Article
Computer Science, Artificial Intelligence
Sushil Kumar Maurya, Dinesh Singh, Ashish Kumar Maurya
Summary: This article discusses the impact of online reviews on consumer purchase decisions and seller market strategies, as well as the challenges and methods of deceptive opinion spam detection.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Hyungho Byun, Sihyun Jeong, Chong-kwon Kim
Summary: The paper proposes an optimized framework for detecting collusive communities, effectively identifying opinion spam reviewers. By detecting collusiveness from behavior and extracting abnormal features for analysis and judgment, the method can effectively and accurately identify spammers.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Review
Computer Science, Information Systems
Arvind Mewada, Rupesh Kumar Dewang
Summary: The article discusses methods for identifying fake reviews and related issues. The main difficulty at present is obtaining a large-scale tagged review dataset.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Review
Computer Science, Artificial Intelligence
Himangshu Paul, Alexander Nikolaev
Summary: As online review systems become more popular, competitors resort to unethical practices to manipulate consumers, prompting the research community to reassess current strategies for combating fake reviews.
DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
Review
Computer Science, Artificial Intelligence
Naveed Hussain, Hamid Turab Mirza, Abid Ali, Faiza Iqbal, Ibrar Hussain, Mohammad Kaleem
Summary: Online reviews are crucial for determining public opinions, but spam reviews have become a serious issue that can impact businesses. The detection of spam reviews and spammers is necessary to maintain the integrity of the review system.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Information Systems
Guangxia Xu, Mengxiao Hu, Chuang Ma
Summary: This paper proposes a novel method SPClique to detect opinion spammer groups, which shows better prediction precision in large-scale review datasets.
INFORMATION SCIENCES
(2021)
Review
Computer Science, Information Systems
Emna Ben Abdallah, Khouloud Boukadi
Summary: This study presents a Dynamic Spam Detection System (DSDS) that improves the detection of new spam behaviors over time. The DSDS system integrates neural networks and reinforcement learning to identify spammer assaults offline and online. It also introduces a new feature selection-based algorithm to explore new spammer behaviors from new reviews. Experimental results show that the proposed system achieves high accuracy rates and low false-positive rates in detecting new spam behaviors.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Review
Computer Science, Artificial Intelligence
Sanjeev Rao, Anil Kumar Verma, Tarunpreet Bhatia
Summary: Online social networks are widely used but face issues such as social spam, requiring research and countermeasures for social spam and spammer detection.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Review
Computer Science, Information Systems
Rami Mohawesh, Shuxiang Xu, Son N. Tran, Robert Ollington, Matthew Springer, Yaser Jararweh, Sumbal Maqsood
Summary: User reviews are crucial in e-commerce but fake reviews have become a significant issue. Investigating existing datasets, feature extraction techniques, and identification methods can help businesses detect fake reviews and improve profitability.
Article
Computer Science, Hardware & Architecture
Yuejun Li, Fangxin Wang, Shuwu Zhang, Xiaofei Niu
Summary: Reviews of products and stores play a crucial role in online e-commerce platforms for customers to make decisions. Some dishonest individuals are hired to write fake reviews, known as opinion spamming, to manipulate the perception of target products or services. Previous research has focused on detecting fake reviews using various methods, but this study introduces the concept of review groups and explores the relationships between reviewers to improve the accuracy of identifying deceptive reviews through grouping algorithms and collusion models.
MOBILE NETWORKS & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Ziyang Wang, Wei Wei, Xian-Ling Mao, Guibing Guo, Pan Zhou, Sheng Jiang
Summary: Due to the significant commercial interests, there is a proliferation of spam reviews aimed at manipulating product reputation. In order to effectively detect collective opinion spammers, this paper proposes an unsupervised network embedding-based approach that utilizes different types of user relationships to represent relevance. Experimental results show significant improvements in AP and AUC compared to existing solutions.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Information Systems
Jiandun Li, Jingyi Hu, Pengpeng Zhang, Liu Yang
Summary: Deceptive online merchandises, also known as review spams, result in significant losses for consumers, manufacturers, and business-to-customer platforms. However, their identification is challenging due to weak supervision and lack of ground-truth labels. The collaboration of crowdsourcing workers in manipulation campaigns further damages the reputation of products and brands. This paper proposes a novel approach using commenting interaction, bipartite graph modeling, and spam indicators to effectively and significantly recognize strong-correlated groups of spam reviewers, outperforming state-of-the-art solutions.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Yafeng Ren, Mengxiang Yan, Donghong Ji
Summary: This study explores a neural network model for deceptive review detection, proposing a hierarchical neural network model and demonstrating through experiments that it achieves competitive accuracy on the Yelp dataset.
INFORMATION SCIENCES
(2022)
Review
Computer Science, Artificial Intelligence
Francisco Janez-Martino, Rocio Alaiz-Rodriguez, Victor Gonzalez-Castro, Eduardo Fidalgo, Enrique Alegre
Summary: Spam emails are no longer just annoying advertisements, but a growing source of scams and attacks. While machine learning-based spam filters have shown high performance in academic journals, users still face fraudulent and malicious emails. The challenges in this field are the dynamic nature of the environment and the presence of spammers as adversaries.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Alexander Ligthart, Cagatay Catal, Bedir Tekinerdogan
Summary: Opinion spam detection is important for identifying fake reviews. Semi-supervised learning approaches are necessary when data is largely unlabeled. Self-training with Naive Bayes base classifier shows the highest accuracy among tested methods.
APPLIED SOFT COMPUTING
(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)