Information Fusion最新文献

{"title":"Large model-driven hyperscale healthcare data fusion analysis in complex multi-sensors","authors":"","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":null,"pages":null},"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":"Eco-friendly integration of shared autonomous mobility on demand and public transit based on multi-source data","authors":"","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":null,"pages":null},"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":"Information fusion for large-scale multi-source data based on the Dempster-Shafer evidence theory","authors":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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}

{"title":"DSAP: Analyzing bias through demographic comparison of datasets","authors":"","doi":"10.1016/j.inffus.2024.102760","DOIUrl":"10.1016/j.inffus.2024.102760","url":null,"abstract":"<div><div>In the last few years, Artificial Intelligence (AI) systems have become increasingly widespread. Unfortunately, these systems can share many biases with human decision-making, including demographic biases. Often, these biases can be traced back to the data used for training, where large uncurated datasets have become the norm. Despite our awareness of these biases, we still lack general tools to detect, quantify, and compare them across different datasets. In this work, we propose DSAP (Demographic Similarity from Auxiliary Profiles), a two-step methodology for comparing the demographic composition of datasets. First, DSAP uses existing demographic estimation models to extract a dataset’s demographic profile. Second, it applies a similarity metric to compare the demographic profiles of different datasets. While these individual components are well-known, their joint use for demographic dataset comparison is novel and has not been previously addressed in the literature. This approach allows three key applications: the identification of demographic blind spots and bias issues across datasets, the measurement of demographic bias, and the assessment of demographic shifts over time. DSAP can be used on datasets with or without explicit demographic information, provided that demographic information can be derived from the samples using auxiliary models, such as those for image or voice datasets. To show the usefulness of the proposed methodology, we consider the Facial Expression Recognition task, where demographic bias has previously been found. The three applications are studied over a set of twenty datasets with varying properties. The code is available at <span><span>https://github.com/irisdominguez/DSAP</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":null,"pages":null},"PeriodicalIF":14.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative technology for human emotion recognition: A scoping review","authors":"","doi":"10.1016/j.inffus.2024.102753","DOIUrl":"10.1016/j.inffus.2024.102753","url":null,"abstract":"<div><div>Affective computing stands at the forefront of artificial intelligence (AI), seeking to imbue machines with the ability to comprehend and respond to human emotions. Central to this field is emotion recognition, which endeavors to identify and interpret human emotional states from different modalities, such as speech, facial images, text, and physiological signals. In recent years, important progress has been made in generative models, including Autoencoder, Generative Adversarial Network, Diffusion Model, and Large Language Model. These models, with their powerful data generation capabilities, emerge as pivotal tools in advancing emotion recognition. However, up to now, there remains a paucity of systematic efforts that review generative technology for emotion recognition. This survey aims to bridge the gaps in the existing literature by conducting a comprehensive analysis of over 330 research papers until June 2024. Specifically, this survey will firstly introduce the mathematical principles of different generative models and the commonly used datasets. Subsequently, through a taxonomy, it will provide an in-depth analysis of how generative techniques address emotion recognition based on different modalities in several aspects, including data augmentation, feature extraction, semi-supervised learning, cross-domain, etc. Finally, the review will outline future research directions, emphasizing the potential of generative models to advance the field of emotion recognition and enhance the emotional intelligence of AI systems.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":null,"pages":null},"PeriodicalIF":14.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561069","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":"Deep learning based 3D segmentation in computer vision: A survey","authors":"","doi":"10.1016/j.inffus.2024.102722","DOIUrl":"10.1016/j.inffus.2024.102722","url":null,"abstract":"<div><div>3D segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving and robotics. It has received significant attention from the computer vision, graphics and machine learning communities. Conventional methods for 3D segmentation, based on hand-crafted features and machine learning classifiers, lack generalization ability. Driven by their success in 2D computer vision, deep learning techniques have recently become the tool of choice for 3D segmentation tasks. This has led to an influx of many methods in the literature that have been evaluated on different benchmark datasets. Whereas survey papers on RGB-D and point cloud segmentation exist, there is a lack of a recent in-depth survey that covers all 3D data modalities and application domains. This paper fills the gap and comprehensively surveys the recent progress in deep learning-based 3D segmentation techniques. We cover over 230 works from the last six years, analyze their strengths and limitations, and discuss their competitive results on benchmark datasets. The survey provides a summary of the most commonly used pipelines and finally highlights promising research directions for the future.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":null,"pages":null},"PeriodicalIF":14.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554705","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":"Mid-Net: Rethinking efficient network architectures for small-sample vascular segmentation","authors":"","doi":"10.1016/j.inffus.2024.102777","DOIUrl":"10.1016/j.inffus.2024.102777","url":null,"abstract":"<div><div>Deep learning-based medical image segmentation methods have demonstrated significant clinical applications. However, training these methods on small-sample vascular datasets remains challenging due to the scarcity of labeled data and severe category imbalance. To address this, this paper proposes Mid-Net, which fully exploits the often-overlooked feature representation potential of the middle-layer network through cross-layer guidance to improve model learning efficiency in data-constrained environments. Mid-Net consists of three core components: the encoding path, the guidance path, and the calibration path. In the encoding path, a feature pyramid structure with large kernel convolutions is used to extract semantic information at different scales. The guidance path combines the sensitivity of the shallow-layer network to spatial details with the global perceptual abilities of the deep-layer network to provide more discriminative guidance to the middle-layer network in a feature-decoupled manner. The calibration path further calibrates the spatial location information of the middle-layer network through end-to-end supervised learning. Experiments conducted on the publicly available retinal vascular datasets DRIVE, STARE, and CHASE_DB1, as well as coronary angiography datasets DCA1 and CHUAC, demonstrate that Mid-Net achieves superior segmentation results with lower computational resource requirements compared to state-of-the-art methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":null,"pages":null},"PeriodicalIF":14.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571538","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":"Polyp-Mamba: A Hybrid Multi-Frequency Perception Gated Selection Network for polyp segmentation","authors":"","doi":"10.1016/j.inffus.2024.102759","DOIUrl":"10.1016/j.inffus.2024.102759","url":null,"abstract":"<div><div>Accurate segmentation of polyps in the colorectal region is crucial for medical diagnosis and the localization of polyp areas. However, challenges arise from blurred boundaries due to the similarity between polyp edges and surrounding tissues, variable polyp morphology, and speckle noise. To address these challenges, we propose a Hybrid Multi-Frequency Perception Gated Selection Network (Polyp-Mamba) for precise polyp segmentation. First, we design a dual multi-frequency fusion encoder that employs Mamba and ResNet to quickly and effectively learn global and local features in polyp images. Specifically, we incorporate a novel Hybrid Multi-Frequency Fusion Module (HMFM) within the encoder, using discrete cosine transform to analyze features from multiple spectral perspectives. This approach mitigates the issue of blurred polyp boundaries caused by their similarity to surrounding tissues, effectively integrating local and global features. Additionally, we construct a Gated Selection Decoder to suppress irrelevant feature regions in the encoder and introduce deep supervision to guide decoder features to align closely with the labels. We conduct extensive experiments using five commonly used polyp test datasets. Comparisons with 14 state-of-the-art segmentation methods demonstrate that our approach surpasses traditional methods in sensitivity to different polyp images, robustness to variations in polyp size and shape, speckle noise, and distribution similarity between surrounding tissues and polyps. Overall, our method achieves superior mDice scores on five polyp test datasets compared to state-of-the-art methods, indicating better performance in polyp segmentation.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":null,"pages":null},"PeriodicalIF":14.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571536","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}