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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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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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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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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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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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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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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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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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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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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Optimized driver fatigue detection method using multimodal neural networks by Shengli Cao, Peihua Feng, Wei Kang, Zeyi Chen, Bo Wang

“…Two advanced neural network models were developed and evaluated: a multimodal feature combination model and a multimodal feature coupled model. …”
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A Deep Learning Framework for the Classification of Brazilian Coins by Debabrata Swain, Viral Rupapara, Amro Nour, Santosh Satapathy, Biswaranjan Acharya, Shakti Mishra, Ali Bostani

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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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HGLFNet: Hybrid Global Semantic and Local Detail Feature Network for Lane Detection by Lei Ding, Chunhui Tang, Yi Fang

“…To address these challenges, this paper introduces a novel Hybrid Global Semantic and Local Detail Feature Network (HGLFNet), designed to enhance lane detection accuracy and robustness in complex scenarios. …”
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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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Hybrid Random Feature Selection and Recurrent Neural Network for Diabetes Prediction by Oyebayo Ridwan Olaniran, Aliu Omotayo Sikiru, Jeza Allohibi, Abdulmajeed Atiah Alharbi, Nada MohammedSaeed Alharbi

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Multi-feature Fusion Network for Classification of Pipeline Magnetic Leakage Signals by WEI Yuanyuan, LIU Ruiping, FU Shimo, WANG Yaoli

“…Finally, a multi-feature entropy weighting method was employed to allocate network weights on the basis of input feature entropy. …”
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LFEN: A language feature enhanced network for scene text recognition by Hui Chen, Runming Jiang, Fang Hu, Min Chen, Yin Zhang

“…Furthermore, by incorporating the intrinsic semantic relationships of text content, this paper employs a sequence-to-sequence (Seq2Seq) model based on convolutional neural networks for text correction. Through the integration of language information, different feature embeddings, and global residual connections, the paper provides a robust solution for text correction in scene text recognition. …”
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Feature enhanced cascading attention network for lightweight image super-resolution by Feng Huang, Hongwei Liu, Liqiong Chen, Ying Shen, Min Yu

“…Therefore, we propose a feature enhanced cascading attention network (FECAN) that introduces a novel feature enhanced cascading attention (FECA) mechanism, consisting of enhanced shuffle attention (ESA) and multi-scale large separable kernel attention (MLSKA). …”
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