MFAFNet: Multi-Scale Feature Adaptive Fusion Network Based on DeepLab V3+ for Cloud and Cloud Shadow Segmentation by Yijia Feng, Zhiyong Fan, Ying Yan, Zhengdong Jiang, Shuai Zhang

“…Experimental results demonstrate that the proposed model outperforms existing methods in cloud and cloud shadow segmentation tasks, achieving more precise segmentation performance.…”
Get full text

Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks by Deependra Rastogi, Prashant Johri, Massimo Donelli, Seifedine Kadry, Arfat Ahmad Khan, Giuseppe Espa, Paola Feraco, Jungeun Kim

“…This method helps to significantly decrease model bias and improve performance. Additionally, in order to predict survival rates, we extract radiomic features from the tumor regions that have been segmented, and then use a Deep Learning Inspired 3D replicator neural network to identify the most effective features. …”
Get full text

A dual encoder network with multiscale feature fusion and multiple pooling channel spatial attention for skin scar image segmentation by Weiyuan Yang, Xiaolin Wang, Guangwei Chen, Jianming Wen, Dexing Kong, Jianfeng Zhang, Xinyang Ge, Hao Xu, Jianhua Qin

“…Comprehensive experiments demonstrate the model’s superior performance in scar segmentation, achieving metrics of 96.01% Accuracy, 77.43% Precision, 90.17% Recall, 71.38% Jaccard Index, and 83.21% Dice Coefficient, which compare favorably with mainstream methods, and our model performs well in all metrics, highlighting its potential for clinical adoption in scar analysis.…”
Get full text

Rore: robust and efficient antioxidant protein classification via a novel dimensionality reduction strategy based on learning of fewer features by Chaolu Meng, Yongqi Hou, Quan Zou, Lei Shi, Xi Su, Ying Ju

“…Abstract In protein identification, researchers increasingly aim to achieve efficient classification using fewer features. While many feature selection methods effectively reduce the number of model features, they often cause information loss caused by merely selecting or discarding features, which limits classifier performance. …”
Get full text

Advancing EGFR mutation subtypes prediction in NSCLC by combining 3D pretrained ConvNeXt, radiomics, and clinical features by Peng Hao, Yinghong Yu, Chan-Tao Huang, Fang Zhou, Yi-Kai Xu, Jiancheng Yang, Jiancheng Yang, Jun Xu

“…The instances were randomly divided into training, validation, and test sets. Feature selection was performed, and XGBoost was used to create solo models and combined models to predict the presence of EGFR and subtypes mutations. …”
Get full text

Risk assessment of corn borer based on feature optimization and weighted spatial clustering: a case study in Shandong Province, China by Yanan Zuo, Min Ji, Jiutao Yang, Zhenjin Li, Jing Wang

“…The results indicated that compared with the original RF model, the improved feature optimization model achieves increases of 18.64%, 11.12%, and 11.21% in OOB_score, Accuracy, and F1_score, respectively, and outperforms eight other benchmark models. …”
Get full text

MDFT-GAN: A Multi-Domain Feature Transformer GAN for Bearing Fault Diagnosis Under Limited and Imbalanced Data Conditions by Chenxi Guo, Vyacheslav V. Potekhin, Peng Li, Elena A. Kovalchuk, Jing Lian

“…Beyond performance metrics, this work also incorporates a Grad-CAM-based interpretability scheme to visualize hierarchical feature activation patterns within the discriminator, providing transparent insight into the model’s decision-making rationale across different fault types. …”
Get full text

MultiSEss: Automatic Sleep Staging Model Based on SE Attention Mechanism and State Space Model by Zhentao Huang, Yuyao Yang, Zhiyuan Wang, Yuan Li, Zuowen Chen, Yahong Ma, Shanwen Zhang

Get full text

VBP-YOLO-prune: Robust apple detection under variable weather via feature-adaptive fusion and efficient YOLO pruning by Haohai You, Hao Wang, Zhanchen Wei, Chunguang Bi, Lijuan Zhang, Xuefang Li, Yingying Yin

“…The model incorporates a V7 downsampling module, BiFPN feature fusion, and an improved PIOUv2 loss function, aiming to improve multi-scale representation and bounding box regression. …”
Get full text

Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection. by Ekramul Haque Tusher, Mohd Arfian Ismail, Abdullah Akib, Lubna A Gabralla, Ashraf Osman Ibrahim, Hafizan Mat Som, Muhammad Akmal Remli

“…Compared with previous studies, the Bagging k-NN model demonstrated superior performance under oversampling conditions, achieving 98.37% accuracy, 98.23% CV score, 97.67% precision, 97.93% recall, 98.18% selectivity, 97.79% F1 score, 98.06% balanced accuracy, 98.05% G-mean, a 1.63% error rate, 0.98 AUC, and a standard deviation of 0.192. …”
Get full text

State-of-Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy Features and Fusion Interpretable Deep Learning Framework by Bohan Shao, Jun Zhong, Jie Tian, Yan Li, Xiyu Chen, Weilin Dou, Qiangqiang Liao, Chunyan Lai, Taolin Lu, Jingying Xie

“…The multi-head attention mechanism is the core of this framework, enabling the model to perform weighted analysis of input features. …”
Get full text

High-throughput end-to-end aphid honeydew excretion behavior recognition method based on rapid adaptive motion-feature fusion by Zhongqiang Song, Jiahao Shen, Qiaoyi Liu, Wanyue Zhang, Ziqian Ren, Kaiwen Yang, Xinle Li, Jialei Liu, Fengming Yan, Wenqiang Li, Yuqing Xing, Lili Wu

“…Compared with the model excluding the RK50 module, the mAP50 improved by 2.9%, and its performance in detecting small-target honeydew significantly surpassed mainstream algorithms. …”
Get full text

A novel feature-oriented quality of anything (QoX) framework for end-to-end robotic services in 6G networks by Varvara Mineeva, Abdelhamied A. Ateya, Artem Volkov, Ammar Muthanna, Andrey Koucheryavy, Samia A. Chelloug, Ahmed A. Abd El-Latif

“…The model provides a comprehensive framework for assessing service performance from both the network and user perspectives. …”
Get full text

MF‐RF: A detection approach based on multi‐features and random forest algorithm for improved collusive interest flooding attack by Meng Yue, Silin Peng, Wenzhi Feng

“…Finally, the Random Forest model is designed to detect the I‐CIFA attack. To evaluate the performance of the approach, extensive experiments are conducted in ndnSIM platform. …”
Get full text

AgriDeep-net: An advanced deep feature fusion-based technique for enhanced fine-grain image analytics in precision agriculture by Rakesh Chandra Joshi, Radim Burget, Malay Kishore Dutta

“…Each model is characterized by unique architectural configurations, enabling strategic feature fusion that empowers AgriDeep-Net to capture nuanced semantic information within multi-class agricultural images. …”
Get full text

Enhanced detection of headache presentation in unruptured brain arteriovenous malformation through combined radiologic features: A cross-sectional study by Chia-Yu Liu, Chia-Feng Lu, Jr-Wei Wu, Yong-Sin Hu, Jih-Yuan Lin, Huai-Che Yang, Jing-Kai Loo, Feng-Chi Chang, Kang-Du Liu, Chung-Jung Lin

“…Statistical analyses, including least absolute shrinkage and selection operator regression and logistic regression, were used to select features and develop models. Receiver operating characteristic and decision curve analyses were performed to evaluate performance. …”
Get full text

Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation by Taeyeub Lee, Joram M. Posma

“…We introduce a novel approach that creates a custom graph dataset, driven by molecular optimisation, that incorporates detailed and realistic chemical features such as bond lengths and partial charges as input into the GNN models. …”
Get full text

PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion by Heqi Yang, Junming Dong, Cancan Wang, Zhida Lian, Hui Chang

“…Printed circuit board (PCB) defect detection faces challenges like small target feature loss and severe background interference. To address these issues, this paper proposes PCES-YOLO, an enhanced YOLOv11-based model. …”
Get full text

The Automatic Joint Teeth Segmentation in Panoramic Dental Images using Mask Recurrent Convolutional Neural Networks with Residual Feature Extraction: by Raghavendra H. Bhalerao, Abhijeet Ashok Salunke, Shristi Sharan, Kamlesh Kumar, Priyank Rathod, Prince Kumar, Manish Chaturvedi, Nandlal Bharwani, Krupa Shah, Dhruv Patel, Keval Patel, Vikas Warikoo, Manisha Abhijeet Salunke, Shashank Pandya

“…We also compare the performance of proposed model and other well established networks such as FPN, UNet, PSPNet, and DeepLabV3. …”
Get full text

Machine Learning‐Based Identification of Children With Intermittent Exotropia Using Multiple Resting‐State Functional Magnetic Resonance Imaging Features by Mengdi Zhou, Huixin Li, Xiaoxia Qu, Lirong Zhang, Xueying He, Xiwen Wang, Jie Hong, Jing Fu, Zhaohui Liu

“…Each rs‐fMRI parameter value of one ROI was taken as a feature. The Pearson correlation coefficient (PCC) was performed to reduce dimensions. …”
Get full text