Article
Computer Science, Information Systems
Jian Tang, Kai Zhang
Summary: The application of three-way decision models in solving multi-criteria ranking problems has gained recognition in recent years. However, most existing models are not directly usable when minimum requirements are set for individual criteria. To address this issue, a two-stage three-way ranking pattern has been proposed, but it fails to provide ranking results for objects that are directly identified as accepted or rejected. To overcome this limitation, a three-stage ranking pattern is introduced, considering criterion fuzzy concept and three-way decision. This pattern offers a more realistic and reliable ranking in a multi-criteria environment.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Jianhua Dai, Tao Chen, Kai Zhang
Summary: Recently, researchers have studied three-way decision models in fuzzy multi-criteria environments from the perspective of criterion fuzzy concept. These models consider the subjective preference of decision-makers for criteria and the evaluation value information system provided by decision-makers. However, the criterion fuzzy concept only takes into account the decision-maker's membership preference to each criterion, neglecting their non-membership preference.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Xiangbin Liu, Wang Mao, Jianhua Dai, Kai Zhang
Summary: This paper proposes a new three-way decision model based on comprehensive fuzzy concepts and applies it to solve multi-criteria decision-making problems. Comparative analysis shows that the method is feasible and stable.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Ting-Yu Chang, Cooper Cheng-Yuan Ku
Summary: Ranking methods are widely used in social science and economics for various purposes. The novel fuzzy filtering ranking method combines Likert scale and discrete fuzzy scores to simplify the ranking process. Experimental results show high consistency between the proposed method and traditional methods in ranking outcomes.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Information Systems
Kai Zhang, Jianhua Dai
Summary: In this paper, the concept of fuzzy fl-covering group approximation spaces and a three-way multi-criteria group decision-making method are proposed to solve ranking and classification problems in a group decision-making environment. The method employs fuzzy fitting neighborhoods and an overall loss function to meet the preferences of decision makers. Numerical and experimental analysis demonstrate the feasibility and superiority of the proposed method.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Ruirui Zhao, Lina Ma, Shenggang Li, Minxia Luo
Summary: Three-way decision is a decision-making method based on human cognitive process, aiming to handle uncertain and inconsistent information. A multi-attribute three-way decision method is proposed and applied in a project investment problem to obtain more reasonable and effective results, showing the effectiveness of the approach in dealing with multi-attribute decision-making with changing information.
COGNITIVE COMPUTATION
(2022)
Article
Computer Science, Information Systems
Qiumei Wang, Jianhua Dai, Zeshui Xu
Summary: This paper proposes a three-way multi-criteria decision-making method based on fuzzy complementary preference relation, which can select the optimal object, rank objects, and classify objects at the same time, providing better decision support for decision-makers.
INFORMATION SCIENCES
(2022)
Article
Operations Research & Management Science
Esther Jose, Puneet Agarwal, Jun Zhuang, Jose Swaminathan
Summary: The Indian Railways is India's largest employer and plays a significant role in the country's transportation network, economy, and social and cultural systems. Evaluating the performance of railway networks is crucial for improvement, and this study introduces a novel evaluation method, finding that the Southern Railway zone performs the best.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Information Systems
Grzegorz Miebs, Milosz Kadzinski
Summary: The researchers propose a heuristic method for constructing compromise incomplete rankings based on partial rankings allowing for incomparability. The algorithms utilize various optimization techniques and are demonstrated to be effective through a real-world case study and experimental comparisons on artificially generated problems.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
L. D. C. S. Subhashini, Yuefeng Li, Jinglan Zhang, Ajantha S. Atukorale
Summary: This paper proposes a novel opinion classification method that integrates semantic patterns with fuzzy concepts in a three-way decision framework, which can effectively improve classification accuracy and reduce uncertain boundaries.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Deepak Sukheja, Javaid Ahmad Shah, G. Madhu, K. Sandeep Kautish, Fahad A. Alghamdi, Ibrahim S. Yahia, El-Sayed M. El-Kenawy, Ali Wagdy Mohamed
Summary: Efficient decision-making is a challenge in research, and accuracy can be improved through fuzzification and defuzzification processes. This paper proposes using the COA method and Hurwicz criteria for solving multi-criteria decision-making problems and demonstrates their application through a simple case study.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Artificial Intelligence
Dharmalingam Marimuthu, G. S. Mahapatra
Summary: This paper focuses on decision-making under uncertainties and proposes a method using complete ranking classification of generalized trapezoidal fuzzy numbers to solve fuzzy multi-criteria decision-making problems. The paper also includes a comparative analysis of existing methods with the proposed method.
Article
Automation & Control Systems
Enrique Herrera-Viedma, Ivan Palomares, Cong-Cong Li, Francisco Javier Cabrerizo, Yucheng Dong, Francisco Chiclana, Francisco Herrera
Summary: The article provides an overview of fuzzy and linguistic decision-making trends, studies, methodologies, and models developed in the last 50 years. It discusses core decision-making frameworks and new complex decision-making frameworks that have emerged in recent years. The challenges associated with these frameworks and key guidelines for future research in the field are highlighted.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Business
Jalil Heidary Dahooie, Romina Raafat, Ali Reza Qorbani, Tugrul Daim
Summary: With the increasing impact of customers on each other's purchasing decisions due to web 2.0 websites, a method that ranks alternative products based on product features and customer comments has become essential. This study proposes an integrated framework combining sentiment analysis and multi-criteria decision-making techniques to address existing gaps in online customer reviews (OCRs) product ranking.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2021)
Article
Mathematics, Applied
A. F. Roldan Lopez de Hierro, M. Sanchez, C. Roldan
Summary: This study introduces a method for multi-criteria decision making in a fuzzy environment, where weights and experts' opinions are represented as triangular fuzzy numbers. By studying the main properties of aggregation functions in the fuzzy framework, a new decision making approach is adopted.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2022)
Article
Computer Science, Information Systems
Kai Zhang, Jianhua Dai
Summary: In this paper, the concept of fuzzy fl-covering group approximation spaces and a three-way multi-criteria group decision-making method are proposed to solve ranking and classification problems in a group decision-making environment. The method employs fuzzy fitting neighborhoods and an overall loss function to meet the preferences of decision makers. Numerical and experimental analysis demonstrate the feasibility and superiority of the proposed method.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Kai Zhang, Jianhua Dai
Summary: The TOPSIS method is a technique for sorting and classifying alternatives. This study introduces decision-theoretic rough fuzzy sets to expand the application scope of the TOPSIS method. A novel TOPSIS method is proposed that simultaneously handles sorting and classification using the principles of the TOPSIS method and decision-theoretic rough fuzzy sets. The method incorporates fuzzy concepts, defines decision areas and joint decision areas, and establishes sorting rules and decision processes.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Bingjie Zhang, Xiaoling Gong, Jian Wang, Fengzhen Tang, Kai Zhang, Wei Wu
Summary: This paper presents a nonstationary fuzzy neural network (NFNN) model that combines NFISs and NNs, achieving self-learning and improved translatability of NNs by synthesizing logical inference and language expression abilities with learning mechanisms.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Xuefei Li, Baodi Liu, Kai Zhang, Honglong Chen, Weijia Cao, Weifeng Liu, Dapeng Tao
Summary: This paper presents a review of multi-view learning (MVL) methods in hyperspectral image (HSI) classification. The use of spatial and spectral information from a large number of spectral bands to improve classification performance is explored in three steps: multi-view construction, interactivity enhancement, and multi-view fusion. Representative approaches and advanced work in each step are analyzed and discussed, providing insights into the development of MVL in HSI classification and guiding future research trends.
Article
Computer Science, Artificial Intelligence
Kai Zhang, Jianhua Dai, Zeshui Xu
Summary: This article proposes criterion-oriented three-way ranking and clustering strategies based on fuzzy multicriteria information decision-making problems. It defines criterion fuzzy sets and uses generalized fuzzy rough approximation to perform qualitative analysis and ranking of alternatives. Numerical examples validate the feasibility and effectiveness of these strategies.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jianming Zhan, Kai Zhang, Peide Liu, Witold Pedrycz
Summary: This paper explores a multi-scale group decision-making method to deal with decision-making problems with multi-scale information. The method consists of two stages, where a newly ranking decision-making approach and a decision fusion approach are introduced. The method provides theoretical and methodological support for establishing a multi-scale decision analysis system.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Jianhua Dai, Tao Chen, Kai Zhang
Summary: Recently, researchers have studied three-way decision models in fuzzy multi-criteria environments from the perspective of criterion fuzzy concept. These models consider the subjective preference of decision-makers for criteria and the evaluation value information system provided by decision-makers. However, the criterion fuzzy concept only takes into account the decision-maker's membership preference to each criterion, neglecting their non-membership preference.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Jian Tang, Kai Zhang
Summary: The application of three-way decision models in solving multi-criteria ranking problems has gained recognition in recent years. However, most existing models are not directly usable when minimum requirements are set for individual criteria. To address this issue, a two-stage three-way ranking pattern has been proposed, but it fails to provide ranking results for objects that are directly identified as accepted or rejected. To overcome this limitation, a three-stage ranking pattern is introduced, considering criterion fuzzy concept and three-way decision. This pattern offers a more realistic and reliable ranking in a multi-criteria environment.
INFORMATION SCIENCES
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Meng Wang, Kai Zhang, Li Zhang, Yue Li, Junru Li, Yue Wang, Shiqi Wang
Summary: This paper proposes an end-to-end image compression framework using Swin-Transformer modules. Experimental results demonstrate that the proposed method outperforms existing methods in compressing both natural scene and screen content images.
2022 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Yang Wang, Kai Zhang, Na Zhang, Zhipin Deng, Li Zhang
Summary: This paper proposes an enhanced motion list reordering approach that utilizes refined motion information to optimize compression efficiency. By using a dedicated motion refinement method and fast algorithms, significant BD-rate savings can be achieved.
2022 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Xi Xie, Kai Zhang, Li Zhang, Meng Wang, Junru Li, Shiqi Wang
Summary: This paper investigates the role of interpolation filters in motion compensation for video coding and proposes 6-tap DCT-based chroma interpolation filters for chroma motion compensation. The experimental results demonstrate a good trade-off between coding performance and complexity.
2022 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Kai Zhang, Li Zhang, Zhipin Deng, Na Zhang, Yang Wang
Summary: In VVC, affine motion compensation (AMC) and history-based motion vector prediction (HMVP) are powerful coding tools. However, HMVP does not consider non-translational motion. This paper presents a method to efficiently represent control point motion vectors (CPMV) using history information, which has been adopted into VVC.
2022 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP)
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Bharath Vishwanath, Kai Zhang, Li Zhang
Summary: Current video coding schemes ignore cross-component correlations between luma and chroma components, but we propose a novel cross-component prediction model for screen content sequences. This model predicts chroma values based on a discrete mapping function between luma and chroma values. Experimental results demonstrate its effectiveness in reducing bit-rate for text and graphics media.
2022 PICTURE CODING SYMPOSIUM (PCS)
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Kai Zhang, Li Zhang, Zhipin Deng
Summary: In this paper, a self-aware filter estimation cross-component linear model (SAFE-CCLM) intra-prediction method is proposed, which can adaptively select the appropriate filter without signaling a filter index. Experimental results show that this method can achieve a certain level of bitrate saving.
2022 PICTURE CODING SYMPOSIUM (PCS)
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Yue Li, Li Zhang, Jizheng Xu, Kai Zhang
Summary: This study reviews the results and observations of the Grand Challenge on Neural Network-based Video Coding, and evaluates the performance of different neural network-based coding schemes in terms of coding efficiency and methodological innovation.
2022 PICTURE CODING SYMPOSIUM (PCS)
(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)
