Concrete Crack Detection and Segregation: A Feature Fusion, Crack Isolation, and Explainable AI-Based Approach by Reshma Ahmed Swarna, Muhammad Minoar Hossain, Mst. Rokeya Khatun, Mohammad Motiur Rahman, Arslan Munir

“…This study evaluates several CNN models, including VGG-16, VGG-19, Inception-V3, and ResNet-50, and various HC techniques, such as wavelet transform, counterlet transform, and curvelet transform for feature extraction. …”
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MVT-Net: A novel cervical tumour segmentation using multi-view feature transfer learning. by Yao Yao, Yunzhi Chen, An Yang, Ye Ye, Lichun Wei, Shuiping Gou, Hua Yang

“…To address these challenges, this study proposes a novel cervical tumour segmentation model based on multi-view feature transfer learning, named MVT-Net. …”
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Leveraging explainable multi-scale features for fine-grained circRNA-miRNA interaction prediction by Li Peng, Wang Wang, Zongyi Yang, Xiangzheng Fu, Wei Liang, Dongsheng Cao

“…Conclusions We utilize a manifold-based method to examine model performance in detail, revealing that MFERL exhibits robust generalization, robustness, and interpretability. …”
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Deep multiscale feature fusion network with dual attention for rolling bearing remaining useful life prediction by Yingming Yang, Zhihai Wang, Xiaoqin Liu, Tao Liu, Zhuopeng Luo

“…Abstract Aiming at the existing life prediction methods for rolling bearing degradation information mining is not sufficient, the critical time step information degree is insufficient, resulting in the loss of key degradation information, model prediction accuracy and model generalization ability is insufficient, this paper proposes a novel deep multiscale feature fusion network with dual attention for rolling bearing remaining useful life (RUL) prediction. …”
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Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMR by Bibhuprasad Sahu, Amrutanshu Panigrahi, Abhilash Pati, Manmath Nath Das, Prince Jain, Ghanashyam Sahoo, Haipeng Liu

“…The features selected, with the aid of fast mRMR and tested with a range of classifiers, Support Vector Machine, Weighted Support Vector Machine, Extreme Gradient Boosting, Adaptive Boosting, and Random Forest classifier, are tested for comprehensively proofed performance. …”
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DynaOOD-Net: Dynamic Progressive Feature Fusion and Energy Balancing for Robust Out-of-Distribution Detection by Jiting Zhou, Zhihao Zhou, Pu Zhang

“…To meet these demands, this paper proposes DynaOOD-Net, a novel detection framework designed to enhance model generalization performance through dynamic feature integration and energy-balanced regularization strategies. …”
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DSAT: a dynamic sparse attention transformer for steel surface defect detection with hierarchical feature fusion by Shouluan Wu, Hui Yang, Liefa Liao, Chao Song, Yating Fang, Jianglong Fu, Tan Li

“…These defects exhibit diverse morphological characteristics and complex patterns, which pose substantial challenges to traditional detection models, particularly regarding multi-scale feature extraction and information retention across network depths. …”
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Classification of Particulate Matter (PM2.5) Concentrations Using Feature Selection and Machine Learning Strategies by Matara Caroline Mongina, Nyambane Simpson Osano, Yusuf Amir Okeyo, Ochungo Elisha Akech, Khattak Afaq

“…The Binary Logistic Regression (BLR) model demonstrated comparatively poorer performance in terms of Sensitivity (0.244), Specificity (0.614), F1-Score (0.455), and AU-ROC (0.508) when compared to other ML models. …”
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A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features by Luigi Russo, Antonietta Sorriso, Silvia Liberata Ullo, Paolo Gamba

“…The study focuses on three distinct regions—Amazonia, Africa, and Siberia—and evaluates the model performance across diverse ecoregions within these areas. …”
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Deep Learning Enhanced Feature Extraction of Potholes Using Vision and LiDAR Data for Road Maintenance by Abhiram Karukayil, Christopher Quail, Fernando Auat Cheein

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Lung Cancer Classification Using the Extreme Gradient Boosting (XGBoost) Algorithm and Mutual Information for Feature Selection by Regitha Zizilia, Yulison Herry Chrisnanto, Gunawan Abdillah

“…In contrast, for the 90:10 and 80:20 split scenarios, a decline in accuracy was observed — testing accuracy dropped to 88.63% and 88.85%, and K-Fold Cross-Validation accuracy fell to 88.87% and 90.24%. Feature selection using Mutual Information improves computational efficiency by reducing the number of features, and it can be effectively applied to simplify feature sets without significantly compromising model performance in certain data scenarios, depending on the characteristics of the dataset.…”
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Advanced feature fusion of radiomics and deep learning for accurate detection of wrist fractures on X-ray images by Mohamed J. Saadh, Qusay Mohammed Hussain, Rafid Jihad Albadr, Hardik Doshi, M. M. Rekha, Mayank Kundlas, Amrita Pal, Jasur Rizaev, Waam Mohammed Taher, Mariem Alwan, Mahmod Jasem Jawad, Ali M. Ali Al-Nuaimi, Bagher Farhood

“…SHAP analysis and t-SNE visualizations confirmed the interpretability and robustness of the selected features. Conclusions This hybrid framework demonstrates the potential for integrating radiomic and deep features to enhance diagnostic performance for wrist and forearm fractures, providing a reliable and interpretable solution suitable for clinical applications.…”
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Single-Image Superresolution for RGB Remote Sensing Imagery via Multiscale CNN-Transformer Feature Fusion by Xudong Yao, Haopeng Zhang, Sizhe Wen, Zhenwei Shi, Zhiguo Jiang

“…Previously, convolutional neural network (CNN) achieves impressive progress in SISR due to its strong local feature extraction capability. However, the CNN is difficult to model long-range dependencies, which limits the performance of SISR. …”
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MRI-based intra-tumoral ecological diversity features and temporal characteristics for predicting microvascular invasion in hepatocellular carcinoma by Yuli Zeng, Huiqin Wu, Yanqiu Zhu, Chao Li, Dongyang Du, Yang Song, Sulian Su, Jie Qin, Guihua Jiang, Guihua Jiang, Guihua Jiang

“…Additionally, temporal features were derived by subtracting the PVP features from the AP features, creating a delta-radiomics model (MDelta). …”
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Few-Shot Object Detection for Remote Sensing Images via Pseudo-Sample Generation and Feature Enhancement by Zhaoguo Huang, Danyang Chen, Cheng Zhong

“…Additionally, since only a subset of instances are labeled in FSOD training data, the model might mistakenly treat unlabeled instances as background, leading to confusion between foreground features and background features, particularly those of novel classes. …”
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AMSformer: A Transformer for Grain Storage Temperature Prediction Using Adaptive Multi-Scale Feature Fusion by Qinghui Zhang, Weixiang Zhang, Quanzhen Huang, Chenxia Wan, Zhihui Li

“…To tackle this issue, this paper introduces an adaptive multi-scale feature fusion transformer model (AMSformer). Firstly, the model utilizes the adaptive channel attention (ACA) mechanism to adjust the weights of different channels according to the input data characteristics and suppress irrelevant or redundant channels. …”
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A Drilling Debris Tracking and Velocity Measurement Method Based on Fine Target Feature Fusion Optimization by Jinteng Yang, Yu Bao, Zumao Xie, Haojie Zhang, Zhongnian Li, Yonggang Li

“…Specifically, we enhance the multi-scale feature fusion capability of the YOLOv11 detection head by incorporating a lightweight feature extraction module, Ghost Conv, and a feature-aligned fusion module, FA-Concat, resulting in an improved model named YOLOv11-Dd (drilling debris). …”
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Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble Learning by Zaib Akram, Kashif Munir, Muhammad Usama Tanveer, Atiq Ur Rehman, Amine Bermak

“…These extracted features are then transferred into a refined probabilistic feature set, upon which we construct an ensemble model leveraging the strengths of Logistic Regression (LR), Random Forest (RF), and Gaussian Naive Bayes (GNB) classifiers. …”
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Graph-based analysis of histopathological images for lung cancer classification using GLCM features and enhanced graph by Imam Dad, JianFeng He, Zulqarnain Baloch

“…Our methodology leverages Gray-Level Co-occurrence Matrix (GLCM) features to quantify tissue texture, constructs a Sparse Cosine Similarity Matrix (SCSM) to model spatial relationships, and employs DeepWalk embeddings to capture topological patterns. …”
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Application of dual branch and bidirectional feedback feature extraction networks for real time accurate positioning of stents by Shixiao Wu, Rui Hu, Chengcheng Guo, Xingyuan Lu, Peng Leng, Zhiwei Wang

“…The DBMedDet model features a parallel dual-branch edge feature extraction network, a bidirectional feedback feature fusion neck sub-network, as well as a position detection head and a classification head specifically designed for thoracic and abdominal aortic stents. …”
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