Information Fusion最新文献

{"title":"An autoencoder-based confederated clustering leveraging a robust model fusion strategy for federated unsupervised learning","authors":"Nahid Hasan ,&nbsp;Md. Golam Rabiul Alam ,&nbsp;Shamim H. Ripon ,&nbsp;Phuoc Hung Pham ,&nbsp;Mohammad Mehedi Hassan","doi":"10.1016/j.inffus.2024.102751","DOIUrl":"10.1016/j.inffus.2024.102751","url":null,"abstract":"<div><div>Concerns related to data privacy, security, and ethical considerations become more prominent as data volumes continue to grow. In contrast to centralized setups, where all data is accessible at a single location, model-based clustering approaches can be successfully employed in federated settings. However, this approach to clustering in federated settings is still relatively unexplored and requires further attention. As federated clustering deals with remote data and requires privacy and security to be maintained, it poses particular challenges as well as possibilities. While model-based clustering offers promise in federated environments, a robust model aggregation method is essential for clustering rather than the generic model aggregation method like Federated Averaging (FedAvg). In this research, we proposed an autoencoder-based clustering method by introducing a novel model aggregation method FednadamN, which is a fusion of Adam and Nadam optimization approaches in a federated learning setting. Therefore, the proposed FednadamN adopted the adaptive learning rates based on the first and second moments of gradients from Adam which offered fast convergence and robustness to noisy data. Furthermore, FednadamN also incorporated the Nesterov-accelerated gradients from Nadam to further enhance the convergence speed and stability. We have studied the performance of the proposed Autoencoder-based clustering methods on benchmark datasets and using the novel FednadamN model aggregation strategy. It shows remarkable performance gain in federated clustering in comparison to the state-of-the-art.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102751"},"PeriodicalIF":14.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Credibility-based multi-sensor fusion for non-Gaussian conversion error mitigation","authors":"Quanbo Ge ,&nbsp;Kai Lin ,&nbsp;Zhongyuan Zhao","doi":"10.1016/j.inffus.2024.102704","DOIUrl":"10.1016/j.inffus.2024.102704","url":null,"abstract":"<div><div>In a complex environment, a multi-sensor fusion algorithm can compensate for the limitations of a single sensor’s performance. In a distributed fusion algorithm, sensors need to transmit local estimates to a central coordinate system, and the existence of coordinate transformation uncertainty can undermine the performance of data transmission. Therefore, this paper proposes a multi-sensor distributed fusion method based on trustworthiness. Firstly, considering the presence of non-Gaussian conversion errors, a credibility-based multi-sensor fusion framework is constructed. Secondly, to address the difficulty in estimating conversion errors when measurement errors follow a non-Gaussian distribution, an optimization model is constructed based on actual measurement information to estimate the distribution of non-Gaussian conversion errors. Then, in response to the non-linear and non-Gaussian characteristics of the target optimization function, a particle swarm optimization algorithm based on trustworthiness adaptive weights is proposed to estimate the coordinate transformation errors. Finally, given the inconsistency in local estimates due to missing sensor measurements or significant errors in a non-Gaussian complex environment, a maximum correntropy consensus algorithm is proposed to avoid the trustworthiness calculation being affected by the current measurement errors, thereby improving the accuracy of the global estimation.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102704"},"PeriodicalIF":14.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large model-driven hyperscale healthcare data fusion analysis in complex multi-sensors","authors":"Jianhui Lv ,&nbsp;Byung-Gyu Kim ,&nbsp;B.D. Parameshachari ,&nbsp;Adam Slowik ,&nbsp;Keqin Li","doi":"10.1016/j.inffus.2024.102780","DOIUrl":"10.1016/j.inffus.2024.102780","url":null,"abstract":"<div><div>In the era of big data and artificial intelligence, healthcare data fusion analysis has become difficult because of the large amounts and different types of sources involved. Traditional methods are ineffective at processing and examination procedures for such complex multi-sensors of hyperscale healthcare data. To address this issue, we propose a novel large model-driven approach for hyperscale healthcare data fusion analysis in complex multi-sensor multi-sensors. Our method integrates data from various medical sensors and sources, using large models to extract and fuse information from structured and unstructured healthcare data. Then, we integrate these features with structured data using a hierarchical residual connected LSTM network, enhancing the model's ability to capture local and global context. Furthermore, we introduce a dynamic ReLU activation function and attention mechanism that allow us to adjust the depth of our networks dynamically while focusing only on relevant information. The experiments on MIMIC-III and eICU-CRD datasets demonstrate the superiority of the proposed method in terms of accuracy, efficiency, and robustness compared to state-of-the-art methods. Therefore, the proposed method provides valuable insights into the potential of large model-driven approaches for tackling the challenges of hyperscale healthcare data fusion analysis in complex multi-sensors.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102780"},"PeriodicalIF":14.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-level information fusion for missing multi-label learning based on stochastic concept clustering","authors":"Zhiming Liu ,&nbsp;Jinhai Li ,&nbsp;Xiao Zhang ,&nbsp;Xizhao Wang","doi":"10.1016/j.inffus.2024.102775","DOIUrl":"10.1016/j.inffus.2024.102775","url":null,"abstract":"<div><div>Missing multi-label learning is to address the problem of missing labels in multi-label datasets for multi-label classification tasks. Notably, the complex dependencies that typically exist between labels make accurate classification particularly challenging in the presence of missing labels. Some existing missing multi-label classification models often utilize feature selection to effectively recognize the dependencies between labels and features. However, they are ineffective at capturing hierarchical relationships of feature information, probably leading to a decline in prediction performance. To address this problem, this paper proposes a missing multi-label classification model based on multi-level stochastic concept clustering (MML-MSCC) to make dependencies between features and labels recognized more accurately and prediction performance better. In our model, optimal granularity selection is achieved through the global mutual information between features and labels, which makes the study of stochastic granule concept across multiple granularities. Furthermore, we utilize a stochastic concept clustering method to combine similar feature information for the purpose of making the missing label completion more reasonable. Note that stochastic granule concept clustering is performed with cross-granularity, thereby effectively capturing hierarchical relationships among feature information. Finally, to evaluate the performance of our model, we compare the MML-MSCC model with 9 existing missing multi-label classification models on 12 open datasets in terms of six evaluation metrics.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102775"},"PeriodicalIF":14.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Mixed-order Graph Learning for incomplete multi-view clustering","authors":"Wei Guo ,&nbsp;Hangjun Che ,&nbsp;Man-Fai Leung ,&nbsp;Long Jin ,&nbsp;Shiping Wen","doi":"10.1016/j.inffus.2024.102776","DOIUrl":"10.1016/j.inffus.2024.102776","url":null,"abstract":"<div><div>Incomplete multi-view clustering (IMVC) aims to address the clustering problem of multi-view data with partially missing samples and has received widespread attention in recent years. Most existing IMVC methods still have the following issues that require to be further addressed. They focus solely on the first-order correlation information among samples, neglecting the more intricate high-order connections. Additionally, these methods always overlook the noise or inaccuracies in the self-representation matrix. To address above issues, a novel method named Robust Mixed-order Graph Learning (RMoGL) is proposed for IMVC. Specifically, to enhance the robustness to noise, the self-representation matrices are separated into clean graphs and noise graphs. To capture complex high-order relationships among samples, the dynamic high-order similarity graphs are innovatively constructed from the recovered data. The clean graphs are endowed with mixed-order information and tend towards to obtain a consensus graph via a self-weighted manner. An efficient algorithm based on Alternating Direction Method of Multipliers (ADMM) is designed to solve the proposed RMoGL, and superior performance is demonstrated by compared with nine state-of-the-art methods across eight datasets. The source code of this work is available at <span><span>https://github.com/guowei1314/RMoGL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102776"},"PeriodicalIF":14.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Eco-friendly integration of shared autonomous mobility on demand and public transit based on multi-source data","authors":"Xinghua Liu ,&nbsp;Xuan Shao ,&nbsp;Ye Li","doi":"10.1016/j.inffus.2024.102771","DOIUrl":"10.1016/j.inffus.2024.102771","url":null,"abstract":"<div><div>Shared Autonomous Mobility on Demand (SAMoD) is considered one of the most efficient modes of transportation for future cities and has thus gained significant attention. However, it may attract the ridership of public transportation (PT) systems, leading to negative externalities such as traffic congestion and environmental pollution. Greater social benefits can only be realized by seamlessly integrating SAMoD with PT systems, leveraging SAMoD’s flexibility and PT’s large-scale transport capacity. Therefore, this study considers the various complex potential interactions between SAMoD and PT (such as subways, BRT, and buses), including first and last-mile services and alternatives, and aims to investigate an optimization framework for network construction and passenger flow allocation in a SAMoD-PT integrated system to achieve an optimal balance between sustainability and efficiency. Specifically, we first applied a hierarchical weighted K-means clustering algorithm to cluster multi-source travel demands and used the Voronoi partition algorithm for regional division. Secondly, potential connections in the multi-modal transportation network were determined using a greedy triangulation algorithm. Subsequently, life cycle assessment and continuous approximation algorithms were employed to quantify environmental costs (including greenhouse gas emissions and energy consumption) as well as passenger and operator costs, respectively. Finally, we constructed a multi-objective optimization model and solved it using the weighted sum method, obtaining the Pareto frontier to balance sustainability and efficiency in the SAMoD-PT integrated system. The results show that the optimized SAMoD-PT integrated system can significantly reduce social costs, mitigate inter-modal competition effects, and ensure the central role of PT. This highlights the great potential of cooperation between SAMoD and PT. These findings provide valuable insights for developing countries on how to plan more efficient and environmentally friendly multi-modal urban transportation systems in the future.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102771"},"PeriodicalIF":14.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resolving multimodal ambiguity via knowledge-injection and ambiguity learning for multimodal sentiment analysis","authors":"Xianbing Zhao ,&nbsp;Xuejiao Li ,&nbsp;Ronghuan Jiang ,&nbsp;Buzhou Tang","doi":"10.1016/j.inffus.2024.102745","DOIUrl":"10.1016/j.inffus.2024.102745","url":null,"abstract":"<div><div>Multimodal Sentiment Analysis (MSA) utilizes complementary multimodal features to predict sentiment polarity, which mainly involves language, vision, and audio modalities. Existing multimodal fusion methods primarily consider the complementarity of different modalities, while neglecting the ambiguity caused by conflicts between modalities (i.e. the text modality predicts positive sentiment while the visual modality predicts negative sentiment). To well diminish these conflicts, we develop a novel multimodal ambiguity learning framework, namely RMA, Resolving Multimodal Ambiguity via Knowledge-Injection and Ambiguity Learning for Multimodal Sentiment Analysis. Specifically, We introduce and filter external knowledge to enhance the consistency of cross-modal sentiment polarity prediction. Immediately, we explicitly measure ambiguity and dynamically adjust the impact between the subordinate modalities and the dominant modality to simultaneously consider the complementarity and conflicts of multiple modalities during multimodal fusion. Experiments demonstrate the dominantity of our proposed model across three public multimodal sentiment analysis datasets CMU-MOSI, CMU-MOSEI, and MELD.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102745"},"PeriodicalIF":14.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information fusion for large-scale multi-source data based on the Dempster-Shafer evidence theory","authors":"Qinli Zhang ,&nbsp;Pengfei Zhang ,&nbsp;Tianrui Li","doi":"10.1016/j.inffus.2024.102754","DOIUrl":"10.1016/j.inffus.2024.102754","url":null,"abstract":"<div><div>There exists many large-scale multi-source data, ranging from genetic information to medical records, and military intelligence. The inherent intricacies and uncertainties embedded within these data sources pose significant challenges to the process of information fusion. Owing to its exceptional capacity to represent data uncertainty, Dempster-Shafer (D-S) evidence theory has emerged as a widely utilized approach in information fusion. However, the evidence theory encounters three significant issues when applied to multi-source data information fusion: (1) the conversion of sample information into evidence and the construction of the basic probability assignment (BPA) function; (2) the resolution of conflicting evidence; and (3) the mitigation of exponential explosion in computation. Addressing the aforementioned challenges, this paper delves into the information fusion strategies for large-scale multi-source data based on Dempster-Shafer evidence theory. Initially, the concept of support matrix is introduced and the data matrix is transformed into a support matrix to address the construction challenges associated with BPA. Next, a method for addressing evidence conflicts is introduced by incorporating an additional data source composed of average values. Furthermore, a solution for mitigating high computational complexity is presented through the utilization of a hierarchical fusion approach. Finally, experimental results show that compared with other five advanced information fusion methods, our information method has improved the classification accuracy by 4.66% on average and reduced the time by 66.35% on average. Hence, our method is both efficient and effective, demonstrating exceptional performance in information fusion.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102754"},"PeriodicalIF":14.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WaterHE-NeRF: Water-ray matching neural radiance fields for underwater scene reconstruction","authors":"Jingchun Zhou ,&nbsp;Tianyu Liang ,&nbsp;Dehuan Zhang ,&nbsp;Siyuan Liu ,&nbsp;Junsheng Wang ,&nbsp;Edmond Q. Wu","doi":"10.1016/j.inffus.2024.102770","DOIUrl":"10.1016/j.inffus.2024.102770","url":null,"abstract":"<div><div>Neural Radiance Field (NeRF) technology demonstrates immense potential in novel viewpoint synthesis tasks due to its physics-based volumetric rendering process, which is particularly promising in underwater scenes. However, existing underwater NeRF methods face challenges in handling light attenuation caused by the water medium and the lack of real Ground Truth (GT) supervision. To address these issues, we propose WaterHE-NeRF, a novel approach incorporating a water-ray matching field developed based on Retinex theory. This field precisely encodes color, density, and illuminance attenuation in three-dimensional space. WaterHE-NeRF employs an illuminance attenuation mechanism to generate degraded and clear multi-view images, optimizing image restoration by combining reconstruction loss with Wasserstein distance. Furthermore, using histogram equalization (HE) as pseudo-GT, WaterHE-NeRF enhances the network’s accuracy in preserving original details and color distribution. Extensive experiments on real underwater and synthetic datasets validate the effectiveness of WaterHE-NeRF.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102770"},"PeriodicalIF":14.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bounded rationality consensus reaching process with regret theory and weighted Moment estimation for multi-attribute group decision making","authors":"Feifei Jin ,&nbsp;Xiaoxuan Gao ,&nbsp;Ligang Zhou","doi":"10.1016/j.inffus.2024.102778","DOIUrl":"10.1016/j.inffus.2024.102778","url":null,"abstract":"<div><div>Probabilistic linguistic term sets perform a particularly active role in the field of decision-making, particularly regarding decision-makers (DMs) who are inclined to convey evaluative information through natural linguistic variables. To effectively improve the current dilemma of multi-attribute group decision-making (MAGDM), this article put forward a new probabilistic linguistic MAGDM method with weighted Moment estimation. First, taking into account the psychological aspect of regret aversion among DMs, we use regret theory to transform the original decision-making matrix into the utility matrix, in which DMs usually exhibit limited rationality during the process of MAGDM. Then, a combined weighting method and a weighted Moment estimation model are investigated to determine the attribute weights, which are more scientifically and reasonably. Subsequently, in the process of consensus reaching process, a new trust propagation mechanism is designed to derive the weights of experts and the adjustment coefficients, in which we consider the shortest and longest propagation paths among DMs. Finally, an empirical validation of the MAGDM method's applicability is conducted utilizing raw coal quality assessment, accompanied by sensitivity and comparative analyses that underscore its advantages and robustness.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102778"},"PeriodicalIF":14.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}