Robust Optical and SAR Image Registration Using Weighted Feature Fusion by Ao Luo, Anxi Yu, Yongsheng Zhang, Wenhao Tong, Huatao Yu

“…To further enhance matching robustness, a confidence-weighted feature fusion strategy is proposed. By establishing a reliability evaluation model for similarity measurement maps, the contribution weights of gradient features and LSD features are dynamically optimized, ensuring adaptive performance under varying conditions. …”
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Multilevel Feature Interaction Network for Remote Sensing Images Semantic Segmentation by Hongkun Chen, Huilan Luo

“…Notably, this study is the first to address ways to enhance the performance for HSR remote sensing image analysis by iteratively refining features at multilevels for different tasks. …”
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Pedestrian Re-Identification Based on Fine-Grained Feature Learning and Fusion by Anming Chen, Weiqiang Liu

“…To evaluate the performance of fine-grained features alignment and fusion, we conduct extensive experiments on three benchmark datasets. …”
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Detection of Road Rage in Vehicle Drivers Based on Speech Feature Fusion by Xiaofeng Feng, Chenhui Liu, Ying Chen

“…Experiments show that feature fusion gives better anti-noise performance than other single feature detection methods. …”
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Maximum Mutual Information Feature Extraction Method Based on the Cloud Platform by Shasha Wei, Huijuan Lu, Wei Jin, Chao Li

“…With the large-scale application of gene chip,gene expression data with high dimension which exists a large number of irrelevant and redundant features may reduce classifier performance problem.A maximum mutual information feature extraction method based on cloud platforms was proposed.Hadoop cloud computing platform could be a parallel computing after gene expression data segmentation,features was extracted at the same time combined with the maximum mutual information method and the characteristics of cloud computing platform filter model was realized.Simulation experiments show that the maximum mutual information feature extraction method based on the cloud platform can rapid extraction of features in a higher classification accuracy which save a lot of time resources to make a highly efficient gene feature extraction system.…”
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Enhanced remote sensing image feature classification using STFF-PSPNet by Haiying Li, Jiaqi Gao, Yang Liu, Chenxi Huang, Lijun Li, Liqiang Zhang

“…By replacing ResNet with the STFF network for better global feature extraction, adding attention modules, and using a combined loss function, the improved model shows excellent performance. …”
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Pavement Disease Visual Detection by Structure Perception and Feature Attention Network by Bin Lv, Shuo Zhang, Haixia Gong, Hongbo Zhang, Bin Dong, Jianzhu Wang, Cong Du, Jianqing Wu

“…Additionally, the convolutional block attention module (CBAM) is integrated to optimize feature map attention across channel and spatial dimensions, enhancing the model focus on critical disease features without significantly increasing complexity. …”
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Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection by Murat Ekinci, Furkancan Demircan, Zafer Cömert, Eyup Gedikli

“…The experimental findings emphasize the potential of strategic feature selection in enhancing the performance of classical machine learning models for ear disease classification. …”
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LFEN: A language feature enhanced network for scene text recognition by Hui Chen, Runming Jiang, Fang Hu, Min Chen, Yin Zhang

“…By directly embedding language features into the text recognition model, we effectively address the issue of accuracy in scene text recognition, reducing the potential risk of error accumulation compared to traditional language recognition-text recognition serial connections. …”
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Global Feature Focusing and Information Enhancement Network for Occluded Pedestrian Detection by ZHENG Kaikui, JI Kangyou, LI Jun, LI Qiming

“…It captures global context information and long range dependencies between feature vectors through forward and backward processing with the State Space Model (SSM). …”
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Fine-Grained Mapping Between Daily Activity Features in Smart Homes by Yahui Wang, Yaqing Liu

“…However, existing heuristic feature mapping methods are often coarse, resulting in only moderate recognition performance. …”
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Efficient feature selection based on Gower distance for breast cancer diagnosis by Salwa Shakir Baawi, Mustafa Noaman Kadhim, Dhiah Al-Shammary

“…The proposed feature selection strategy significantly reduces dimensionality, retains the most relevant features, and improves model performance. …”
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Deep learning-based feature selection for detection of autism spectrum disorder by Ibrahim Nafisah, Nermine Mahmoud, Ahmed A. Ewees, Mohamed G. Khattap, Abdelghani Dahou, Safar M. Alghamdi, Ibrahim A. Fares, Mohammed Azmi Al-Betar, Mohammed Azmi Al-Betar, Mohamed Abd Elaziz, Mohamed Abd Elaziz

“…The enhanced feature selection process, coupled with the hybrid model, addresses limitations in current neuroimaging analyses and offers a promising direction for more accurate and clinically applicable ASD detection models.…”
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Internally generated intangible assets: features of recognition and directions for improving accounting by S.F. Lehenchuk, V.R. Ocheredko

“…The influence of research and development costs on the performance of Ukrainian and American pharmaceutical enterprises based on the use of the multiple linear regression model has been determined. …”
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Anomaly traffic detection method based on data augmentation and feature mining by AN Yishuai, FU Yu, YU Yihan, LIU Taotao

“…Finally, a multi-layer graph convolutional network with a hierarchical attention mechanism was designed, in which local and global features were hierarchically extracted and fused through a multi-level neighborhood aggregation strategy, significantly enhancing the model’s capability to identify key features. …”
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Speaker verification method based on cross-domain attentive feature fusion by Zhen YANG, Tianlang WANG, Haiyan GUO, Tingting WANG

“…Aiming at the problem that the lack of structure information among speech signal sample in the front-end acoustic features of speaker verification system, a speaker verification method based on cross-domain attentive feature fusion was proposed.Firstly, a feature extraction method based on the graph signal processing (GSP) was proposed to extract the structural information of speech signals, each sample point in a speech signal frame was regarded as a graph node to construct the speech graph signal and the graph frequency information of the speech signal was extracted through the graph Fourier transform and filter banks.Then, an attentive feature fusion network with the residual neural network and the squeeze-and- excitation block was proposed to fuse the features in the traditional time-frequency domain and those in the graph frequency domain to promote the speaker verification system performance.Finally, the experiment was carried out on the VoxCeleb, SITW, and CN-Celeb datasets.The experimental results show that the proposed method performs better than the baseline ECAPA-TDNN model in terms of equal error rate (EER) and minimum detection cost function (min-DCF).…”
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Speaker verification method based on cross-domain attentive feature fusion by Zhen YANG, Tianlang WANG, Haiyan GUO, Tingting WANG

“…Aiming at the problem that the lack of structure information among speech signal sample in the front-end acoustic features of speaker verification system, a speaker verification method based on cross-domain attentive feature fusion was proposed.Firstly, a feature extraction method based on the graph signal processing (GSP) was proposed to extract the structural information of speech signals, each sample point in a speech signal frame was regarded as a graph node to construct the speech graph signal and the graph frequency information of the speech signal was extracted through the graph Fourier transform and filter banks.Then, an attentive feature fusion network with the residual neural network and the squeeze-and- excitation block was proposed to fuse the features in the traditional time-frequency domain and those in the graph frequency domain to promote the speaker verification system performance.Finally, the experiment was carried out on the VoxCeleb, SITW, and CN-Celeb datasets.The experimental results show that the proposed method performs better than the baseline ECAPA-TDNN model in terms of equal error rate (EER) and minimum detection cost function (min-DCF).…”
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Approach of detecting low-rate DoS attack based on combined features by Zhi-jun WU, Jing-an ZHANG, Meng YUE, Cai-feng ZHANG

“…LDoS (low-rate denial of service) attack is a kind of RoQ (reduction of quality) attack which has the characteristics of low average rate and strong concealment.These characteristics pose great threats to the security of cloud computing platform and big data center.Based on network traffic analysis,three intrinsic characteristics of LDoS attack flow were extracted to be a set of input to BP neural network,which is a classifier for LDoS attack detection.Hence,an approach of detecting LDoS attacks was proposed based on novel combined feature value.The proposed approach can speedily and accurately model the LDoS attack flows by the efficient self-organizing learning process of BP neural network,in which a proper decision-making indicator is set to detect LDoS attack in accuracy at the end of output.The proposed detection approach was tested in NS2 platform and verified in test-bed network environment by using the Linux TCP-kernel source code,which is a widely accepted LDoS attack generation tool.The detection probability derived from hypothesis testing is 96.68%.Compared with available researches,analysis results show that the performance of combined features detection is better than that of single feature,and has high computational efficiency.…”
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Features of Labour Productivity Measurement in Medical Organizations of the Kemerovo Region – Kuzbass by Artem N. Popsuyko, Galina V. Artamonova

Feature-Based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy by Feng Wang, M. Cenk Gursoy, Senem Velipasalar

“…For all aforementioned analyses, we evaluate the performance of the proposed learning scheme via experiments on an image classification task and a natural language processing task to demonstrate its effectiveness (<uri>https://github.com/wfwf10/Feature-based-Federated-Transfer-Learning</uri>).…”
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