CANini: In-Depth Traffic Analysis for Design and Robustness Evaluation of DTree-Based IDS in Automotive Networking Systems by Nicasio Canino, Pierpaolo Dini, Stefano Mazzetti, Daniele Rossi, Sergio Saponara

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Feature learning and generalization in deep networks with orthogonal weights by Hannah Day, Yonatan Kahn, Daniel A Roberts

“…We speculate that this structure preserves finite-width feature learning while reducing overall noise, thus improving both generalization and training speed in deep networks with depth comparable to width. …”
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Embedded feature selection using dual-network architecture by Abderrahim Abbassi, Arved Dörpinghaus, Niklas Römgens, Tanja Grießmann, Raimund Rolfes

“…However, existing methods often face challenges due to the complexity of feature interdependencies, uncertainty regarding the exact number of relevant features, and the need for hyperparameter optimization, which increases methodological complexity.This research proposes a novel dual-network architecture for feature selection that addresses these issues. …”
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Effects of feature selection and normalization on network intrusion detection by Mubarak Albarka Umar, Zhanfang Chen, Khaled Shuaib, Yan Liu

“…Furthermore, while feature selection benefits simpler algorithms (such as RF), normalization is more useful for complex algorithms like ANNs and deep neural networks (DNNs), and algorithms such as Naive Bayes are unsuitable for IDS modeling. …”
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A database to initiate methodological advances in the evaluation of transitivity assumption in network meta-analysis: qualitative features and limitations of the tracenma R package by Loukia M. Spineli, Andrés Mauricio García-Sierra, Juan Jose Yepes-Nuñez

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Adaptive feature interaction enhancement network for text classification by Rui Su, Shangbing Gao, Kefan Zhao, Junqiang Zhang

“…To address this issue, we propose an Adaptive Feature Interactive Enhancement Network (AFIENet). …”
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Application of deconvolutional networks for feature interpretability in epilepsy detection by Sihao Shao, Yu Zhou, Ruiheng Wu, Aiping Yang, Qiang Li

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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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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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Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection by Sangita Baruah, Vaskar Deka, Dulumani Das, Utpal Barman, Manob Jyoti Saikia

“…Employing differential evolution, we propose a feature selection approach that enhances the ability to discern peer-to-peer (P2P) botnet traffic amidst evolving cyber threats. …”
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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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Identification and Evolution of Core Technologies in the Chip Field Based on Patent Networks by Ying Wang, Renda Chen, Jindong Chen

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Collective variables of neural networks: empirical time evolution and scaling laws by Samuel Tovey, Sven Krippendorf , Michael Spannowsky, Konstantin Nikolaou, Christian Holm

“…Due to the ubiquity of the latter in deep neural network architectures and its flexibility in the creation of feature-rich representations, we argue that this network entropy evolution be considered the onset of a deep learning regime.…”
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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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Time Series Forecasting Based on Temporal Networks Evolution and Dynamic Constraints by Yunlong Peng, Han Li, Xu Han

“…Subsequently, leveraging the unique evolutionary patterns of temporal networks, we employ matrix evolution to predict the topological structure of the network at the next time step. …”
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Limitations of gene duplication models: evolution of modules in protein interaction networks. by Frank Emmert-Streib

“…This observation reveals our incomplete understanding of the structural evolution of protein networks on the module level.…”
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Comprehensive Evaluation of Techniques for Intelligent Chatter Detection in Micro-Milling Processes by Guilherme Serpa Sestito, Wesley Angelino De Souza, Alessandro Roger Rodrigues, Maira Martins Da Silva

“…This work proposed using feature selection to evaluate the impact of several statistical features on the performance of ML classifiers for chatter detection during micro-milling operations, compare them to the performance of the Convolutional Neural Network algorithm, and discuss the employability of the techniques on the STM32F446RE microcontroller. …”
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Feature Interaction and Adaptive Fusion Network With Spectral Modulation for Pansharpening by Lihua Jian, Jiabo Liu, Lihui Chen, Di Zhang, Gemine Vivone, Xichuan Zhou

“…This article introduces a feature interaction and adaptive fusion network (FIAFN) with spectral modulation for pansharpening to address these issues. …”
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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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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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