Article
Computer Science, Information Systems
Lipeng Pan, Yong Deng
Summary: This paper extends the Dempster-Shafer evidence theory to the complex domain to effectively describe and process uncertain information in multidimensional characteristic data and periodic data with phase angle changes. It introduces the complex mass function and other basic concepts to describe uncertainty and supplements the complex Dempster rule of combination. A method to generate complex mass function and apply it to target recognition is proposed, showing improved recognition rate compared to the traditional mass function approach.
INFORMATION SCIENCES
(2022)
Article
Mathematics, Interdisciplinary Applications
Qianli Zhou, Yong Deng
Summary: This paper explores the relationship between the Sierpinski gasket and matrix calculus in Dempster-Shafer Theory (DST), connecting fractal theory and DST from a geometric perspective for the first time. Additionally, a method to generate the Sierpinski gasket using the Kronecker product is proposed based on the generation process of matrices.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Computer Science, Artificial Intelligence
Ildar Batyrshin, Luis Alfonso Villa-Vargas, Marco Antonio Ramirez-Salinas, Moises Salinas-Rosales, Nailya Kubysheva
Summary: The paper introduces the notion of negation of a probability distribution, emphasizing the need for such a negation in knowledge-based systems. The study focuses on transforming probability distributions point by point using decreasing functions defined on [0,1]. The characterization of linear negators is presented as a convex combination of Yager's and uniform negators.
Article
Computer Science, Artificial Intelligence
Thierry Denoeux
Summary: This study addresses the combination of belief functions in a communication network where agents hold evidence and can only exchange information with neighbors. Distributed implementations of Dempster's rule and the cautious rule are proposed based on average and maximum consensus algorithms. Procedures for agents to agree on a frame of discernment and supported hypotheses are described to reduce data exchange, along with a demonstration of the feasibility of a robust combination procedure using a distributed implementation of the RANSAC algorithm.
INFORMATION FUSION
(2021)
Article
Computer Science, Information Systems
Jie Zhao, Kang Hao Cheong
Summary: The inference of the source in a pandemic outbreak has attracted considerable attention due to its practical potential. We propose an evidential source localization (ESL) model that utilizes evidence theory to determine the source node by information fusion. ESL is characterized by its ability to detect sources of disease at an early stage of the pandemic. Experimental results demonstrate the superiority of ESL compared to other state-of-the-art methods in terms of efficiency and effectiveness.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Thierry Denoeux
Summary: This article presents a neural network model for regression that quantifies prediction uncertainty using GRFNs. The model is competitive in terms of prediction accuracy and calibration error compared to other advanced techniques.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Vaclav Snasel, Irina Perfilieva, Meenu Singh, Millie Pant, Zahra Alijani, Lingping Kong
Summary: This study proposes a new weighting method for multiple-criteria decision-making (MCDM) that addresses the limitations of the commonly used entropy-based method. By considering multiple evaluation factors and incorporating them into the weighting method, the proposed approach overcomes the limitations of the entropy-based method and reduces computation requirements. The study also introduces a fuzzy MCDM model for handling uncertainty and tests the correctness and effectiveness of the proposed approach on real-life applications. The experimental results suggest that the proposed approach is promising and offers valuable insights for decision-makers.
APPLIED SOFT COMPUTING
(2023)
Article
Engineering, Electrical & Electronic
Like Wang, Yu Bao
Summary: An improved evidence combination method is proposed in this paper to enhance the effect of conflict data fusion, by calculating support degree and belief entropy to combine conflicting evidences. This solution effectively addresses the counter-intuitive behaviors of classical Dempster-Shafer evidence theory in high conflict data scenarios.
JOURNAL OF SENSORS
(2021)
Article
Computer Science, Information Systems
Moxian Song, Chenxi Sun, Derun Cai, Shenda Hong, Hongyan Li
Summary: This paper proposes a method based on evidential fusion to classify vaguely labeled data, dividing them into small data groups using a valid label-set cover assignment algorithm and applying evidence theory for classification. Experimental results demonstrate that this method outperforms other methods in performance.
INFORMATION SCIENCES
(2022)
Article
Chemistry, Analytical
Abhijeet Sahu, Katherine Davis
Summary: False alerts caused by misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to significant economic and operational damage. To address this issue, a research proposes an evidence theoretic approach that uses Dempster-Shafer combination rules to reduce false alerts. The approach is demonstrated in a cyber-physical power system testbed, and classifiers are trained using datasets from Man-In-The-Middle attack emulation in a synthetic electric grid. The results show the effectiveness of the proposed method.
Article
Computer Science, Artificial Intelligence
Matthew Beechey, Konstantinos G. Kyriakopoulos, Sangarapillai Lambotharan
Summary: The study introduces a novel evidence-based feature selection method for network security, allowing security analysts to rank features in terms of uncertainty levels without expert knowledge. This approach enables fast and accurate detection and differentiation of cyber threats, outperforming or at least matching state-of-the-art techniques.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Mathematics
Harish Garg, R. Sujatha, D. Nagarajan, J. Kavikumar, Jeonghwan Gwak
Summary: This paper studies the interval probability problems of picture fuzzy sets and their belief structure, introducing the concept of evidence theory using DST. Through defining the concept of interval probability distribution and discussing its properties, an illustrative example related to the decision-making process is utilized to demonstrate the application of the presented work.
JOURNAL OF MATHEMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Pengyu Xue, Liguo Fei, Weiping Ding
Summary: Factors such as global climate change, environmental damage, and human activities have led to an increase in natural disasters, highlighting the importance of disaster prevention and response. Volunteers play a crucial role in rescue efforts, but their allocation poses challenges due to their voluntary and fragmented characteristics. This study proposes a volunteer assignment method that takes into account various factors and introduces L-T2FNs and prospect theory to enhance certainty and consider the psychology of disaster victims. An algorithm is developed to reduce evidence fusion conflicts and provide a solution for allocating volunteers to appropriate disaster sites, as demonstrated using the 7.8 magnitude earthquake in Turkey in 2023.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Information Systems
Yuanpeng He, Yong Deng
Summary: A new method called two-dimensional quantum mass function (TDQMF) is proposed to handle uncertain quantum information, which offers more flexibility and effectiveness in handling uncertainty in the field of quantum compared with the original quantum mass function.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Xinyang Deng, Siyu Xue, Wen Jiang
Summary: This paper proposes a quantization scheme for mass functions in order to solve the uncertainty involved in information fusion. The proposed quantum model, averaging operator, and combination rule demonstrate effective performance.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
C. Lopez-Molina, S. Iglesias-Rey, B. De Baets
Summary: Quantitative image comparison is a critical topic in image processing literature, with diverse applications. Existing measures of comparison often overlook the context in which the comparison takes place. This paper presents a context-aware comparison method for binary images, tested on the BSDS500 benchmark.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Lorenz Linhardt, Klaus-Robert Mueller, Gregoire Montavon
Summary: This paper investigates the issue of mismatches between the decision strategy of the explainable model and the user's domain knowledge, and proposes a new method EGEM to mitigate hidden flaws in the model. Experimental results demonstrate that the approach can significantly reduce reliance on Clever Hans strategies and improve the accuracy of the model on new data.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Zhimin Shao, Weibei Dou, Yu Pan
Summary: This paper proposes a novel algorithm, Dual-level Deep Evidential Fusion (DDEF), to integrate multimodal information at both the BBA level and multimodal level, aiming to enhance accuracy, robustness, and reliability. The DDEF approach utilizes the Dirichlet framework and BBA methods for effective uncertainty estimation and employs the Dempster-Shafer Theory for dual-level fusion. The experimental results show that the proposed DDEF outperforms existing methods in synthetic digit classification and real-world medical prognosis after BCI treatment.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Abhishek K. Ghosh, Danilo S. Catelli, Samuel Wilson, Niamh C. Nowlan, Ravi Vaidyanathan
Summary: The inability of current FM monitoring methods to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. This investigation introduces a novel wearable FM monitor with a heterogeneous sensor suite and a data fusion architecture to efficiently capture and separate FM from interfering artifacts. The performance of the device and architecture were validated through at-home use, demonstrating high accuracy in detecting and recognizing FM events. This research is a major milestone in the development of low-cost wearable FM monitors for pervasive monitoring of FM in unsupervised environments.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jianlei Kong, Xiaomeng Fan, Min Zuo, Muhammet Deveci, Xuebo Jin, Kaiyang Zhong
Summary: In this study, we propose an intelligent traffic flow prediction framework based on the adaptive dual-graphic transformer with a cross-fusion strategy, aiming to uncover latent graphic feature representations that transcend temporal and spatial limitations. By establishing a traffic spatiotemporal prediction model using a cross-fusion attention mechanism, our proposed model achieves superior prediction performance on practical urban traffic flow datasets, particularly for long-term predictions.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huilai Zhi, Jinhai Li
Summary: This article addresses the issue that conflict analysis based on single-valued information systems is no longer valid. It proposes a conflict analysis method based on component similarity, which uses three-way n-valued concept lattices to handle set-valued formal contexts and realizes fast conflict analysis from an information fusion viewpoint. Experimental results verify the effectiveness of this method in reducing time consumption.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huchang Liao, Jiaxin Qi, Jiawei Zhang, Chonghui Zhang, Fan Liu, Weiping Ding
Summary: In this paper, a hospital selection approach based on a fuzzy multi-criterion decision-making method is proposed. This approach considers sentiment evaluation values of unstructured data from online reviews and structured data of public indexes simultaneously. The methodology involves collecting and processing online reviews, classifying topics and sentiments, quantifying sentiment analysis results using fuzzy numbers, and obtaining final preference scores of hospitals based on patients' preferences. A case study and robustness analysis are conducted to validate the effectiveness of the method.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Faramarz Farhangian, Rafael M. O. Cruz, George D. C. Cavalcanti
Summary: The proliferation of social networks has posed challenges in combating fake news, but automatic fake news detection using artificial intelligence has become more feasible. This paper revisits the definitions and perspectives of fake news and proposes an updated taxonomy, based on multiple criteria, for the field. The study finds that optimal feature extraction techniques vary depending on the dataset, and context-dependent models based on transformer models consistently exhibit superior performance.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti, Rafael M. O. Cruz
Summary: In this study, a dynamic selection technique is proposed to handle sparse and overlapped data. The technique leverages the relationships between instances and classifiers to learn a dynamic classifier combination rule. Experimental results show that the proposed method outperforms static selection and other dynamic selection techniques.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Bin Yu, Ruihui Xu, Mingjie Cai, Weiping Ding
Summary: This paper introduces a clustering method based on non-Euclidean metric and multi-granularity staged clustering to address the challenges posed by complex spatial structure data to traditional clustering methods. The method improves the similarity measure and employs an attenuation-diffusion pattern for local to global clustering, achieving good clustering results.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jian Zhu, Pengbo Hu, Bingqian Li, Yi Zhou
Summary: The acquisition of multi-view hash representation for heterogeneous data is highly important for multimedia retrieval. Current approaches suffer from limited retrieval precision due to insufficient integration of multi-view features and failure to effectively utilize metric information from diverse samples. In this paper, we propose an innovative method called Fast Metric Multi-View Hashing (FMMVH), which demonstrates the superiority of gate-based fusion over traditional methods. We also introduce a novel deep metric loss function to leverage metric information from dissimilar samples. By optimizing and employing model compression techniques, our FMMVH method significantly outperforms existing state-of-the-art methods on benchmark datasets, with up to 7.47% improvement in mean Average Precision (mAP).
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Fayaz Ali Dharejo, Iyyakutti Iyappan Ganapathi, Muhammad Zawish, Basit Alawode, Moath Alathbah, Naoufel Werghi, Sajid Javed
Summary: The resource-limited nature of underwater vision equipment affects underwater robotics and ocean engineering tasks. Super-resolution methods, particularly using Vision Transformers (ViTs), have emerged to enhance low-resolution underwater images. However, ViTs face challenges in handling severe degradation in underwater imaging. In contrast, Multi-scale ViTs (MViTs) overcome these challenges by preserving long-range dependencies through evolving channel capacity. This study proposes a novel algorithm, SwinWave-SR, for efficient and accurate multi-scale super-resolution for underwater images.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Weiwei Jiang, Haoyu Han, Yang Zhang, Jianbin Mu
Summary: This study incorporates federated learning and split learning paradigms with satellite-terrestrial integrated networks and introduces a split-then-federated learning framework and federated split learning with long short-term memory to handle sequential data in STINs. The proposed solution is demonstrated to be effective through a case study of electricity theft detection based on a real-world dataset.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Najah Abuali, Mohammad Bilal Khan, Farman Ullah, Mohammad Hayajneh, Hikmat Ullah, Shahid Mumtaz
Summary: The demand for innovative solutions in biomedical systems for precise diagnosis and management of critical diseases is increasing. A promising technology, non-invasive and intelligent Internet of Medical Things (IoMT) system, emerges to assess patients with reduced health risks. This research introduces a comprehensive framework for early diagnosis of respiratory abnormalities through RF sensing and SDR technology. The results highlight the superior performance of deep learning frameworks in classifying respiratory anomalies.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Shichen Huang, Weina Fu, Zhaoyue Zhang, Shuai Liu
Summary: In the era of adversarial machine learning (AML), developing robust and generalized algorithms has become a key research topic. This study proposes a global similarity matching module and a global-local cognition fusion training mechanism based on relationship adversarial sample generation to improve image-text matching algorithm. Experimental results show significant improvements in accuracy and robustness, performing well in facing security challenges and promoting the fusion of visual and linguistic modalities.
INFORMATION FUSION
(2024)