Article
Computer Science, Artificial Intelligence
Xiang Jia, Yingming Wang
Summary: This paper improves the traditional MCDM techniques by introducing the Choquet integral-based intuitionistic fuzzy arithmetic aggregation (CIIFAA) operator and the Choquet integral-based intuitionistic fuzzy hybrid arithmetic aggregation (CIIFHAA) operator, to better handle decision-making problems in an intuitionistic fuzzy environment.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Xiuqin Ma, Hongwu Qin, Jemal H. Abawajy
Summary: In this article, a new decision-making approach based on IVIFSS is proposed to address the challenge of selecting an optimal choice under uncertainty. The approach utilizes the choice value and score value of membership/nonmembership degrees and also includes three parameter reduction algorithms. The effectiveness and computational efficiency of the proposed approach are demonstrated through a comparison with the adjustable IVIFSSs approach and a real application.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Wan-Hui Lee, Jen-Hui Tsai, Lai-Chung Lee
Summary: This paper introduces a new MCDM method based on intuitionistic fuzzy sets, utilizing normalized intuitionistic fuzzy entropy values to calculate criteria weights, a weighted similarity measure to account for hesitancy in IFS elements, and the combination of WSM with the Extended TOPSIS Method to overcome limitations of existing methods. This proposed approach offers a simplified solution for handling MCDM problems under intuitionistic fuzzy environments.
JOURNAL OF INTERNET TECHNOLOGY
(2021)
Article
Mathematics
Zeeshan Ali, Tahir Mahmood, Miin-Shen Yang
Summary: In this paper, the authors derive the Frank operational laws for CIF information and propose prioritized aggregation operators based on these laws. They also introduce the WASPAS method under the consideration of CIF information and provide numerical examples to compare the proposed operators with existing ones in multi-attribute decision-making procedures, demonstrating the validity and worth of the proposed approaches.
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
Computer Science, Interdisciplinary Applications
Jin Ye, Jianming Zhan, Zeshui Xu
Summary: This paper proposes a novel decision-making method based on fuzzy rough sets to transform uncertain data into intuitionistic fuzzy data, establish a new MADM method, and introduce intuitionistic fuzzy weights and global intuitionistic fuzzy thresholds.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Thermodynamics
Naijie Chai, Wenliang Zhou, Gabriel Lodewijks, Ziyu Chen
Summary: This study compares four fuzzy multi-criteria decision-making methods for selecting a sustainable battery supplier (SBS) for a battery-swapping station. The results of a case analysis demonstrate that economic criteria are the most important, and fuzzy VIKOR shows greater potential in sustainable supplier decision analysis.
INTERNATIONAL JOURNAL OF GREEN ENERGY
(2023)
Article
Computer Science, Artificial Intelligence
Ali Mohtashami
Summary: This paper introduces a new method for fuzzy pairwise comparisons, Best-Worst Method for considering Fuzzy Pairwise Comparisons (FBWM), which differs from previous approaches by obtaining crisp weights from a fuzzy pairwise comparison matrix, eliminating the need for supplementary aggregation of fuzzy weights and ranking procedures.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Esra Cakir, Mehmet Ali Tas
Summary: This study introduces a new C-IFS multi-criteria decision making method by utilizing circular intuitionistic fuzzy sets. The study contributes to C-IFS by proposing formulations for radius calculation and a new defuzzification function. The results show that considering the optimistic and pessimistic points as well as the decision-maker attitude leads to more precise results in C-IFS.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Green & Sustainable Science & Technology
Shubham Dutt Attri, Shweta Singh, Atul Dhar, Satvasheel Powar
Summary: This paper presents the application of multi-criteria decision-making in the assessment of wastewater treatment technologies and provides a comparison study of six technologies based on sustainability parameters. The study proposes a reliable decision-making method for selecting sustainable wastewater treatment technologies.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Computer Science, Artificial Intelligence
Amit K. Shukla, Vishnu Prakash, Rahul Nath, Pranab K. Muhuri
Summary: This paper introduces an intelligent decision-making method that handles uncertainty in multiple dimensions and decision-maker hesitancy. It proposes a novel trapezoidal type-2 intuitionistic fuzzy set to handle uncertainty in multiple dimensions and presents the generation procedure, operations, comparison, and distance computation for such set. Additionally, it discusses the computation of decision-maker weights and criterion weights and applies this method to a renewable energy resource selection problem in multi-criteria decision-making.
Article
Management
Muhammad Jawad, Munazza Naz, Haseena Muqaddus
Summary: Portfolio selection for Stock evaluation and selection is a multi-criteria decision-making problem that requires the use of appropriate techniques. The Analytic Hierarchy Process (AHP) is widely used in operation management to simplify complex decision problems. The Spherical Fuzzy Sets (SFS) are advantageous in handling uncertainty and vagueness. This paper modifies the Fuzzy Analytic Hierarchy Process (FAHP) into Spherical fuzzy AHP (SFAHP) by combining AHP and SFS, and also introduces the concept of Spherical Fuzzy Preference Relation (SFPR) and develops an algorithm to construct a consistent SFPR. The proposed approach is validated through an illustrative application of portfolio selection on the Pakistan Stock Exchange.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Information Systems
Andreas Andreou, Constandinos X. Mavromoustakis, Evangelos K. Markakis, Houbing Song
Summary: This paper presents a novel methodology for interpreting and evaluating residents' feedback on service provision in urban environments. The methodology integrates Intuitionistic Preference Relations and the Best-Worst Method, providing a refined mechanism for capturing residents' preferences and facilitating decision-making processes.
Article
Computer Science, Information Systems
Kamal Kumar, Shyi-Ming Chen
Summary: This paper proposes an improved intuitionistic fuzzy Einstein weighted averaging operator to overcome the drawbacks of existing operators, and introduces a new multiattribute decision making method based on it.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Liang-Hsuan Chen, Chien-Cheng Tu
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2015)
Article
Computer Science, Artificial Intelligence
Liang-Hsuan Chen, Hsin-Hung Chen
KNOWLEDGE-BASED SYSTEMS
(2015)
Article
Management
Liang-Hsuan Chen, Wen-Chang Ko, Feng-Ting Yeh
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2017)
Article
Engineering, Multidisciplinary
Liang-Hsuan Chen, Hsin-Hung Chen
APPLIED MATHEMATICAL MODELLING
(2013)
Article
Business
Liang-Hsuan Chen, Wen-Chang Ko, Chien-Yao Tseng
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2013)
Article
Computer Science, Artificial Intelligence
Liang-Hsuan Chen, Chien-Cheng Tu
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2014)
Article
Engineering, Industrial
Liang-Hsuan Chen, Cheng-Nien Chen
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2014)
Article
Engineering, Industrial
Wen-Chang Ko, Liang-Hsuan Chen
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2014)
Article
Computer Science, Artificial Intelligence
Liang-Hsuan Chen, Chan-Ching Hsueh, Chia-Jung Chang
KNOWLEDGE-BASED SYSTEMS
(2013)
Article
Automation & Control Systems
Liang-Hsuan Chen, Chien-Cheng Tu
IEEE TRANSACTIONS ON CYBERNETICS
(2014)
Article
Computer Science, Artificial Intelligence
Liang-Hsuan Chen, Sheng-Hsing Nien
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Liang-Hsuan Chen, Sheng-Hsing Nien
FUZZY OPTIMIZATION AND DECISION MAKING
(2020)
Article
Computer Science, Artificial Intelligence
Liang-Hsuan Chen, Sheng-Hsing Nien
Summary: This article introduces an intuitionistic fuzzy linear regression model, which considers intuitionistic fuzzy numbers for variables and parameters. A mathematical programming problem is proposed to optimize the IFN parameters by minimizing the absolute distances between observed and predicted IFNs. A three-step procedure is suggested for computational efficiency improvement. Comparisons with existing approaches show outstanding performance in similarity and distance measures.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Information Systems
Cheng-Hsiung Chiang, Liang-Hsuan Chen
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING
(2015)
Article
Computer Science, Artificial Intelligence
Liang-Hsuan Chen, Hsin-Hung Chen
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2015)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)
