Article
Computer Science, Artificial Intelligence
Lin Yao, Zhenyu Chen, Haibo Hu, Guowei Wu, Bin Wu
Summary: In this article, a comprehensive trajectory publishing algorithm is proposed, which effectively protects the privacy of sensitive labels and location data, and achieves higher data utility.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Shuilian Yuan, Dechang Pi, Xiaodong Zhao, Meng Xu
Summary: With the popularity of mobile devices with positioning functions, location-based services have become essential in people's daily lives, but the issue of privacy leakage is becoming increasingly critical. Current differential privacy technology has gained attention, but its application in trajectory privacy protection is challenging due to the need to consider both spatial and temporal characteristics. A differential privacy trajectory data protection scheme based on R-tree has been proposed to address this issue, utilizing DPTS-tree to protect user privacy information effectively.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Guoying Qiu, Deke Guo, Yulong Shen, Guoming Tang, Sheng Chen
Summary: This article introduces a mobile semantic-aware privacy model, MSP, to address the personalized requirements between users and locations. By constructing a hierarchical semantic tree and evaluating the privacy sensitivity of locations, an adaptive privacy-preserving mechanism is developed to achieve a balance between personalized privacy preservation and data availability.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Review
Multidisciplinary Sciences
Yunfeng Wang, Mingzhen Li, Yang Xin, Guangcan Yang, Qifeng Tang, Hongliang Zhu, Yixian Yang, Yuling Chen
Summary: This research proposes a method of exchanging reviews before users submit them to prevent adversaries from easily obtaining user information. By introducing two types of attacks, the study presents two defense mechanisms against different types of attacks.
Article
Computer Science, Artificial Intelligence
Zuan Wang, Xiaofeng Ding, Junfeng Lu, Liang Zhang, Pan Zhou, Kim-Kwang Raymond Choo, Hai Jin
Summary: This paper presents SecSky, an efficient solution for supporting secure location-based skyline queries. The solution utilizes a secure index and a novel secure protocol to improve query efficiency and protect privacy.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Zhaoman Liu, Lei Wu, Weizhi Meng, Hao Wang, Wei Wang
Summary: With the maturity of Internet-of-Things technology, location-based service (LBS) is rapidly developing in intelligent terminal devices, but the large amount of spatial data generated poses a burden on providers; outsourcing spatial data to cloud server has become a new trend, but faces issues of data disclosure and query disclosure; the proposed ARQ scheme allows efficient range query while protecting data and user privacy, applicable to various forms of spatial data, and has practical significance.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Changqiao Xu, Liang Zhu, Yang Liu, Jianfeng Guan, Shui Yu
Summary: The study introduces a DP-LTOD scheme that obfuscates original trajectory sequences for privacy preservation. By partitioning the original trajectory sequence and selecting suitable locations and segments, the scheme successfully protects trajectory privacy while discovering latent trajectory communities. Experimental results demonstrate that the scheme effectively detects latent trajectory communities and safeguards user privacy.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2021)
Article
Automation & Control Systems
Zhigang Gao, Yucai Huang, Leilei Zheng, Huijuan Lu, Bo Wu, Jianhui Zhang
Summary: This article proposes a differential location privacy-preserving mechanism based on trajectory obfuscation (LPMT), which extracts trajectory features through a sliding window algorithm and performs Laplace sampling in the target obfuscation subregion to obtain obfuscated GPS points. LPMT can reduce data quality loss by over 20% while providing the same level of obfuscation quality, indicating its strong security and high quality of service.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Civil
Sujin Cai, Xin Lyu, Xin Li, Duohan Ban, Tao Zeng
Summary: A novel differential privacy-based algorithm named DPTD is proposed for trajectory database releasing, which divides trajectory space into planes and adds noise based on spatio-temporal correlation to protect privacy while ensuring data availability. The method improves privacy protection and data availability simultaneously.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Telecommunications
Yi Liu, Jing Tian, Yunming Du, Shuai Li
Summary: This paper proposes a privacy preservation algorithm based on random sensitive areas, which selects multiple random sensitive areas and anonymous users to generalize the real location, thereby increasing the difficulty for adversaries to identify the real location.
WIRELESS PERSONAL COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Qingyun Zhang, Xing Zhang, Mingyue Wang, Xiaohui Li
Summary: The LBS Privacy Protection Scheme Based on Differential Privacy (DPLQ) includes privacy protection algorithms for both user location and query data, utilizing Laplace and exponential mechanisms to resist malicious attacks and effectively safeguard user privacy.
IET INFORMATION SECURITY
(2021)
Article
Computer Science, Hardware & Architecture
Minglai Shao, Jianxin Li, Qiben Yan, Feng Chen, Hongyi Huang, Xunxun Chen
Summary: Mobile devices have become an important part of daily life, with increasing concerns about potential privacy leakage, especially location privacy. This article introduces a novel location inference attack framework, iTracker, which can recover multiple trajectories simultaneously using a structured sparsity model, outperforming existing recovery algorithms.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2021)
Article
Engineering, Civil
Chuan Xu, Yingyi Ding, Chao Chen, Yong Ding, Wei Zhou, Sheng Wen
Summary: This paper proposes a personalized location privacy protection scheme based on differential privacy to protect the privacy of location-based services in vehicular networks. By establishing a utility model and a sensitivity distance index, differentiated protection for users' different locations is achieved. An optimal solution for false location is obtained through a multi-objective optimization model.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Jianhao Wei, Yaping Lin, Xin Yao, Jin Zhang
Summary: This paper proposes a differential privacy-based location protection scheme for protecting the location privacy of workers and tasks in spatial crowdsourcing, while achieving efficient task allocation.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Yang Cao, Yonghui Xiao, Li Xiong, Liquan Bai, Masatoshi Yoshikawa
Summary: This paper introduces the concept of spatiotemporal events and ε-spatiotemporal event privacy, and highlights the potential weaknesses in existing LPPMs in protecting this type of privacy. It presents a framework, PriSTE, to enhance existing LPPMs to provide better protection for spatiotemporal event privacy by adjusting privacy budgets. Experiments conducted on real-life and synthetic data confirm the effectiveness and efficiency of the proposed method.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
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)