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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An optimized ensemble model with advanced feature selection for network intrusion detection by Afaq Ahmed, Muhammad Asim, Irshad Ullah, Zainulabidin, Abdelhamied A. Ateya

“…In today’s digital era, advancements in technology have led to unparalleled levels of connectivity, but have also brought forth a new wave of cyber threats. Network Intrusion Detection Systems (NIDS) are crucial for ensuring the security and integrity of networked systems by identifying and mitigating unauthorized access and malicious activities. …”
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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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Deep blur detection network with boundary-aware multi-scale features by Xiaoli Sun, Qiwei Wang, Xiujun Zhang, Chen Xu, Weiqiang Zhang

“…To solve this problem, we newly establish a boundary-aware multi-scale deep network in this paper. First, the VGG-16 network is used to extract the deep features from multi-scale layers. …”
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Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs by Jing Li, Wei Zong, Yang-Wai Chow, Willy Susilo

“…Network Intrusion Detection Systems (NIDS) often suffer from severe class imbalance, where minority attack types are underrepresented, leading to degraded detection performance. …”
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Power Metal Corrosion Evaluation Method Based on Image Feature Analysis by ZHONG Yao, ＲEN Xiao, WU Gao-lin, WANG Qian, WANG Xu-peng, HAO Jian

“…First，multi-dimensional feature parameters were extracted through image preprocessing，chromatics，statistics， wavelet and fractal analysis methods; then，a metal corrosion state evaluation method was proposed based on neural network algorithm，and the effectiveness of the method was verified. …”
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Agricultural Cultivation Cost Prediction Using Neural Networks and Feature Importance Analysis by Salmania Putri, Tora Fahrudin, Asti Widayanti

“…This research aims to achieve high productivity in the agricultural sector by using neural networks or Deep Learning methods to predict the cost of agricultural cultivation, as well as identifying significant factors that affect the profitability of potato commodities with Feature Importance analysis. …”
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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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Enhancing Text Similarity Measurement with Hybrid Siamese Neural Networks and Lexical Features by Bei Zhou

“…Evaluation across three distinct datasets demonstrates the superiority of the hybrid Siamese neural network model, leveraging convolutional networks and lexical features, showcasing higher Pearson's correlation and lower mean square errors (MSE) compared to literature models. …”
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Liver segmentation network based on detail enhancement and multi-scale feature fusion by Lu Tinglan, Qin Jun, Qin Guihe, Shi Weili, Zhang Wentao

“…Through the aforementioned research, this paper proposes a liver segmentation network based on detail enhancement and multi-scale feature fusion (DEMF-Net). …”
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Re-Calibrating Network by Refining Initial Features Through Generative Gradient Regularization by Naim Reza, Ho Yub Jung

“…The experiments show that implementing this method on a pre-trained network effectively re-calibrates the network and augments higher variance filters of the initial layer of the network, which helps produce refined features. …”
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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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A Lightweight Tri-Stream Feature Fusion Network for Speech Emotion Recognition by Ronghe Cao, Yunxing Wang, Xiaolong Wu, Shuang Jin, Huiling Niu

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TFNet: point cloud Semantic Segmentation Network based on Triple feature extraction by Yong Li, Falin Chen, Qi Lin, Zhen Li, Dongxu Gao, Jingchao Yang

“…To address these challenges, we propose TFNet, an end-to-end deep neural network specifically designed to enhance local geometric feature extraction and improve performance on density variations. …”
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Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion by Yuhang Yang, Yuanqing Luo, Yingyu Yang, Shuang Kang

“…In this paper, we introduce a novel garbage classification approach that leverages a dynamic graph neural network based on multimodal feature fusion. Specifically, the proposed method employs an enhanced Residual Network Attention Module (RNAM) network to capture deep semantic features and utilizes CIELAB color (LAB) histograms to extract color distribution characteristics, achieving a complementary integration of multimodal information. …”
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Survey on network system security metrics by Chensi WU, Weiqiang XIE, Yixiao JI, Su YANG, Ziyi JIA, Song ZHAO, Yuqing ZHANG

“…With the improvement for comprehensive and objective understanding of the network system,the research and application of network system security metrics (NSSM) are noticed more.The quantitative evaluation of network system security is developing towards precision and objectification.NSSM can provide the objective and scientific basis for the confrontation of attack-defense and decision of emergency response.The global metrics of network system security is a crucial point in the field of security metrics.From the perspective of global metrics,the status and role of global metrics in security evaluation were pointed out.Three development stages of metrics (perceiving,cognizing and deepening) and their characteristics were analyzed and summarized.The process of global metrics was described.The metrics models,metrics systems and metrics tools were analyzed,and their functions,interrelations,and features in security metrics were pointed out.Then the technical challenges of global metrics of network systems were explained in detail,and ten opportunities and challenges were summarized in tabular form.Finally,the next direction and development trend of network system security metrics research were forecasted.The survey shows that NSSM has a good application prospect in network security.…”
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HFF-Net: A hybrid convolutional neural network for diabetic retinopathy screening and grading by Muhammad Hassaan Ashraf, Hamed Alghamdi

“…HFF-Net extracts multiscale features that fused at multiple levels within the network, utilizing the swish activation function for improved learning stability. …”
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