Article
Computer Science, Artificial Intelligence
Ruizhi Zhou, Lingfeng Niu, Hong Yang
Summary: The paper introduces a new unsupervised feature selection method for attributed graphs based on regularized sparse learning, utilizing pseudo class labels to learn the interdependency between link and content information, with a new regularization term designed for learning link information.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jianyu Miao, Tiejun Yang, Chao Fan, Zhensong Chen, Xuan Fei, Xuchan Ju, Ke Wang, Mingliang Xu
Summary: This paper proposes a novel unsupervised feature selection method called self-paced analysis-synthesis dictionary learning (SPASDL), which integrates dictionary learning and self-paced learning. The method overcomes the limitations of existing methods in feature selection process. Experimental results demonstrate the effectiveness and superiority of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Zihao Song, Peng Song
Summary: Feature selection is a fundamental and challenging topic in machine learning and pattern recognition, and unsupervised feature selection methods have received extensive attention. In this article, a novel latent energy preserving embedding method is proposed for unsupervised feature selection, which utilizes self-representation learning strategy and graph Laplacian for mining manifold information and selects features using l(2,1)-norm. Extensive experiments on real-world datasets validate the effectiveness of the proposed method.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Mengshi Huang, Hongmei Chen, Yong Mi, Chuan Luo, Shi-Jinn Horng, Tianrui Li
Summary: In this paper, a minimum-redundant unsupervised feature selection (UFS) approach, called SLRDR, is proposed to address the problems by combining sparse latent representation learning and dual manifold regularization. The proposed approach learns a subspace of latent representation and pseudo-label matrix in the high-quality latent space, and utilizes manifold learning and sparse regression to select more discriminative features. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed approach.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Mechanical
Junjiang Liu, Baijie Qiao, Yanan Wang, Weifeng He, Xuefeng Chen
Summary: In this paper, a non-convex sparse regularization method is proposed to promote sparsity and improve solution accuracy. The Alternating Direction Method of Multipliers (ADMM) algorithm is used to solve the non-convex optimization problem. The proposed method outperforms the standard e'1 regularization in both localization accuracy and time-history reconstruction accuracy.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Saeed Karami, Farid Saberi-Movahed, Prayag Tiwari, Pekka Marttinen, Sahar Vahdati
Summary: Subspace distance is a valuable tool used in various feature selection methods. It can identify a representative subspace that efficiently approximates the original feature space. However, most existing methods based on subspace distance have limitations in achieving this objective.
Article
Computer Science, Artificial Intelligence
Hyunki Lim, Dae-Won Kim
Summary: This study introduces a new unsupervised feature selection method called DUFS, which selects a small feature set by considering pairwise dependence of features and eliminating redundant features. Experimental results show that the proposed method outperforms existing unsupervised feature selection methods in most cases.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Interdisciplinary Applications
Hongchun Qu, Yangqi Zheng, Lin Li, Fei Guo
Summary: An unsupervised feature extraction algorithm called block diagonal projection (BDP) is proposed, which imposes L2,1 norm constraint on the projection matrix to achieve row sparsity. This algorithm improves interpretability and feature extraction performance.
Article
Mathematics, Applied
Jinlan Li, Zhaoyang Xie, Guoqi Liu, Liu Yang, Jian Zou
Summary: In this paper, a convex-nonconvex graph total variation (CNC-GTV) regularization method is proposed for diffuse optical tomography (DOT) reconstruction. By combining the powerful representation ability of graph and the edge-preserving ability of total variation (TV) regularization, this method solves the issue of underestimating large edge values that classical TV regularization tends to have. The global convexity of the objective function is guaranteed by adjusting the nonconvex control parameters, and an alternating direction multiplier method (ADMM) is used to solve the proposed DOT reconstruction model. Numerical experiments demonstrate the superior performance of the proposed model in terms of visual and numerical results.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2023)
Article
Biology
Jiajia Li, Faming Xu, Na Gao, Yuanqiang Zhu, Yuewen Hao, Chen Qiao
Summary: This paper proposes a sparse non-convex based explainable deep belief network model, which combines non-convex sparsity learning with deep belief networks to achieve better model performance and interpretability in medical image analysis.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Jianyu Miao, Tiejun Yang, Lijun Sun, Xuan Fei, Lingfeng Niu, Yong Shi
Summary: Unsupervised Feature Selection (UFS) has gained popularity for improving learning performance and reducing computational costs. This paper proposes a novel UFS approach GLLE, integrating local linear embedding and manifold regularization in the feature subspace, achieving more promising results on real-world benchmark datasets.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Shixuan Zhou, Peng Song, Yanwei Yu, Wenming Zheng
Summary: Multi-view unsupervised feature selection (MUFS) is a popular research topic that aims to select a compact representative feature subset from multi-view data. However, most existing MUFS methods overlook the discriminative ability of multi-view data. This paper proposes a novel MUFS method called structural regularization based discriminative multi-view unsupervised feature selection (SDFS), which addresses these limitations and outperforms state-of-the-art MUFS models.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ronghua Shang, Jiarui Kong, Jie Feng, Licheng Jiao
Summary: This paper proposes a feature selection method NLRL-LE, which utilizes non-convex constraint and latent representation learning to improve feature selection by incorporating more complete information. Experimental results demonstrate that NLRL-LE outperforms seven other algorithms on twelve datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Ronghua Shang, Lujuan Wang, Fanhua Shang, Licheng Jiao, Yangyang Li
Summary: The DSLRL algorithm leverages internal association information in data space and feature space for guiding feature selection. In the absence of label information, it optimizes a low-dimensional latent representation matrix of data space to provide clustering indicators, and uses non-negative and orthogonal conditions to constrain the sparse transform matrix for more accurate feature evaluation.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Information Systems
Qiang Lin, Min Men, Liran Yang, Ping Zhong
Summary: This paper presents a novel supervised multi-view feature selection method based on locally sparse regularization and block computing. By dividing the multi-view dataset into sub-blocks and utilizing ADMM, a sharing sub-model is proposed for feature selection on each class. The proposed method outperforms several state-of-the-art feature selection methods in terms of classification accuracy and training speed.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Yange Chen, Yuan Ping, Zhili Zhang, Baocang Wang, SuYu He
Summary: A novel privacy-preserving image multi-classification deep-learning (PIDL) model is presented in this paper, with two schemes proposed that adopt secure calculation protocols applied in a fog control center (FCC) with a non-colluding honest server to protect data and model privacy in robot systems. The proposed schemes realize security, correctness, and efficiency with low communication and computational costs, as demonstrated in security analysis and performance evaluation.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Yange Chen, Hequn Liu, Baocang Wang, Baljinnyam Sonompil, Yuan Ping, Zhili Zhang
Summary: This paper proposes a new threshold hybrid encryption for integrity auditing method in cloud storage, using AES and ECC with Shamir secret sharing. The method distributes and manages keys without a trusted center, and also includes an integrity auditing and re-signature method to solve collusion issues. Security analysis and performance evaluation show that the scheme achieves correctness, security, and efficiency with low communication and computation cost.
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Hao Liu, Chun Guo, Yunhe Cui, Guowei Shen, Yuan Ping
Summary: This article proposes a 2-stage packer identification method based on function call graph (FCG) and file attributes, achieving high accuracy in malware detection. Analysis of FCG and file attributes differences and experimental results show the effectiveness of the 2-SPIFF method.
APPLIED INTELLIGENCE
(2021)
Editorial Material
Computer Science, Information Systems
Dachao Wang, Baocang Wang, Yuan Ping
Summary: Format-preserving encryption (FPE) is a method that allows encrypting data while maintaining its original format. In a cryptography study from 2018, two new FPE schemes were proposed, with the second one using the Mix-Swap-Unmix algorithm to achieve a specific matching exchange process. However, it was later proven that this matching exchange process is invalid, leading to the conclusion that the equivalence does not exist.
IET INFORMATION SECURITY
(2021)
Article
Computer Science, Artificial Intelligence
Yuping Lai, Huirui Cao, Lijuan Luo, Yongmei Zhang, Fukun Bi, Xiaolin Gui, Yuan Ping
Summary: The paper addresses Bayesian estimation of the finite Gamma mixture model (GaMM) under the extended variational inference (EVI) framework, optimizing model performance by introducing lower-bound approximations and automatically determining the optimal mixture component number. The proposed method shows excellent performance in evaluations with synthesized and real data, demonstrating statistically significant improvements in accuracies and runtime compared to referred methods.
Article
Biochemical Research Methods
Deguo Wang, Yongzhen Wang, Meng Zhang, Yongqing Zhang, Juntao Sun, Chunmei Song, Fugang Xiao, Yuan Ping, Chen Pan, Yushan Hu, Chaoqun Wang, Yanhong Liu
Summary: A novel method called ladder-shape melting temperature isothermal amplification (LMTIA) was developed in this study, which was found to be more sensitive than the existing LAMP assay. This method has the potential to be used for prevention and control of emerging epidemics caused by different types of pathogens.
Review
Mathematics, Interdisciplinary Applications
Yunzhen Zhang, Yuan Ping, Zhili Zhang, Guangzhe Zhao
Summary: This paper discusses the multistability phenomenon and its control strategy in memristor-based nonlinear oscillator circuits, and introduces the dimensionality reduction modeling method and flux-charge analysis method. By examining the theory and application promotion of incremental integral transformation method and multistability reconstitution, the application prospect of memristor-coupled systems is demonstrated.
Article
Computer Science, Artificial Intelligence
Yuping Lai, Wenbo Guan, Lijuan Luo, Qiang Ruan, Yuan Ping, Heping Song, Hongying Meng, Yu Pan
Summary: In this study, an extended VI framework was used to derive a closed-form solution for estimating parameters in the Dirichlet mixture process of the Beta-Liouville distribution. Experimental results demonstrated the superior performance and effectiveness of this method in challenging real-world applications such as object detection and text categorization.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Yan Cao, Yuan Ping, Shaohua Tao, YongGang Chen, YanXia Zhu
Summary: Cyber-Physical-Social Space (CPSS) is a promising paradigm that combines cyberspace, physical space, and social space to create an intelligent environment. However, existing security analysis methods for CPSS do not take into account its open and dynamic characteristics. This paper proposes an adaptive security analysis framework for CPSS that includes an access control model, a Labelled Transition System (LTS), and a policy adjustment method to prevent unauthorized information flow and ensure space security.
COMPUTERS & SECURITY
(2022)
Article
Engineering, Multidisciplinary
Hui Ma, Yuan Ping, Yong Zhang
Summary: This paper proposes a novel privacy-preserving cross-zone ride-matching scheme, CRide, which achieves high ride-matching accuracy and acceptable efficiency by extending the zone range and introducing ciphertext technology.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2022)
Article
Computer Science, Information Systems
Huina Li, Yuan Ping, Bin Hao, Chun Guo, Yujian Liu
Summary: This article proposes an improved boundary support vector clustering (IBSVC) method that achieves reasonable boundaries and comfortable parameters through self-adaptive support. The method enhances the accuracy and efficiency of clustering through movable edge selection and flexible parameter selection.
Article
Computer Science, Artificial Intelligence
Shaohua Tao, Runhe Qiu, Bo Xu, Yuan Ping
Summary: Existing recommendation methods neglect the relationship between micro-behavior and knowledge graph and the explicit reasoning for user-item interaction data. This paper proposes a model that incorporates micro-behavior and knowledge graph into reinforcement learning for explainable recommendation, achieving better recommendation results.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Yange Chen, Suyu He, Baocang Wang, Pu Duan, Benyu Zhang, Zhiyong Hong, Yuan Ping
Summary: Industrial Internet of Things (IIoT) is changing traditional industries with the development of big data and deep learning. However, the lack of large-scale datasets can lead to performance issues and data leakage. Privacy-preserving federated learning schemes have been proposed, but security issues remain. In this article, the security of a scheme called DeepPAR is analyzed and an improved scheme is proposed to address the security vulnerabilities. Performance analysis illustrates the security and accuracy of the improved scheme.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Huaxin Deng, Chun Guo, Guowei Shen, Yunhe Cui, Yuan Ping
Summary: With the increase in malware, the detection and classification of malware have become more challenging. Various methods based on malware visualization and deep learning have been proposed, but they fail to retain the semantic and statistical properties in the generated malware images, which are often large and inconsistent in size. This article proposes a new malware visualization method based on assembly instructions and Markov transfer matrices, which effectively characterizes malware. It also introduces a competitive malware classification method, MCTVD, based on three-channel visualization and deep learning, achieving an accuracy of 99.44% on Microsoft's public malware dataset.
COMPUTERS & SECURITY
(2023)
Article
Computer Science, Information Systems
Houlong Fu, Chun Guo, Chaohui Jiang, Yuan Ping, Xiaodan Lv
Summary: An SQL Injection Attack (SQLIA) is a significant cyber security threat to Web services, with different stages causing varying levels of damage. By analyzing outbound traffic from the Web server, we propose an SQLIA detection and stage identification method (SDSIOT) that achieves high accuracy in both detection (98.57%) and stage identification (94.01%). It outperforms ModSecurity by 8.22 percentage points in SQLIA detection accuracy.
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)