DAF-UNet: Deformable U-Net with Atrous-Convolution Feature Pyramid for Retinal Vessel Segmentation by Yongchao Duan, Rui Yang, Ming Zhao, Mingrui Qi, Sheng-Lung Peng

“…However, the inherent challenges posed by the complex geometries of vessels and the highly imbalanced distribution of thick versus thin vessel pixels demand innovative solutions for robust feature extraction. In this paper, we introduce DAF-UNet, a novel architecture that integrates advanced modules to address these challenges. …”
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Marine object detection in forward-looking sonar images via semantic-spatial feature enhancement by Zhen Wang, Zhen Wang, Jianxin Guo, Shanwen Zhang, Nan Xu

“…Specifically, we introduce the competitive coordinate attention mechanism (CCAM) and the spatial group enhance attention mechanism (SGEAM), both integrated into the backbone network to effectively capture semantic and spatial features within sonar images, while feature fusion is employed to suppress complex marine background noise. …”
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Multi-CNN Deep Feature Fusion and Stacking Ensemble Classifier for Breast Ultrasound Lesion Classification by Kemal PANÇ, Sümeyye SEKMEN

“…Conclusion: The proposed approach, combining multi-convolutional neural network deep feature fusion, optimized feature selection, and ensemble stacking, shows significant potential for automated breast ultrasound classification, especially for benign and normal cases. …”
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An IoT intrusion detection framework based on feature selection and large language models fine-tuning by Huan Ma, Wan Zhang, Dalong Zhang, Baozhan Chen

“…This algorithm utilizes the CMA-ES algorithm for feature search while also taking into account the mutual information and collinearity among features, thereby more effectively reducing redundancy features. …”
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AFDR-Det: Adaptive Feature Dual-Refinement Oriented Detector for Remote Sensing Object Detection by Jie Yang, Li Zhou, Yongfeng Ju

“…Additionally, most head networks use two branch networks to implement object localization and classification tasks separately, resulting in a lack of information interaction between the classification and localization tasks, leading to spatial feature misalignment issues. …”
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Vision Transformer Embedded Feature Fusion Model with Pre-Trained Transformers for Keratoconus Disease Classification by Md Fatin Ishrak, Md Maruf Rahman, Md Imran Kabir Joy, Anna Tamuly, Salma Akter, Dewan M. Tanim, Shahajada Jawar, Nayeem Ahmed, Md Sadekur Rahman

“…The primary objective of this research is to develop a feature fusion hybrid deep learning framework that integrates pretrained Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) for the automated classification of keratoconus into three distinct categories: Keratoconus, Normal, and Suspect. …”
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Advanced 3D Face Reconstruction from Single 2D Images Using Enhanced Adversarial Neural Networks and Graph Neural Networks by Mohamed Fathallah, Sherif Eletriby, Maazen Alsabaan, Mohamed I. Ibrahem, Gamal Farok

“…Key innovations include (1) a generator architecture based on Graph Convolutional Networks (GCNs) with a novel loss function and identity blocks, mitigating mode collapse and instability; (2) the integration of facial landmarks and a non-parametric efficient-net decoder for enhanced feature capture; and (3) a lightweight GCN-based discriminator for improved accuracy and stability. …”
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Graph neural network-tracker: a graph neural network-based multi-sensor fusion framework for robust unmanned aerial vehicle tracking by Karim Dabbabi, Tijeni Delleji

“…In this study, we propose graph neural network-tracker (GNN-tracker), a novel GNN-based UAV tracking framework that effectively integrates graph-based spatial-temporal modelling, Transformer-based feature extraction, and multi-sensor fusion to enhance tracking robustness and accuracy. …”
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Shared Genomic Features Between Lung Adenocarcinoma and Type 2 Diabetes: A Bioinformatics Study by Nuerbiye Nueraihemaiti, Dilihuma Dilimulati, Alhar Baishan, Sendaer Hailati, Nulibiya Maihemuti, Alifeiye Aikebaier, Yipaerguli Paerhati, Wenting Zhou

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Energy-efficient data routing using neuro-fuzzy based data routing mechanism for IoT-enabled WSNs by Sakthi Shunmuga Sundaram Paulraj, T. Deepa

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Novel feature extraction method for signal analysis based on independent component analysis and wavelet transform. by Mariusz Topolski, Jędrzej Kozal

“…Recently many approaches for signal feature extraction were created. However, plenty of proposed methods are based on convolutional neural networks. …”
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Radar-Based Activity Recognition in Strictly Privacy-Sensitive Settings Through Deep Feature Learning by Giovanni Diraco, Gabriele Rescio, Alessandro Leone

“…Deep learning models based on pre-trained feature extractors combined with bidirectional long short-term memory networks were employed for classification. …”
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Spectrogram-Based Arrhythmia Classification Using Three-Channel Deep Learning Model with Feature Fusion by Alaa Eleyan, Fatih Bayram, Gülden Eleyan

“…This spectrogram is further processed to generate a histogram of oriented gradients (HOG) and local binary pattern (LBP) features. Three separate 2D convolutional neural networks (CNNs) then analyze these three image representations in parallel. …”
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Who is WithMe? EEG features for attention in a visual task, with auditory and rhythmic support by Renata Turkeš, Steven Mortier, Jorg De Winne, Jorg De Winne, Dick Botteldooren, Paul Devos, Steven Latré, Tim Verdonck

“…The goal of this study is to investigate which EEG data representations or features are most closely linked to attention, and to what extent they can handle the cross-subject variability.MethodsWe explore the features obtained from the univariate time series from a single EEG channel, such as time domain features and recurrence plots, as well as representations obtained directly from the multivariate time series, such as global field power or functional brain networks. …”
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PRCFX-DT: a new graph-based approach for feature selection and classification of genomic sequences by Amin Khodaei, Sania Eskandari, Hadi Sharifi, Behzad Mozaffari-Tazehkand

“…Results This study proposes a novel approach that utilizes complex networks and probabilistic graph modeling methods to analyze viral genomic sequences for feature extraction. …”
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Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis by Daniel Nasef, Demarcus Nasef, Viola Sawiris, Peter Girgis, Milan Toma

“…The performance of various ML models, including decision trees, support vector machines, and neural networks, is evaluated using metrics such as accuracy, AUC, recall, precision, F1, Kappa, and MCC. …”
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The Mechanism of Formation of Extremist Attitudes in Minors by E. V. Vaske, O. I. Goryunova

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Research on the Application of Deep Learning Algorithm in the Damage Detection of Steel Structures by Qingyun Ge, Caimei Li, Fulian Yang

“…This study introduces a novel Convolutional Long Short-Term Memory (ConvLSTM) network for steel structure damage detection, aimed at enhancing the accuracy and reliability of structural health monitoring systems. …”
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Improved Convolutional Neural Networks for Course Teaching Quality Assessment by Yun Liu

“…In this paper, a separate long-term recursive convolutional network (SLRCN) microexpression recognition algorithm is proposed using deep learning technology for building a course teaching effectiveness evaluation model. …”
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Research progress of abnormal user detection technology in social network by Qiang QU, Hongtao YU, Ruiyang HUANG

“…In social networks,the problem of anomalous users detection is one of the key problems in network security research.The anomalous users conduct false comments,cyberbullying or cyberattacks by creating multiple vests,which seriously threaten the information security of normal users and the credit system of social networks ,so a large number of researchers conducted in-depth study of the issue.The research results of the issue in recent years were reviewed and an overall structure was summarized.The data collection layer introduces the data acquisition methods and related data sets,and the feature presentation layer expounds attribute features,content features,network features,activity features and auxiliary features.The algorithm selection layer introduces supervised algorithms,unsupervised algorithms and graph algorithms.The result evaluation layer elaborates the method of data annotation method and index.Finally,the future research direction in this field was looked forward.…”
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