AEFFNet: Attention Enhanced Feature Fusion Network for Small Object Detection in UAV Imagery by Zhaoyu Nian, Wenzhu Yang, Hao Chen

“…The rapid advancement of unmanned aerial vehicle (UAV) technology has markedly increased the use of drone-captured imagery across various applications, necessitating enhanced accuracy and real-time performance in UAV image detection. 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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Predictive Analysis of Maritime Congestion Using Dynamic Big Data and Multiscale Feature Analysis by Yalin Wu

“…However, maritime network congestion has become an increasingly critical challenge that significantly affects shipping efficiency and the overall operational performance of the industry. This study proposes an innovative congestion prediction approach using dynamic big data analysis of vessel trajectories and multiscale feature analysis. …”
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Ensemble Learning for Network Intrusion Detection Based on Correlation and Embedded Feature Selection Techniques by Ghalia Nassreddine, Mohamad Nassereddine, Obada Al-Khatib

“…Combining the results of machine learning models like the random forest, decision tree, k-nearest neighbors, and XGBoost with logistic regression as a meta-model is what this method is based on. …”
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Brain tumor intelligent diagnosis based on Auto-Encoder and U-Net feature extraction. by Yaru Cao, Fengning Liang, Teng Zhao, Jinting Han, Yingchao Wang, Haowen Wu, Kexing Zhang, Huiwen Qiu, Yizhe Ding, Hong Zhu

“…The encoder of the feature extractor based on dense block, is used to enhance feature propagation and reduce the number of parameters. …”
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Image Alignment Based on Deep Learning to Extract Deep Feature Information from Images by Lin Zhu, Yuxing Mao, Jianyu Pan

“…This network aims to enhance image alignment performance through multi-level feature learning. DFA-Net is based on the deep residual architecture and introduces spatial pyramid pooling to achieve cross-scalar feature fusion, effectively enhancing the feature’s adaptability to scale. …”
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Multidomain Feature Fusion for Varying Speed Bearing Diagnosis Using Broad Learning System by Tingting Wu, Yufen Zhuang, Bi Fan, Hainan Guo, Wei Fan, Cai Yi, Kangkang Xu

“…Then, the broad learning system is employed with the fused features for classification. Our experimental results suggest that, compared with other machine learning models, the proposed broad learning system model, which makes full use of the fused feature, can improve the diagnosis performance significantly in terms of both accuracy and robustness analysis.…”
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End-to-end feature fusion for jointly optimized speech enhancement and automatic speech recognition by Mohamed Medani, Nasir Saleem, Fethi Fkih, Manal Abdullah Alohali, Hela Elmannai, Sami Bourouis

“…This fusion approach seeks to mitigate speech distortions, enhancing the overall performance of the ASR system. The proposed model consists of an attentional codec equipped with a causal attention mechanism for SE, a GRU-based fusion network, and an ASR system. …”
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A lightweight YOLO network using temporal features for high-resolution sonar segmentation by Sen Gao, Sen Gao, Wei Guo, Wei Guo, Gaofei Xu, Gaofei Xu, Ben Liu, Ben Liu, Yu Sun, Bo Yuan

“…The model was trained and evaluated on a high-resolution sonar dataset collected using an AUV-mounted Oculus MD750d multibeam forward-looking sonar in two distinct underwater environments.ResultsImplementation on Nvidia Jetson TX2 demonstrated significant performance improvements. (1) Processing latency reduced to 87.4 ms (keyframes) and 35.3 ms (non-keyframes)(2)Maintained competitive segmentation accuracy compared to conventional methods and achieved low latency.DiscussionThe proposed architecture successfully addresses the speed-accuracy trade-off in sonar image segmentation through its innovative temporal feature utilization and computational skipping mechanism. …”
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An Optimized Cascaded CNN Approach for Feature Extraction From Brain MRIs for Tumor Classification by Santosh Kumar Chhotray, Debahuti Mishra, Sarada Prasanna Pati, Sashikala Mishra

“…This study enhances brain tumor classification by leveraging pre-trained models and attention mechanisms, ultimately improving accuracy and reliability in medical imaging diagnostics through feature extraction. …”
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Improving synergistic drug combination prediction with signature-based gene expression features in oncology by Mozhgan Mozaffarilegha, Sajjad Gharaghani

“…We compared their performance with that of conventional drug signatures and chemical structure-based descriptors.Results:Our results demonstrate that models incorporating DRS features consistently outperform traditional approaches across all evaluated algorithms. …”
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Remote Sensing Image Change Detection Based on Multi-Level Diversity Feature Fusion by Honggang Xie, Wanjie Ma

“…Furthermore, to effectively address the problem of boundary misjudgment in change areas caused by fixed thresholds, an Adaptive Threshold Module is devised to adaptively learn the texture features of change and unchanged regions, enabling the generation of more accurate thresholds for boundary determination, thereby improving the robustness of the algorithm model and alleviating false alarms. …”
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A Method for Few-Shot Radar Target Recognition Based on Multimodal Feature Fusion by Yongjing Zhou, Yonggang Li, Weigang Zhu

“…Furthermore, we establish a multimodal fusion classification network that integrates bi-directional long short-term memory and residual neural network architectures, facilitating deep bimodal fusion through an encoding-decoding framework augmented by an energy embedding strategy. To optimize the model, we propose a cross-modal equilibrium loss function that amalgamates similarity metrics from diverse features with cross-entropy loss, thereby guiding the optimization process towards enhancing metric spatial discrimination and balancing classification performance. …”
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Thyroglobulin-to-tumor volume ratio combined with ultrasound features for diagnosing thyroid follicular neoplasms by Xixi Zhu, Xixi Zhu, Fen Liu, Jiaye Liu, Jiaye Liu, Zhihui Li, Yu Ma

“…The combined diagnostic model incorporating Tg/Vol ratio and CEUS features significantly improves FTC detection accuracy.…”
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A feature selection and scoring scheme for dimensionality reduction in a machine learning task by PHILEMON UTEN EMMOH, christopher ifeanyi Eke, Timothy Moses

“…It helps in reducing the dimensionality of a dataset and improving model performance. Most of the feature selection techniques have restriction in the kind of dataset to be used. …”
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Imbalanced feature generation based on bootstrap power spectral curve for estimating respiratory rate by Soojeong Lee, Gyanendra Prasad Joshi, Gangseong Lee

“…Hence, we use the parametric bootstrap model generated by artificial feature curves to estimate RR and solve this problem. …”
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An XAI Approach to Melanoma Diagnosis: Explaining the Output of Convolutional Neural Networks with Feature Injection by Flavia Grignaffini, Enrico De Santis, Fabrizio Frezza, Antonello Rizzi

“…In contrast, the Shapley additive explanations method was used to perform a feature importance analysis on the additional handcrafted information. …”
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An adaptive power system transient stability assessment method based on shared feature extraction by Jiexiang Hu, Le Zheng, Wei Ai, Yansong Li, Jun Liu, Xinglei Chen

“…This paper proposes a robust and transferable adaptive TSA method based on shared feature extraction of the power system. A domain adversarial alignment network is used to train a shared feature extractor, aligning data before and after system variations to capture critical stability features. …”
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EFTGAN: Elemental features and transferring corrected data augmentation for the study of high-entropy alloys by Yibo Sun, Cong Hou, Nguyen-Dung Tran, Yuhang Lu, Zimo Li, Ying Chen, Jun Ni

“…However, the complexity of computing material structures limits the practical use of these models. To address this challenge and improve prediction accuracy in small data sets, we develop a generative network framework: Elemental Features enhanced and Transferring corrected data augmentation in Generative Adversarial Networks (EFTGAN). …”
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Memory Efficient Local Features Descriptor for Identity Document Detection on Mobile and Embedded Devices by Daniil P. Matalov, Elena E. Limonova, Natalya S. Skoryukina, Vladimir V. Arlazarov

“…We train a binary descriptor using the retrieved dataset of patches, each bit of the descriptor relies on a single computationally-efficient feature. To estimate the influence of different feature spaces on the descriptor performance, we perform descriptor training experiments using gradient-based and intensity-based features. …”
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Multi‐scale feature extraction for energy‐efficient object detection in remote sensing images by Di Wu, Hongning Liu, Jiawei Xu, Fei Xie

“…At the core of MRTMDet is a new backbone network MobileViT++ and a feature fusion network SFC‐FPN, which enhances the model's ability to capture global and multi‐scale features by carefully designing a hybrid feature processing unit of CNN and a transformer based on vision transformer (ViT) and poly‐scale convolution (PSConv), respectively. …”
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