. Sukhotzkiy, M. I. Filonova, V. O. Kuznechik

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Machine learning-based academic performance prediction with explainability for enhanced decision-making in educational institutions by Wesam Ahmed, Mudasir Ahmad Wani, Pawel Plawiak, Souham Meshoul, Amena Mahmoud, Mohamed Hammad

“…On the second, more complex dataset, the VR model also performed best with an R² of 0.7716 using the full feature set, highlighting its robustness and adaptability across diverse academic contexts. …”
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Salient Object Detection Based on Background Feature Clustering by Kan Huang, Yong Zhang, Bo Lv, Yongbiao Shi

“…We model the background distribution based on feature clustering algorithm, which allows for fully exploiting statistical and structural information of the background. …”
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Beyond <i>xG</i>: A Dual Prediction Model for Analyzing Player Performance Through Expected and Actual Goals in European Soccer Leagues by Davronbek Malikov, Jaeho Kim

“…This approach improves prediction accuracy and provides actionable insights for coaches and analysts to optimize team performance. By using constructed features from various methods in the dataset, we improve our model’s performance by as much as 12%. …”
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Advancing Knowledge on Machine Learning Algorithms for Predicting Childhood Vaccination Defaulters in Ghana: A Comparative Performance Analysis by Eliezer Ofori Odei-Lartey, Stephaney Gyaase, Dominic Asamoah, Thomas Gyan, Kwaku Poku Asante, Michael Asante

“…The results showed higher performance across the ensemble tree classifiers. The random forest and extreme gradient boosting models reported the highest F1 scores (0.92) and AUCs (0.95) on augmented unseen data. …”
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Instance segmentation of oyster mushroom datasets: A novel data sampling methodology for training and evaluation of deep learning models by Christos Charisis, Meiqing Wang, Dimitrios Argyropoulos

“…A custom data splitting and reduction strategy was designed to generate multiple training subsets for an in-depth model performance evaluation. Also, the study aims to examine the ability of five feature extraction backbone configurations of Mask R-CNN: i) CNN-based (ResNet50, ResNeXt101 and ConvNeXt) and ii) Transformer-based (Swin small and tiny) to accurately detect and segment single mushroom instances within the cluster in the images. …”
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Shared and distinct neural signatures of feature and spatial attention by Anmin Yang, Jinhua Tian, Wenbo Wang, Liqin Zhou, Ke Zhou

“…The debate on whether feature attention (FA) and spatial attention (SA) share a common neural mechanism remains unresolved. …”
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Pathogenetic features of experimental osteoarthrosis induced by dexamethasone and talc by E. V. Gladkova

“…We performed a morphometric evaluation of articular cartilages (their thickness, extracellular matrix arrangement, spatial arrangement of the main components, distribution density, and main cellular indices of chondrocytes), as well as changes in subchondral bones (the presence of trabeculae in the basal layer of the articular cartilage and individual osteophytes) in 30 rats with a model of primary osteoarthrosis induced by sequential administration of 0.5 ml dexamethasone (2 mg) and 1 ml 10% sterile talc suspension mixed with normal saline into the joint cavity. …”
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CT Anatomical Features and Dimensions of the Rabbit Adrenal Glands by Kamelia Stamatova-Yovcheva, Rosen Dimitrov, Diyana Vladova, David Yovchev, Hristo Hristov, Vladi Nedev, Nikolay Goranov, Avche Dineva

“…The rabbit is a preferred animal pet species and is also used as an experimental model in research. The aim of this study was to investigate the CT anatomical features of the rabbit adrenal glands, using the CT device SOMATOM. …”
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Integrating CEUS Imaging Features and LI-RADS Classification for Postoperative Early Recurrence Prediction in Solitary Hepatocellular Carcinoma: A Machine Learning-Based Prognostic... by Liang L, Pang J, Zhang B, Que Q, Gao R, Wu Y, Peng J, Zhang W, Bai X, Wen R, He Y, Yang H

“…Patients were randomly assigned to training (n = 196) and validation (n = 83) cohorts in a 7:3 ratio. Feature selection was performed using univariate Cox regression (p ≤ 0.05), and four ML algorithms—Random Survival Forest (RSF), Gradient Boosting Machine (GBM), CoxBoost, and XGBoost—were applied to develop recurrence prediction models. …”
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Adaptive Particle Swarm Optimization with Landscape Learning for Global Optimization and Feature Selection by Khalil Abbal, Mohammed El-Amrani, Oussama Aoun, Youssef Benadada

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Building a composition-microstructure-performance model for C–V–Cr–Mo wear-resistant steel via the thermodynamic calculations and machine learning synergy by Shuaiwu Tong, Shuaijun Zhang, Chong Chen, Tao Jiang, Peng Li, Shizhong Wei

“…By using phase content and experimental parameters as input features, the Gradient Boosted Tree model and Support Vector Regression model demonstrated strong applicability in predicting frictional performance and wear, respectively. …”
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A Mixed Method Approach for Modelling Entry Capacity at Rotary Intersections by Antonio PRATELLI, Lorenzo BROCCHINI, Reginald Roy SOULEYRETTE, Teng WANG

“…This paper introduces a novel approach combining modern roundabouts capacity models with the old rotary ones. In particular, the present study proposes a mixed approach based on an iterative process that combines the English TRRL model, which is suited for the old rotaries and based on short weaving sections capacity, with the features of the HCM-7th entry capacity model of the modern roundabouts, which is based on the circulating-traffic priority rule. …”
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