Feature Extraction Model of SE-CMT Semantic Information Supplement by DU Ruishan, ZHOU Changkun, XIE Hongtao, LI Hongjie

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Critical evaluation of feature importance assessment in FFNN-based models for predicting Kamlet-Taft parameters by Yoshiyasu Takefuji

“…Mohan et al. developed a feed-forward neural network (FFNN) model to predict Kamlet-Taft parameters using quantum chemically derived features, achieving notable predictive accuracy. …”
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Deep Time Series Intelligent Framework for Power Data Asset Evaluation by Lihong Ge, Xin Li, Li Wang, Jian Wei, Bo Huang

“…In the evaluation of the complex and rich Solar-Power dataset and Electricity dataset, TSENet achieved significant performance improvements over other state-of-the-art baseline methods.Through the synergistic design of deep convolutional structures and an efficient memory mechanism, it effectively addresses issues such as inadequate modeling of long-term dependencies, insufficient extraction of short-term features, and high prediction volatility, thereby significantly enhancing both the accuracy and robustness of forecasting in power asset evaluation tasks.…”
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RFID Privacy Risk Evaluation Based on Synthetic Method of Extended Attack Tree and Information Feature Entropy by Peng Li, Chao Xu, Long Chen, Ruchuan Wang

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Aerial–Terrestrial Image Feature Matching: An Evaluation of Recent Deep Learning Methods by Hui Wang, Jiangxue Yu, San Jiang, Dejin Zhang, Qingquan Li

“…However, their performance in handling challenging large-angle aerial–terrestrial datasets still needs to be evaluated. To assess their performance for aerial–terrestrial images, this study has reviewed and evaluated four types of recent deep-learning-based feature matching networks and selected four sets of aerial–terrestrial datasets for experimental tests. …”
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Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion by Xiaoshuo Cui, Xiaoxue Sun, Shuxin Xuan, Jinyu Liu, Dongfang Zhang, Jun Zhang, Xiaofei Fan, Xuesong Suo

“…However, few studies have used spectral image information to predict and evaluate the shelf life of broccoli. In this study, multispectral imaging combined with multi-feature data fusion was used to predict and evaluate the shelf life of broccoli. …”
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Extraction of Concealed Features From RF-EMF Monitoring at Kindergartens and Schools by Nikola Djuric, Dragan Kljajic, Nicola Pasquino, Vidak Otasevic, Snezana Djuric

“…The analysis is performed on a case study of EMF-sensitive areas in the Serbian city of Novi Sad, i.e., two kindergartens and an elementary school, revealing some of the concealed features in the behavior of the EMFs exposure in those areas, through a comparative evaluation.…”
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INTEVAL as a Positively Charged Social Network by Per Bastøe, Anita Haslie

“… Background: In this article Bastøe and Haslie seeks to employ theoretical insights from two connected bodies of literature to understand the unique characteristics of The International Evaluation Research Group (Inteval). One perspective draws on insights from social network theories about how some networks are supportive and innovative while others are not. …”
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Time Series Forecasting Model Based on the Adapted Transformer Neural Network and FFT-Based Features Extraction by Kyrylo Yemets, Ivan Izonin, Ivanna Dronyuk

“…In today’s data-driven world, where information is one of the most valuable resources, forecasting the behavior of time series, collected by modern sensor networks and IoT systems, is crucial across various fields, including finance, climatology, and engineering. …”
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PolSAR image classification using shallow to deep feature fusion network with complex valued attention by Mohammed Q. Alkhatib, M. Sami Zitouni, Mina Al-Saad, Nour Aburaed, Hussain Al-Ahmad

“…Deep Learning (DL) methods offer effective solutions for overcoming these challenges in PolSAR feature extraction. Convolutional Neural Networks (CNNs) play a crucial role in capturing PolSAR image characteristics by exploiting kernel capabilities to consider local information and the complex-valued nature of PolSAR data. …”
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FEPA-Net: A Building Extraction Network Based on Fusing the Feature Extraction and Position Attention Module by Yuexin Liu, Yulin Duan, Xuya Zhang, Wen Zhang, Chang Wang

“…In this paper, we propose the FEPA-Net network model, which integrates the feature extraction and position attention module for the extraction of buildings in remote sensing images. …”
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Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network by Shengwei Qin, Yao Jin, Hailong Hu

“…The first module, called the feature enhancement module (FEM), aims to prevent feature blurring. …”
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Coalmine image super-resolution reconstruction via fusing multi-dimensional feature and residual attention network by Jian CHENG, Lifei MI, Hao LI, Heping LI, Guangfu WANG, Yongzhuang MA

“…To address issues such as the loss of edge texture information and blurring of details in coalmine images, a coalmine image super-resolution reconstruction method integrating multi-dimensional features and residual attention networks is proposed. …”
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Bayesian Optimized ANFIS Network Using Grid Partition and Feature Spectrum for Urban Light Pollution Assessment by Nuoqi Wang, Cheng Wang, Zhihong Zhao, Peiyu Wu, Wenqian Xu, Bang Qin, Dong Wang, Rongjun Zhang, Qi Yao

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A Lightweight Intrusion Detection System with Dynamic Feature Fusion Federated Learning for Vehicular Network Security by Junjun Li, Yanyan Ma, Jiahui Bai, Congming Chen, Tingting Xu, Chi Ding

“…To solve these problems, a lightweight vehicular network intrusion detection framework based on Dynamic Feature Fusion Federated Learning (DFF-FL) is proposed. …”
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Anomaly Detection in Industrial Machine Sounds Using High-Frequency Features and Gate Recurrent Unit Networks by Thi-Thu-Huong Le, Andro Aprila Adiputra, Jiwon Yun, Howon Kim

“…Experimental results demonstrate that eXtreme Gradient Boosting (XGBoost) outperforms Support Vector Machine (SVM) and Decision Tree (DT) models in the ML approach across both feature sets. In the DL approach, Gated Recurrent Units (GRU) perform better on MFCC features than Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. …”
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Internet traffic classification method based on behavior feature learning by Zhen LIU, Ruoyu WANG

“…The connection graph based internet traffic classification method can reflect the connectivity behavior between hosts.Thus，it has high stability.But the heuristic rules summarized for traffic classification are generally incomplete，and they difficultly obtain high classification accuracy.Host communication behavior model and BOF method was researched，and a set of host connection related behavior features （HCBF）was extracted from the multiple flows with the same ｛destination IP，destination port and transport protocol｝.To evaluate the performance of HCBF，it was compared with the existing feature set on the respect of basic classification performance and classification stability.The experiments were carried out on the traffic collected in the traditional and mobile networks.Results show that HCBF out performs existing feature sets.…”
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Study of the Faults Network of Kunashir Island (Kuril Islands) in Order to Reconstruct the Features of its Tectonic Evolution by Krikun Nikita Sergeevich, Abdrahmanov Il'nur, Talovina Irina

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Network intrusion detection model using wrapper based feature selection and multi head attention transformers by Muhammad Umer, Muhammad Tahir, Muhammad Sardaraz, Muhammad Sharif, Hela Elmannai, Abeer D. Algarni

“…Accuracy, Precision, recall, and F-1 score are used to evaluate the model. The proposed model improves the accuracy of intrusion detection by selecting the most relevant features while reducing the feature space. …”
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Point rotation invariant features and attention fusion network for point cloud registration of 3D shapes by Zeyang Liu, Zhiguo Lu, Yancong Shan

“…This paper introduces a novel learning-based registration method, titled Point Rotation Invariant Feature and Attention Fusion Network (PRIF), specifically tailored for point cloud registration tasks. …”
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