MRSNet: Multi-Resolution Scale Feature Fusion-Based Universal Density Counting Network by Yi Zhang, Wei Song, Mingyue Shao, Xiangchun Liu

“…Motivated by this, we propose a multi-resolution scale feature fusion-based universal density counting network (MRSNet). …”
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A quantitative benchmark of neural network feature selection methods for detecting nonlinear signals by Antoine Passemiers, Pietro Folco, Daniele Raimondi, Giovanni Birolo, Yves Moreau, Piero Fariselli

“…We also use the same settings to benchmark the reliability of gradient-based feature attribution techniques for Neural Networks (NNs), such as Saliency Maps (SM). …”
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Interpretable capsule networks via self attention routing on spatially invariant feature surfaces by Peizhang Li, Jiyuan Ru, Qing Fei, Zhen Chen, Bo Wang

“…However, current classification approaches based on convolutional neural networks often suffer from limited generalization and robustness, particularly when processing data characterized by abstract class features and pronounced spatial attributes. …”
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Dynamic Bidirectional Feature Enhancement Network for Thin Cloud Removal in Remote Sensing Images by Yu Wang, Hao Chen, Ye Zhang, Guozheng Li

“…To address these issues, we propose a dynamic bidirectional feature enhancement network for thin cloud removal in optical remote sensing images. …”
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Application of Generative Adversarial Networks Based on Global and Local Feature Information in Hippocampus Segmentation by WEI Zhihong, KONG Xudong, KONG Yan, YAN Shiju, DING Yang, WEI Xianding, KONG Dong, YANG Bo

“…To address this issue, this study proposes a generative adversarial network (GAN) based on global and local feature information (GLGAN) for hippocampus segmentation. …”
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Copy-Move Forgery Detection Technique Using Graph Convolutional Networks Feature Extraction by Varun Shinde, Vineet Dhanawat, Ahmad Almogren, Anjanava Biswas, Muhammad Bilal, Rizwan Ali Naqvi, Ateeq Ur Rehman

“…This paper presents a new method for CMF Detection (CMFD) that uses the power of Graph Convolution Networks (GCNs) and its multiple layers with ReLU activation, for CMFD and analysis. …”
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Evolution of the Limpopo River Basin in Botswana based on morphometric and morphotectonic features from selected rivers using GIS techniques by One Moses, Read B Mapeo, Joyce G Maphanyane

“…This study used morphometric techniques to generate new information describing the evolution and hydrogeological behaviour of the Limpopo River Basin in Botswana, based on the analysis of drainage surface features, form, and size. …”
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Transfer learning based feature selection for feedforward neural network for speech emotion classifier by D. V. Krasnoproshin, M. I. Vashkevich

“…Proposed transfer learning approach consist in employing the backward-step selection algorithm for feature selection using statistical learning classifiers, the obtained subset of features than subsequently used to train feedforward neural networks. …”
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A music structure analysis method based on beat feature and improved residual networks. by Bing Lu, Qianxue Zhang, Yi Guo, Fuqiang Hu, Xuejun Xiong

“…In response to the issues of insufficient audio feature representation and insufficient model generalization ability in music structure analysis methods, a music structure analysis method based on beat feature fusion and an improved residual network was designed. …”
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Identifying Influential Nodes in Complex Networks via Transformer with Multi-Scale Feature Fusion by Tingshuai Jiang, Yirun Ruan, Tianyuan Yu, Liang Bai, Yifei Yuan

“…Through the transformer module, node information is effectively aggregated, thereby improving the model’s ability to recognize key nodes. We perform evaluations using six real-world and three synthetic network datasets, comparing our method against multiple baselines using the SIR model to validate its effectiveness. …”
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AEFFNet: Attention Enhanced Feature Fusion Network for Small Object Detection in UAV Imagery by Zhaoyu Nian, Wenzhu Yang, Hao Chen

“…Addressing the specific challenges posed by small and densely distributed objects in such images, we introduce an attention enhanced feature fusion network (AEFFNet) designed specifically for small object detection in UAV imagery. …”
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Network traffic anomaly detection model based on feature grouping and multi‐autoencoders integration by Yang Zhou, Haoyang Zeng, Zhourong Zheng, Wei Zhang

“…Abstract This paper presents a network traffic anomaly detection model based on feature grouping and multiple autoencoders (multi‐AEs) integration. …”
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Terrain and Atmosphere Classification Framework on Satellite Data Through Attentional Feature Fusion Network by Antoni Jaszcz, Dawid Połap

“…Hence, in this paper, we propose a neural classifier architecture that analyzes different features by the parallel processing of information in the network and combines them with a feature fusion mechanism. …”
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A lightweight remote sensing image detection model with feature aggregation diffusion network by Xiaohui Cheng, Xukun Wang, Yun Deng, Qiu Lu, Yanping Kang, Jian Tang, Yuanyuan Shi, Junyu Zhao

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Optical Flow Magnification and Cosine Similarity Feature Fusion Network for Micro-Expression Recognition by Heyou Chang, Jiazheng Yang, Kai Huang, Wei Xu, Jian Zhang, Hao Zheng

“…Additionally, an enhanced MobileNetV3-based feature extraction module, incorporating Kolmogorov–Arnold networks and convolutional attention mechanisms, effectively captures both global and local features from optical flow images. …”
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A feature-level ensemble machine learning approach for attack detection in IoT networks by Firoz Khan, B. S. Sunil Kumar, Sangeeta Sangani

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Attention Feature Fusion Network via Knowledge Propagation for Automated Respiratory Sound Classification by Ida A. P. A. Crisdayanti, Sung Woo Nam, Seong Kwan Jung, Seong-Eun Kim

“…<italic>Methods:</italic> We developed a deep convolutional neural network (CNN) model that utilizes spectrographic representations of respiratory sounds within an image classification framework. …”
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A Lightweight Network for UAV Multi-Scale Feature Fusion-Based Object Detection by Sheng Deng, Yaping Wan

“…To capture multi-scale object information, a Multi-Scale Cross-Axis Attention (MSCA) mechanism is embedded within the backbone network. The neck network integrates a Multi-Scale Fusion Block (MSFB) to combine multi-level features, further boosting detection precision. …”
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Enhancing Security in Industrial IoT Networks: Machine Learning Solutions for Feature Selection and Reduction by Ahmad Houkan, Ashwin Kumar Sahoo, Sarada Prasad Gochhayat, Prabodh Kumar Sahoo, Haipeng Liu, Syed Ghufran Khalid, Prince Jain

“…Six machine learning algorithms&#x2014;Decision Trees, k-nearest neighbors, Gaussian Support Vector Machine, Neural Network, Support Vector Machines kernel, and Logistic Regression Kernel&#x2014;were evaluated for both binary and multi-class classification using feature sets of 4, 12, 23, 50, and 79 features. …”
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Autonomous computing and network convergence:architecture, technologies, and prospects by Xiaomao ZHOU, Qingmin JIA, Yujiao HU, Kai GUO, Qianpiao MA, Hui LIU, Renchao XIE

“…In view of the new service scenarios and the demand for high intelligence network in computing and network convergence (CNC), the concept of autonomous CNC (Auto-CNC) is elaborated, where intelligence was introduced into all the aspects of CNC, including resource integration, process automation, and system intelligence.The current research directions and remaining challenges of CNC were introduced, and three key features, i.e., intent-driven computing network, the autonomous system operation and the adaptive co-evolution of communication, computing intelligence, were summarized from the proposed Auto-CNC.Meanwhile, the reference architecture and key technologies of Auto-CNC were described, which were followed by several preliminary exploration cases.Finally, future research trends and technical advice were discussed and recommended.…”
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