Article
Computer Science, Artificial Intelligence
Xiaoqing Ye, Dun Liu
Summary: This paper proposes a novel interpretable sequential three-way recommendation strategy, CTR-CS3WR, which introduces collaborative topic regression and three novel granulation methods. Extensive experiments on two CiteUlike datasets confirm the effectiveness of the proposed strategy.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoqing Ye, Dun Liu
Summary: This paper proposes a novel interpretable sequential three-way recommendation strategy, CTR-CS3WR, which introduces collaborative topic regression and designs three granulation methods to achieve multilevel characteristics of recommendation information and interpretability of recommendation results. Experimental results validate the effectiveness of the proposed strategy.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Yi Xu, Baofeng Li
Summary: This paper discusses the importance of multiview and multilevel in granular computing, and introduces a new partition order product space model. It proposes search algorithms and fusion strategies for solving three-way decisions from multiple views and multiple levels.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Xiaoqing Ye, Dun Liu
Summary: This paper proposes a novel sequential recommendation strategy from the temporal-spatial perspective, which constructs multilevel recommendation information using recurrent neural network and achieves multi-step recommendation through a temporal-spatial three-way recommendation strategy. A temporal-spatial three-way recommendation based on recurrent neural network is further proposed to realize recommendation with lower decision cost.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Wenbin Qian, Yangyang Zhou, Jin Qian, Yinglong Wang
Summary: This paper proposes a cost-sensitive sequential three-way decision model for information systems with fuzzy decision, which achieves better classification performance and lower test costs by optimizing information granularity.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Artificial Intelligence
Wenbin Chen, Qinghua Zhang, Yongyang Dai
Summary: This study proposes a new sequential multi-class three-way decision model by considering the granular structure of the sequential process. The model defines decision cost, calculates attribute sequence, and the experimental results demonstrate its advantage in decision cost.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Information Systems
A. Savchenko
Summary: A novel image recognition algorithm based on sequential three-way decisions is introduced to speed up the inference in a convolutional neural network. This approach does not require a special training procedure for neural networks and can be used with arbitrary architectures, demonstrating a reduction in running time of up to 40% with a controlled decrease in accuracy when tested on several datasets and neural architectures.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoqing Ye, Dun Liu, Tianrui Li
Summary: Recommender system (RS) is an information processing system that captures user preferences and makes recommendations using recommendation information (RI) learned from different data sources. Existing recommendation strategies mainly focus on static recommendations and ignore the multilevel characteristic of RI. To address this, granular computing and sequential three-way decisions are introduced, and a naive recommendation method called cost-sensitive sequential three-way recommendation (CS3WR) based on collaborative deep learning (CDL) is proposed. Experimental results on two CiteUlike datasets validate the feasibility and effectiveness of the proposed methods.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Computer Science, Artificial Intelligence
Yingsheng Chen, Jinhai Li, Jinjin Li, Rongde Lin, Dongxiao Chen
Summary: This paper explores the issue of optimal scale selection in multi-scale decision information systems, emphasizing the dynamic changes and increasing amount of information in big data. It further investigates the change laws of optimal scale when adding an object, developing sufficient and necessary conditions for updating the optimal scale, making the theoretical study more complete.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Information Systems
Ke Sun, Tieyun Qian, Xu Chen, Ming Zhong
Summary: In this paper, we propose a context-aware seq2seq translation model to capture the inter-sequence dependency for sequential recommendations. The injected VAE in our model redresses the semantic imbalance between context and item, leading to superior performance over state-of-the-art baselines.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Yongjing Zhang, Guannan Li, Wangchen Dai, Chengxin Hong, Jin Qian, Zhaoyang Han
Summary: In the context of IoT, decision models and GRC can be used for data processing. By incorporating device-free sensor data into decision models, IoT data can be processed more efficiently and accurate location positioning can be achieved.
COMPUTER COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Qingzhao Kong, Xiawei Zhang, Weihua Xu, Binghan Long
Summary: Granular computing and three-way decision are important methods in knowledge discovery and data mining. This paper presents a novel granular computing model based on the idea of three-way decision and discusses its mathematical properties. The model is also applied to network security, and algorithms for computing description set, description degree, attribute reduction, and reduction degree are developed. Numerical experiments are conducted to validate the effectiveness of the algorithms and analyze the related factors.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Artificial Intelligence
Jie Yang, Tian Luo, Fan Zhao, Shuai Li, Xin Jin
Summary: This paper proposes a data-driven sequential three-way decisions (DDS3WD) model to address the processing of unlabeled information systems (UIS), establishing the model by updating attributes and validating it through experiments.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Xianyong Zhang, Yanhong Zhou, Xiao Tang, Yunrui Fan
Summary: This study aims to improve the conditional neighborhood entropy by establishing three-level granular structures and three-way neighborhood entropies. The improved measurement method provides more accurate, hierarchical, systematic, and monotonic measurements. The effectiveness of the method is verified through decision table examples and data set experiments, facilitating uncertainty measurement, information processing, and knowledge discovery.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Information Systems
En Xu, Zhiwen Yu, Nuo Li, Helei Cui, Lina Yao, Bin Guo
Summary: Sequential recommendation is a powerful technology for predicting users' next interaction based on their historical behaviors. Previous studies have focused on optimizing recommendation accuracy on different datasets, but have not explored the intrinsic predictability of sequential recommendation. This study proposes a method to quantify the predictability of sequential recommendations by determining the size of the candidate set.
FRONTIERS OF COMPUTER SCIENCE
(2023)
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)