GeoAT: Geometry-Aware Attention Feature Matching Network by Yan Li, Yingdan Wu, Yang Ming, Yong Zhang, Zhesheng Cheng

“…This method leverages low-resolution image features to obtain global geometric constraint information between images and uses an affine transformation matrix to guide the subsequent attention computation on high-resolution features, achieving efficient and accurate matching in a coarse-to-fine manner. …”
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Exploring New Dimensions of Archives: Finding Audiovisually Similar Programmes with the Help of Neural Networks by Sara Veldhoen, Rana Klein, Mari Wigham, Roeland Ordelman, Teng Long, Nanne van Noord

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Explainable Spatio-Temporal Inference Network for Car-Sharing Demand Prediction by Nihad Brahimi, Huaping Zhang, Zahid Razzaq

“…Predicting vehicle demand is challenging due to the interconnections of temporal, spatial, and spatio-temporal features. This paper presents the Explainable Spatio-Temporal Inference Network (eX-STIN), a new approach that improves upon our prior Unified Spatio-Temporal Inference Prediction Network (USTIN) model. …”
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An Evaluation of Skin Lesion Segmentation Using Deep Learning Architectures by Rabia Öztaş Kara, Devrim Akgün, Bekir Murat Aydın, Gökçen Çetinel, Sevda Gül

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A Multi-Path Feature Extraction and Transformer Feature Enhancement DEM Super-Resolution Reconstruction Network by Mingqiang Guo, Feng Xiong, Ying Huang, Zhizheng Zhang, Jiaming Zhang

“…The network structure has three parts: feature extraction, image reconstruction, and feature enhancement. …”
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Hyperspectral Attention Network for Object Tracking by Shuangjiang Yu, Jianjun Ni, Shuai Fu, Tao Qu

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DOA Estimation by Feature Extraction Based on Parallel Deep Neural Networks and MRMR Feature Selection Algorithm by Ashwaq Neaman Hassan Al-Tameemi, Mahmood Mohassel Feghhi, Behzad Mozaffari Tazehkand

“…In parallel, the proposed model extracts spatial and temporal features using a convolution neural network (CNN) and long short-term memory (LSTM). …”
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Spoofed Speech Detection with Weighted Phase Features and Convolutional Networks by Gökay Dişken

“…The extracted features are then fed to a convolutional neural network as input. …”
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Grape Leaf Diseases Identification System Using Convolutional Neural Networks and LoRa Technology by Zinon Zinonos, Socratis Gkelios, Ala F. Khalifeh, Diofantos G. Hadjimitsis, Yiannis S. Boutalis, Savvas A. Chatzichristofis

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Hybrid feature learning framework for the classification of encrypted network traffic by S. Ramraj, G. Usha

“…The focus of this research is to evaluate the performance of the Support Vector Machine (SVM) in classifying network packets by application type, as well as classifying the type of data communicated within an application. …”
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Formation Features of the Customer Segments for the Network Organizations in the Smart Era by Elena V. Yaroshenko

“…This purpose has defined the statement and the solution of the following tasks: to explore characteristic features of the network forms of the organization of economic activity of the companies, their prospects, Smart technologies’ influence on them; to reveal the work importance with different client profiles; to explore the existing methods and tools of formation of key customer segments; to define criteria for selection of key groups; to reveal the characteristics of customer segments’ formation for the network organizations.In the research process, methods of the system analysis, a method of analogies, methods of generalizations, a method of the expert evaluations, methods of classification and clustering were applied.This paper explores the characteristics and principles of functioning of network organizations, the appearance of which is directly linked with the development of Smart society. …”
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SLFCNet: an ultra-lightweight and efficient strawberry feature classification network by Wenchao Xu, Yangxu Wang, Jiahao Yang

“…Methods In this study, we have developed a lightweight model capable of real-time detection and classification of strawberry fruit, named the Strawberry Lightweight Feature Classify Network (SLFCNet). This innovative system incorporates a lightweight encoder and a self-designed feature extraction module called the Combined Convolutional Concatenation and Sequential Convolutional (C3SC). …”
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The influence of correlated features on neural network attribution methods in geoscience by Evan Krell, Antonios Mamalakis, Scott A. King, Philippe Tissot, Imme Ebert-Uphoff

“…Correlated features may also cause inaccurate attributions because XAI methods typically evaluate isolated features, whereas networks learn multifeature patterns. …”
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Software Defect Prediction through Neural Network and Feature Selections by Mutasem Shabeb Alkhasawneh

“…To predict the software defect, this study proposed a model consisting of feature selection and classifications. The correlation base method was used for feature selection, and radial base function neural network (RBF) was used for classification. …”
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Structural Evaluation for Distribution Networks with Distributed Generation Based on Complex Network by Fei Xue, Yingyu Xu, Huaiying Zhu, Shaofeng Lu, Tao Huang, Jinling Zhang

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Evaluating the Performance of Machine Learning Classifiers for Network Intrusion Detection: A Comparative Study Using the UNSW-NB15 Dataset by Iwan Handoyo Putro

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Comprehensive Style Transfer for Facial Images Using Enhanced Feature Attribution in Generative Adversarial Nets by Yongseon Yoo, Seonggyu Kim, Jong-Min Lee

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Aggregated Time Series Features in a Voxel-Based Network Architecture by Zsolt Vincze, Andras Rovid

“…During the evaluation, the authors examine the object detection performance of a popular voxel-based neural network with its original architecture and several variants where the time domain related features were propagated through the network and aggregated at different stages of processing. …”
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Image denoising based on deep feature fusion and U-Net network by Yong Zhang

“…Therefore, we propose a novel image denoising method based on deep feature fusion and U-Net network. This new method uses a two-branch U-Net network to fuse features and preserve image texture. …”
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Graph Attention Neural Network Model With Behavior Features for Knowledge Tracking by Wei Zhang, Sen Hu, Kaiyuan Qu

“…In order to solve the above problems, a graph attention neural network model with behavior features for knowledge tracking (GAKT-BF) is proposed in this paper. …”
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