Article
Computer Science, Theory & Methods
Linlin Xie, Guoping Lin, Jinjin Li, Yidong Lin
Summary: Attribute reduction, an essential challenge in pattern recognition, data mining, and knowledge discovery, can be improved by using a new measure called local information entropy.
FUZZY SETS AND SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Junyi Chai
Summary: The paper introduces a new type of rough set approximation based on classes rather than class unions and extends it to a series of DRSA models, including classical DRSA, variable consistency DRSA model, variable precision DRSA model, and believable rough set approach model. Additionally, methods of criteria reduction under the class-based rough approximation framework are explored, and relationships among the proposed and previous reducts in DRSA are clarified.
Article
Computer Science, Artificial Intelligence
Juncheng Bai, Bingzhen Sun, Xiaoli Chu, Ting Wang, Hongtao Li, Qingchun Huang
Summary: This paper introduces a new multi-attribute prediction approach based on neighborhood rough set and multivariate variational mode decomposition to improve the accuracy of disease prediction. Experimental results show that the proposed method has high accuracy and stability, and can provide a new quantitative theory and method for chronic disease management decision-making in medical decision-making.
APPLIED SOFT COMPUTING
(2022)
Article
Mathematics
Katarina Rogulj, Jelena Kilic Pamukovic, Majda Ivic
Summary: The research proposes an algorithm based on the theory of rough neutrosophic sets to solve the strategic planning problem related to the remediation of historic pedestrian bridges. A new multicriteria decision-making model is developed by fusing rough set and neutrosophic set theory, with the introduction of a new cross entropy and weighted rough neutrosophic symmetric cross entropy.
Article
Computer Science, Artificial Intelligence
Noor Rehman, Abbas Ali, Kostaq Hila
Summary: This note demonstrates with counterexamples that Theorem 5.7 and Theorem 5.9 in Tiwari et al. (2018) are incorrect, and presents their corrected versions.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Bo Lei, Jiulun Fan
Summary: This paper proposes an adaptive granulation Renyi rough entropy image thresholding method, which has advantages in dealing with uncertainty information and selecting granule size, and its effectiveness is verified through experiments.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Likui An, Sinan Ji, Changzhong Wang, Xiaodong Fan
Summary: A novel model, weighted multigranulation fuzzy decision rough sets, is proposed in this paper, which uses Gaussian kernel to compute similarity and obtain multiple fuzzy granulations from multisource fuzzy information system. The proposed method is compared with Sun's multigranulation rough set model, demonstrating its effectiveness in multisource data analysis.
Article
Computer Science, Artificial Intelligence
Tareq M. Al-shami
Summary: This paper introduces a topological method to create new rough set models, which generates new rough approximations and accuracy measures that preserve monotonicity and allow for comparison. Compared to previous approaches, this method is more accurate, especially in addressing issues related to Dengue fever.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Physics, Multidisciplinary
Adam Brandenburger, Pierfrancesco La Mura, Stuart Zoble
Summary: This study contributes to the program of axiomatizing quantum mechanics by characterizing the states of qubits using an entropic uncertainty principle formulated on an eight-point phase space, employing Renyi entropy to define the signed phase-space probability distributions for representing quantum states.
Article
Computer Science, Artificial Intelligence
Faryal Nosheen, Usman Qamar, Muhammad Summair Raza
Summary: Classical Rough Set Theory (RST) is a widely-used tool for dealing with uncertainty in categorical data, but it does not consider preference order of attribute values. Dominance-based Rough Set Approach (DRSA) is a generalization of RST that focuses on dominance aspect of attributes. The proposed heuristic approach for computing DRSA approximations shows a significant reduction in execution time and structural complexity, avoiding redundant computations and improving efficiency.
Article
Computer Science, Artificial Intelligence
Juncheng Bai, Jianfeng Guo, Bingzhen Sun, Yuqi Guo, Youwei Chen, Xia Xiao
Summary: This paper combines machine learning with rough set theory to establish a new prediction model, which quantitatively divides stock data into three categories and predicts future trends accordingly. A new portfolio strategy is proposed based on this model. Experimental results show that the proposed approach not only provides scientific support and reference for investors, but also offers a new theoretical and methodological framework for stock market investment decisions.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Miroslav Karny
Summary: The paper presents a method for assigning proper prior probabilities to new, generally compound hypotheses using the minimum relative-entropy principle and forecaster-based knowledge transfer. The technique shows strong application potential in creating hypotheses, addressing learning problems with insufficient data, and sequential Monte Carlo estimation. Interesting open research problems related to this method are also listed.
PATTERN RECOGNITION LETTERS
(2021)
Article
Mathematics, Applied
Xinsheng Wang, Yu Zhang, Yujun Zhu
Summary: This paper considers the complexity of an upper semi-continuous set-valued map F on a compact metric space, using entropy-like invariants from various perspectives. It introduces and investigates several topological versions of entropy, including pointwise entropies hp(F) and hm(F), branch entropy hi(F), and tree entropy ht(F). The properties of these entropies are discussed, as well as their relations with the classical entropy htop(F). The calculation or estimation of these entropies for certain finitely-generated set-valued maps on intervals or graphs are also explored.
JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Marko Palangetic, Chris Cornelis, Salvatore Greco, Roman Slowinski
Summary: This paper extends the fuzzy dominance-based rough set approach (DRSA) and explores the application of Ordered Weighted Average (OWA) operators. The theoretical properties of hybridizing OWA operators with fuzzy DRSA are examined, and the robustness of the standard fuzzy DRSA approach is experimentally compared with the OWA approach.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2021)
Article
Computer Science, Artificial Intelligence
Jiucheng Xu, Kanglin Qu, Xiangru Meng, Yuanhao Sun, Qincheng Hou
Summary: This study proposes a multiperspective rough set model to address several issues in feature selection, by defining interclass boundaries, introducing three neighborhood concepts, and integrating information theory and algebraic views. It also presents multiview entropy measures and a nonmonotonic feature selection algorithm. Furthermore, Information Gain is utilized to reduce high-dimensional data sets and improve classification accuracy.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Ching-Hsue Cheng, Hsien-Hsiu Chen, Tai-Liang Chen
Article
Biology
Ching-Hsue Cheng, Jing-Rong Chang, Hao-Hsuan Huang
COMPUTERS IN BIOLOGY AND MEDICINE
(2020)
Review
Computer Science, Artificial Intelligence
You-Shyang Chen, Ching-Hsue Cheng, Wei-Lun Hung
Summary: This study addresses the emerging issue of tea and health by proposing a hybrid method of intelligent text mining and topic modeling. It fills the gap in knowledge on the curative effects of tea against fatal diseases, particularly cancer, and offers eight beneficial directions for future research and applications.
Article
Computer Science, Artificial Intelligence
Ching-Hsue Cheng, Yung-Fu Kao, Hsien-Ping Lin
Summary: The study establishes a model for detecting financial statement fraud, addresses missing values and imbalanced classes, proposes useful rules through various methods, utilizes a random forest model, and demonstrates the robustness of ensemble learning in this research.
APPLIED SOFT COMPUTING
(2021)
Article
Biology
Yun-Chun Wang, Ching-Hsue Cheng
Summary: This study proposes a multiple combined method to address class imbalances in medical data, utilizing resampling, particle swarm optimization, and MetaCost. Experimental results demonstrate improvement in various evaluation metrics, suggesting the effectiveness of the proposed approach in comparison to traditional methods.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Ching-Hsue Cheng, Shu-Fen Huang
Summary: In the field of medical data imputation methods, a clustering-based purity and distance imputation method is proposed to improve the handling of missing values. Experimental results indicate that this method can enhance imputation performance in terms of accuracy, AUC, and RMSE for different missing degrees and types.
Article
Chemistry, Multidisciplinary
Ching-Hsue Cheng, Ming-Chi Tsai, Yi-Chen Cheng
Summary: This study used smartcard data to forecast bus passenger flow and established an integrated-weight time-series forecast model. The lag period was found to significantly affect the forecast results, and the proposed model was more effective than other individual intelligent forecast models in improving passenger flow forecasting.
APPLIED SCIENCES-BASEL
(2022)
Article
Environmental Sciences
Ching-Hsue Cheng, Ming-Chi Tsai
Summary: In this study, a hybrid methodology was used to forecast the concentrations of air pollutants, showing that random forest and intelligent time series support vector regression performed well in classification and prediction. These research results are of great reference value for addressing air quality issues.
Article
Multidisciplinary Sciences
Ching-Hsue Cheng, Jun-He Yang, Po-Chien Liu
Summary: This study analyzes traffic accident data in Taoyuan, Taiwan to identify key attributes related to accident severity. The findings provide insights for governments and stakeholders to reduce road accident risk factors.
Article
Computer Science, Artificial Intelligence
Ching-Hsue Cheng, Zheng-Ting Ji
Summary: This study utilized visualized knowledge representation to analyze lung cancer literature, employing natural language processing and latent Dirichlet allocation method for topic modeling and classification. A new weighted knowledge graph construction method was proposed and trained using graph neural network algorithms. The results showed improved classification performance and effective reduction of edges on the knowledge graphs.
APPLIED INTELLIGENCE
(2023)
Article
Social Sciences, Interdisciplinary
Ching-Hsue Cheng, Ming-Chi Tsai, Chin Chang
Summary: A stock forecasting and trading system is complex, and this study proposes an effective time series model for predicting stock prices by integrating various models and methods, demonstrating its advantages over traditional models.
Article
Computer Science, Information Systems
Ching-Hsue Cheng, Wen-Hong Cai
Summary: This study proposes an intelligent financial fraud detection system using a double-weight latent Dirichlet allocation (DW-LDA) algorithm to extract keywords and build an intelligent text fraud detection model. In addition, it uses SMOTE and random undersampling to handle imbalanced datasets. The results show that the proposed algorithm outperforms existing topic models in terms of performance.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ching-Hsue Cheng, Mu-Yen Chen, Jing-Rong Chang
Summary: This paper presents a linguistic MCDM aggregation model, which can handle problems under different decision situations based on the decision-maker's preference.
GRANULAR COMPUTING
(2023)
Article
Environmental Studies
Ching-Hsue Cheng, Ming-Chi Tsai
Summary: This study proposed an intelligent homogeneous model based on an enhanced weighted kernel self-organizing map (EW-KSOM) for forecasting house prices, and found that the best prediction algorithm is the combination of EW-KSOM and random forest. The top five key factors influencing house prices include transferred land area, house age, building transfer total area, population percentage, and the total number of floors.
Article
Education & Educational Research
Ching-Hsue Cheng, Chung-Hsi Chen
Summary: This research explores the impact of a mobile-assisted English learning system on elementary school students' learning achievement, finding that the system benefits students' learning outcomes. Additionally, it highlights that lower levels of English anxiety and higher levels of perceived usefulness lead to better learning achievement.
COMPUTER ASSISTED LANGUAGE LEARNING
(2022)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)