Multi‐task image restoration network based on spatial aggregation attention and multi‐feature fusion by Chunyan Peng, Xueya Zhao, Yangbo Chen, Wanqing Zhang, Yuhui Zheng

“…Additionally, to enhance the model's understanding and representation capabilities of images, the feature fusion mechanism (FFM) is designed to merge feature information from different levels. …”
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EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection by Alanoud Al Mazroa, Majdy M. Eltahir, Shouki A. Ebad, Faiz Abdullah Alotaibi, Venkatachalam K, Jaehyuk Cho

“…For classification purposes, AWFM enables our model to modify the importance of features dynamically. …”
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An approach to arousal disorder classification using deformable convolution and adaptive multiscale features in EEG signals by Andia Foroughi, Fardad Farokhi, Fereidoun Nowshiravan Rahatabad, Alireza Kashaninia

“…We apply this model after segmenting EEG data into 30-second windows and generating spectrogram images. …”
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Early prediction of Alzheimer’s disease using artificial intelligence and cortical features on T1WI sequences by Rong Zeng, Rong Zeng, Beisheng Yang, Faqi Wu, Huan Liu, Xiaojia Wu, Lin Tang, Rao Song, Qingqing Zheng, Xia Wang, Dajing Guo

“…The performance of each model was assessed by the concordance index (C-index).ResultsIn the training group, the C-indexes of the network, morphology, network-clinical, morphology-clinical, morphology-network and morphology-network-clinical models were 0.834, 0.926, 0.915, 0.949, 0.928, and 0.951, respectively. …”
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Domain adaptation spatial feature perception neural network for cross-subject EEG emotion recognition by Wei Lu, Wei Lu, Xiaobo Zhang, Xiaobo Zhang, Lingnan Xia, Hua Ma, Tien-Ping Tan

“…Currently, EEG-based emotion recognition, utilizing deep learning frameworks, has been effectively applied and achieved commendable performance. However, existing deep learning-based models face challenges in capturing both the spatial activity features and spatial topology features of EEG signals simultaneously. …”
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MCFA: Multi-Scale Cascade and Feature Adaptive Alignment Network for Cross-View Geo-Localization by Kaiji Hou, Qiang Tong, Na Yan, Xiulei Liu, Shoulu Hou

“…Existing methods have made certain advancements in extracting local features from images. However, they exhibit limitations in modeling the interactions among local features and fall short in aligning cross-view representations accurately. …”
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Spatio-Temporal Feature Aware Vision Transformers for Real-Time Unmanned Aerial Vehicle Tracking by Hao Zhang, Hengzhou Ye, Xiaoyu Guo, Xu Zhang, Yao Rong, Shuiwang Li

“…Addressing the shortcomings of existing tracking systems in capturing temporal relationships between consecutive frames, NT-Track meticulously analyzes the positional changes in targets across frames and leverages the similarity of the surrounding areas to extract feature information. Furthermore, our method integrates spatial and temporal information seamlessly into a unified framework through the introduction of a temporal feature fusion technique, thereby bolstering the overall performance of the model. …”
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Reverse modeling of the human mandible 3D geometric model by Mitić Jelena, Vitković Nikola, Manić Miodrag, Trajanović Miroslav, Petrović Slađana, Arsić Stojanka

“…The aim of this research was to present application of a new method, method of anatomical features (MAF), for the creation of the geometrical model (surface and solid) of the human mandible. …”
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MRA-YOLOv8: A Network Enhancing Feature Extraction Ability for Photovoltaic Cell Defects by Nannan Wang, Siqi Huang, Xiangpeng Liu, Zhining Wang, Yi Liu, Zhe Gao

“…This module provides finer-grained multi-scale features, improving feature extraction from complex backgrounds and enhancing the model’s robustness. …”
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Differentiating Emphysema From Emphysema-Dominated COPD Patients with CT Imaging Feature and Machine Learning by Guo W, Li M, Li Y, Fan X, Wu L

“…A random forest model was developed using these QCT features to differentiate between the two groups. …”
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Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis by Fernando Pedro Silva Almeida, Mauro Castelli, Nadine Côrte-Real

“…This study addresses the gap in existing research by comprehensively analyzing the performance of various machine learning algorithms, including ensemble learning and deep learning models, to improve prediction accuracy. …”
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Towards Efficient SAR Ship Detection: Multi-Level Feature Fusion and Lightweight Network Design by Wei Xu, Zengyuan Guo, Pingping Huang, Weixian Tan, Zhiqi Gao

“…Thus, guided by the principles of lightweight design, robustness, and energy efficiency optimization, this study proposes a three-stage collaborative multi-level feature fusion framework to reduce model complexity without compromising detection performance. …”
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Multi-Scale Feature Mixed Attention Network for Cloud and Snow Segmentation in Remote Sensing Images by Liling Zhao, Junyu Chen, Zichen Liao, Feng Shi

“…In addition, existing segmentation models often misjudge the body of clouds and snow with similar features. …”
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A Scale-Aware and Discriminative Feature Learning Network for Fine-Grained Rigid Object Recognition by Yangte Gao, Chenwei Deng, Liang Chen, Zicong Zhu

“…The experimental results show that introducing only a small number of parameters during training, SD-Net, improves the performance of the models based on the ResNet and ViT by about 4.6 points. …”
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LocaLock: Enhancing Multi-Object Tracking in Satellite Videos via Local Feature Matching by Lingyu Kong, Zhiyuan Yan, Hanru Shi, Ting Zhang, Lei Wang

“…Specifically, LocaLock utilizes an anchor-free detection backbone for efficiency and employs a local cost volume (LCV) module to perform precise feature matching in the local area. …”
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Multi-View Attention Network With Iterative Feature Refinement and Boundary Awareness for Endoscopic Image Segmentation by Dongzhi He, Rui Zhang, Yu Liang, Jiaping Chen, Yunqi Li

“…Moreover, a Multi-scale Feature Integration (MFI) block is proposed to integrate multi-scale features during the decoder stage to capture potentially useful features. …”
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Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images by Yuhang Zhang, Wuxia Zhang, Songtao Ding, Siyuan Wu, Xiaoqiang Lu

“…To address these issues, we propose a Spatial-Temporal Semantic Feature Interaction Network (STS-FINet) to improve the performance of SCD in RSI. …”
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Ensemble of feature augmented convolutional neural network and deep autoencoder for efficient detection of network attacks by Selvakumar B, Sivaanandh M, Muneeswaran K, Lakshmanan B

“…To assess the stated ensemble model, numerous experiments are conducted on benchmark datasets like NSL-KDD and CICDS2017. …”
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Passenger Demand-Oriented High-Speed Train Stop Planning with Service-Node Features Analysis by Fanxiao Liu, Qiyuan Peng, Gongyuan Lu, Guangyuan Zhang

“…A restrained MDP-based stop plan model is formulated, and a numerical experiment is conducted to demonstrate the performance of the proposed approach with real-world train operation data collected from the Beijing-Shanghai high-speed railway.…”
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Advanced cloud intrusion detection framework using graph based features transformers and contrastive learning by Vijay Govindarajan, Junaid Hussain Muzamal

“…Experimental results show an average accuracy of 99.97%, with high precision and recall across all attack types, including minority classes such as U2R and R2L. The model achieves low false-positive rates and demonstrates real-time inference performance with modest resource requirements. …”
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