Steganographer identification of JPEG image based on feature selection and graph convolutional representation by Qianqian ZHANG, Yi ZHANG, Hao LI, Yuanyuan MA, Xiangyang LUO

“…Aiming at the problem that the feature dimension of JPEG image steganalysis is too high, which leads to the complexity of distance calculation between users and a decrease in the identification performance of the steganographer, a method for steganographer recognition based on feature selection and graph convolutional representation was proposed.Firstly, the steganalysis features of the user’s images were extracted, and the feature subset with highseparability was selected.Then, the users were represented as a graph, and the features of users were obtained by training the graph convolutional neural network.Finally, because inter-class separability and intra-class aggregation were considered, the features of users that could capture the differences between users were learned.For steganographers who use JPEG steganography, such as nsF5, UED, J-UNIWARD, and so on, to embed secret information in images, the proposed method can reduce the feature dimensions and computing.The identification accuracy of various payloads can reach more than 80.4%, and it has an obvious advantage at the low payload.…”
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Integrating Correlation-Based Feature Selection and Clustering for Improved Cardiovascular Disease Diagnosis by Agnieszka Wosiak, Danuta Zakrzewska

“…Based on the growing problem of heart diseases, their efficient diagnosis is of great importance to the modern world. …”
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Analysis of super-long and sparse feature in pseudo-random sequence based on similarity by Chun-jie CAO, Jing-zhang SUN, Zhi-qiang ZHANG, Long-juan WANG, Meng-xing HUANG

“…Similarity analysis of pseudo-random sequence in wireless communication networks is a research hotspot problem in the domain of information warfare.Based on the difficulties in super-long sequence,extremely sparse feature,and futilities in engineering application for real-time processing exist in similarity analysis of sequence in wireless net-work,a method of similarity analysis of sequence in a certain margin of misacceptance probability was proposed.Firstly,the similarity probability distribution of real-random sequence was theoretically analyzed.Secondly,according to the standard of NIST SP 800-22,the randomness of pseudo-bitstream was analyzed and the validity of pseudo-bitstream was judged.Finally,similarity was analyzed and verified by combining super-long pseudo-random sequence in real wireless communication networks.The results indicate that the lower bound of similarity value is 0.62 when misacceptance prob-ability uncertainty at about 1%.Above conclusion is considerable importance from the significance and theoretical values in network security domains,such as protocol analysis,traffic analysis,intrusion detection and others.…”
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A Deep Modelling Method for Bearing Faults Incorporating Multi-domain Features by NIU Guojun, TANG Zhenhao, WANG Mengjiao

“…In order to solve this problem in bearing fault diagnosis, this study proposes a bearing fault depth modeling method based on time-frequency domain feature extraction. …”
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Online LiDAR-Camera Extrinsic Calibration Using Selected Semantic Features by Ping-Tzu Lin, Ying-Shiuan Huang, Wen-Chieh Lin, Chieh-Chih Wang, Huei-Yung Lin

“…Our method could effectively ameliorate the problem of targetless methods which usually lack robust features and the correspondences. …”
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RESEARCH ON ROLLING BEARING FAULT FEATURE EXTRACTION METHOD WITH SGMD-MOMEDA (MT) by CAO YaLei, DU YingJun, WEI Guang, DONG XinMin, GAO LiPeng, LIU YuXi

“…Aiming at the problem that the vibration signal of rolling bearing is difficult to extract due to the characteristics of non-linear, non-stationary and low signal-to-noise ratio, a new fault extraction method based on symplectic geometry mode decomposition（SGMD） and multipoint optimal minimum entropy deconvolution adjusted（MOMEDA） theory is proposed. …”
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Prediction Model of Material Properties Based on Feature Fusion and Convolutional Neural Network by SHI Jingchen, LIU Feining, WANG Wenjie, ZHAO Rui

“…Aiming at the problem that most machine learning models need a lot of prior knowledge and manual selection of feature vectors in the prediction of material properties, a convolutional neural network model OPCNN (Orbital of Electron and Periodic table CNN) is established by feature fusion based on two descriptors, electronic orbit matrix and periodic table method. …”
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A feature-based intelligent deduplication compression system with extreme resemblance detection by Xiaotong Wu, Jiaquan Gao, Genlin Ji, Taotao Wu, Yuan Tian, Najla Al-Nabhan

“…In this paper, we study the problem of utilising the duplicate and resemblance detection techniques to further compress data. …”
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Deconstructability prediction for building using machine learning and ensemble feature selection techniques by Habeeb Balogun, Hafiz Alaka, Eren Demir, Christian Nnaemeka Egwim, Godoyon Ebenezer Wusu, Wasiu Yusuf, Muideen Adegoke, Iqbal Qasim

“…A deconstructability predictive model using a machine learning-based model and ensemble feature selection techniques was developed to tackle this problem. …”
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The Graph Attention Recommendation Method for Enhancing User Features Based on Knowledge Graphs by Hui Wang, Qin Li, Huilan Luo, Yanfei Tang

“…Knowledge graphs have shown great potential in alleviating the data sparsity problem in recommendation systems. However, existing graph-attention-based recommendation methods primarily focus on user–item–entity interactions, overlooking potential relationships between users while introducing noisy entities and redundant high-order information. …”
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Multi-Scale Feature Similarity and Object Detection for Small Printing Defects Detection by Haojie Lou, Yuanlin Zheng, Wenqian Chen, Haiwen Liu

“…It is always a challenging task in the industry to detect the small printing defects under complex background. To address this problem, a defect detection algorithm based on multi-scale feature similarity evaluation and small object defect detection is proposed. …”
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Generative adversarial networks for generating synthetic features for Wi-Fi signal quality. by Mauro Castelli, Luca Manzoni, Tatiane Espindola, Aleš Popovič, Andrea De Lorenzo

“…We chose the WGAN as the final model, but both models are suitable for addressing the problem at hand.…”
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Ship detection optimization method in SAR imagery based on multi-feature weighting by Quanhua ZHAO, Xiao WANG, Yu LI, Guanghui WANG

“…Aiming at the problem that the accuracy of traditional ship detection algorithms is not satisfying in complex scene with many false alarm targets,a ship detection optimization method in SAR imagery based on multi-feature weighting was proposed.Firstly,the marker-based watershed algorithm was employed to remove land from SAR amplitude image.Then,the CFAR algorithm based on log-normal distribution was used to obtain candidate targets from no land image.Furthermore,the length to width ratio,the ship area and the contrast ratio of the candidate targets were extracted.Finally,a variance coefficient method was proposed to distribute the weight of the three features,and the confidence levels were calculated by combining the normalized feature vectors of the candidate targets with the feature weight.By determining the best confidence level,false alarm targets among the candidate targets were removed to optimize ship detection results.In order to verify the proposed method,experiments were carried on with the GF-3 SAR images of different complex scenes.The experimental results show that the proposed method is feasible and effective.…”
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Multilevel Feature Fusion-Based GCN for Rumor Detection with Topic Relevance Mining by Shenyu Chen, Meng Li, Weifeng Yang

“…This paper addresses the problem of detecting internet rumors in social media. …”
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Features of Manufacturing the Element Base of High-Temperature Electronics Using Laser Radiation by E. В. Shershnev

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Fault diagnosis algorithm based on multi-channel neighbor feature convolutional network by Huang Xiao, Hanqing Jian

“…First, to mitigate the covariate shift problem in the data, inverted mel-scale frequency cepstral coefficients are introduced to obtain domain-invariant features with high recognition accuracy. …”
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Thinking Styles Inventory: Psychometric features in college students from Buenos Aires by Agustín Freiberg Hoffmann, Fabiana Uriel, Rocío Fernández Da Lama, Mercedes Fernández Liporace

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Re-Identification of Amur Tigers by Combining Multi-branch and Multi-granularity Features by LI Xiao nan, ZHU Meng, REN Hong e, TAO Rui

“…In order to solve the problem of insufficient detailed feature extraction in the re-identification of the Amur tiger, a re- identification model of the Amur tiger, CMM-NET, was proposed, which combined multi-branch and multi-granularity features. …”
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Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications by Duarte Coelho, Ana Madureira, Ivo Pereira, Ramiro Gonçalves, Susana Nicola, Inês César, Daniel Alves de Oliveira

“…This work presents a cost-effective proposal for feature selection, which is a crucial part of machine learning processes, and intends to partly solve this problem through computational time reduction. …”
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A composite Feature Selection Method to improve Classifying Imbalanced Big Data by Shaymaa Razoqi, Ghayda Al-Talib

“…Therefore, this research proposed a composed feature selection method using the filter feature selection technique and permutation-based important features with the ensemble learning method. …”
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