Information Processing & Management最新文献

Unsupervised Adaptive Hypergraph Correlation Hashing for multimedia retrieval 用于多媒体检索的无监督自适应超图相关性哈希算法

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-11-18 DOI: 10.1016/j.ipm.2024.103958

Yunfei Chen , Yitian Long , Zhan Yang , Jun Long

Cross-modal hashing has attracted widespread attention from researchers due to its capabilities to handle large volumes of heterogeneous multimedia information with fast retrieval speed and low storage cost. However, current cross-modal hashing methods still face issues such as incomplete embedding of semantic correlation information and long parameter tuning cycles. To address these problems, we propose a method called Unsupervised Adaptive Hypergraph Correlation Hashing (UAHCH). First, the hypergraph-based correlation enhanced hashing constructs a hypergraph based on semantic information and correlation information, leveraging a hypergraph neural network to integrate the hypergraph information into the hash codes, ensuring the richness of the semantics and the integrity of correlation relationships. Next, the fast parameter adaptive strategy is designed for the automated optimization of neural network parameters for the UAHCH method and various neural network models, achieving optimal performance more efficiently. Finally, comprehensive experiments are conducted on widely used datasets. The results show that the proposed UAHCH method achieves superior performance, with average improvements of 3.06% on MIRFlickr, 1.45% on NUS-WIDE, and 4.65% on MSCOCO compared to the latest baseline methods. The code has been made publicly available at https://github.com/YunfeiChenMY/UAHCH.

跨模态哈希算法能处理大量异构多媒体信息，检索速度快，存储成本低，因此受到研究人员的广泛关注。然而，目前的跨模态哈希方法仍面临语义相关信息嵌入不完整、参数调整周期长等问题。为了解决这些问题，我们提出了一种称为无监督自适应超图相关散列（UAHCH）的方法。首先，基于超图的相关性增强散列根据语义信息和相关性信息构建超图，利用超图神经网络将超图信息整合到散列代码中，确保语义的丰富性和相关关系的完整性。其次，设计了快速参数自适应策略，用于自动优化 UAHCH 方法和各种神经网络模型的神经网络参数，更高效地实现最优性能。最后，在广泛使用的数据集上进行了综合实验。结果表明，与最新的基线方法相比，所提出的 UAHCH 方法性能优越，在 MIRFlickr 上平均提高了 3.06%，在 NUS-WIDE 上平均提高了 1.45%，在 MSCOCO 上平均提高了 4.65%。代码已在 https://github.com/YunfeiChenMY/UAHCH 上公开发布。

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引用次数: 0

Enhancing robustness in implicit feedback recommender systems with subgraph contrastive learning 利用子图对比学习增强隐式反馈推荐系统的鲁棒性

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-11-16 DOI: 10.1016/j.ipm.2024.103962

Yi Yang , Shaopeng Guan , Xiaoyang Wen

Contrastive learning operates by distinguishing differences between various nodes to facilitate item recommendations. However, current graph contrastive learning (GCL) methods suffer from insufficient robustness. To mitigate the impact of noise and accurately capture user preferences, we propose a subgraph-based GCL method: SubGCL. Firstly, we devise a dynamic perceptual signal extractor that leverages node degree and neighborhood information to model subgraphs corresponding to nodes and compute mutual information scores. This approach enhances view adaptivity, thereby improving data augmentation robustness against noise perturbations. Secondly, we develop an association graph self-attention propagation mechanism. This mechanism constructs node clusters by randomly sampling nodes and edges, facilitating self-attention propagation on the graph to learn cluster associations and enhance recommendation accuracy. Finally, we reconstruct graph structures through recommendation loss and update node embeddings via contrastive learning to bolster the model’s accuracy and robustness in implicit feedback data. We conducted experiments on three publicly available real-world datasets. Results demonstrate that, compared to existing contrastive learning recommendation approaches, SubGCL achieves an average improvement of 4.96% and 3.98% in Recall and NDCG metrics, respectively.

对比学习通过区分不同节点之间的差异来促进项目推荐。然而，目前的图对比学习（GCL）方法存在鲁棒性不足的问题。为了减轻噪音的影响并准确捕捉用户偏好，我们提出了一种基于子图的 GCL 方法：SubGCL。首先，我们设计了一种动态感知信号提取器，利用节点度和邻域信息对节点对应的子图进行建模，并计算互信息得分。这种方法增强了视图适应性，从而提高了数据增强对噪声扰动的鲁棒性。其次，我们开发了一种关联图自关注传播机制。该机制通过随机抽样节点和边来构建节点簇，促进图上的自关注传播，从而学习簇关联并提高推荐准确性。最后，我们通过推荐损失重构图结构，并通过对比学习更新节点嵌入，以提高模型在隐式反馈数据中的准确性和鲁棒性。我们在三个公开的真实世界数据集上进行了实验。结果表明，与现有的对比学习推荐方法相比，SubGCL 在 Recall 和 NDCG 指标上分别平均提高了 4.96% 和 3.98%。

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引用次数: 0

Patients' cognitive and behavioral paradoxes in the process of adopting conflicting health information: A dynamic perspective 患者在接受相互矛盾的健康信息过程中的认知和行为悖论：动态视角

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-11-15 DOI: 10.1016/j.ipm.2024.103939

Yan Jin , Di Zhao , Zhuo Sun , Chongwu Bi , Ruixian Yang , Shengli Deng

Diversified access to health information has increased the likelihood of encountering conflicting health messages, making it more difficult for patients to adopt information rationally. Prior research has primarily focused on the outcomes of patients' information adoption and responded to concerns by exploring the influences that led to these outcomes, overlooking a crucial aspect. Specifically, patients' cognitive and behavioral responses are continuously fluctuating during the process of information adoption. A total of 336 subjects (valid sample) participated in this study. A combination of situational experiments, grounded theory, and questionnaires was employed to develop a model of patients' adoption of conflicting health information. The concept of "trans-theory" was introduced to explain how patients' cognitive and behavioral responses changed at different segments of adoption. In contrast to prior studies viewing information adoption as a whole, we propose that the process can be divided into four distinct segments: information attention, comprehension, evaluation, and decision. Moreover, the sequential influence of information, ability, psychological, and environmental factors in the adoption process produces three common paradoxes in patients' cognitive and behavioral responses, affecting their ability to make rational adoption decisions. This study explores the dynamics of information adoption from the patient's perspective, providing novel insights into the study of conflicting health information adoption and offering guidance for designing more effective interventions for facilitating rational adoption by patients. Additionally, it can help the healthcare system better understand patients' cognitive and behavioral responses to deliver more effective healthcare services.

健康信息获取渠道的多样化增加了遇到相互矛盾的健康信息的可能性，使患者更难理性地采纳信息。以往的研究主要关注患者采用信息的结果，并通过探讨导致这些结果的影响因素来回应人们的担忧，但忽略了一个重要方面。具体来说，在信息采纳过程中，患者的认知和行为反应是不断波动的。共有 336 名受试者（有效样本）参与了本研究。研究综合运用了情景实验、基础理论和问卷调查等方法，建立了患者采用相互冲突的健康信息的模型。研究引入了 "跨理论 "的概念，以解释患者在采用信息的不同阶段的认知和行为反应是如何变化的。与之前将信息采纳视为一个整体的研究不同，我们建议将这一过程分为四个不同的环节：信息注意、理解、评估和决策。此外，在采纳过程中，信息、能力、心理和环境因素的先后影响会在患者的认知和行为反应中产生三种常见的悖论，影响他们做出理性采纳决策的能力。本研究从患者的角度探讨了信息采纳的动态变化，为研究健康信息采纳的矛盾提供了新的见解，为设计更有效的干预措施促进患者理性采纳提供了指导。此外，它还能帮助医疗系统更好地了解患者的认知和行为反应，从而提供更有效的医疗服务。

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引用次数: 0

Domain disentanglement and fusion based on hyperbolic neural networks for zero-shot sketch-based image retrieval 基于双曲神经网络的基于零镜头草图的图像检索的域分解和融合

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-11-15 DOI: 10.1016/j.ipm.2024.103963

Qing Zhang , Jing Zhang , Xiangdong Su , Yonghe Wang , Feilong Bao , Guanglai Gao

With the advancement of zero-shot sketch-based image retrieval (ZS-SBIR) tasks, existing methods still encounter two major challenges: Euclidean space fails to effectively represent data with hierarchical structures, leading to non-discriminative retrieval features; relying solely on visual information is insufficient to align cross-domain features and maximize their domain generalization capabilities. To tackle these issues, this paper designs a hyperbolic neural networks based ZS-SBIR framework that considers domain disentanglement and fusion learning, called “DDFUS”. Specifically, we present a contrastive cross-modal learning method that guides the alignment of multi-domain visual representations with semantic representations in the hyperbolic space. This approach ensures that each visual representation possesses rich semantic hierarchical structure information. Furthermore, we propose a domain disentanglement method based on hyperbolic neural networks that employs paired hyperbolic encoders to decompose the representation of each domain into domain-invariant and domain-specific features to reduce information disturbance between domains. Moreover, we design an advanced cross-domain fusion method that promotes the fusion and exchange of multi-domain information through the reconstruction and generation of cross-domain samples. It significantly enhances the representation and generalization capabilities of domain-invariant features. Comprehensive experiments demonstrate that the mAP@all of our DDFUS model surpasses CNN-based models by 18.99 % on the Sketchy dataset, 1.93 % on the more difficult TU-Berlin dataset, and 11.4 % on the more challenging QuickDraw dataset.

随着基于零镜头草图的图像检索（ZS-SBIR）任务的发展，现有方法仍面临两大挑战：欧几里得空间无法有效表示具有层次结构的数据，导致检索特征缺乏区分度；仅仅依靠视觉信息不足以调整跨领域特征并最大限度地提高其领域泛化能力。为了解决这些问题，本文设计了一种基于双曲神经网络的 ZS-SBIR 框架，该框架考虑了领域分离和融合学习，称为 "DDFUS"。具体来说，我们提出了一种对比性跨模态学习方法，可引导多域视觉表征与双曲空间中的语义表征进行对齐。这种方法确保每个视觉表征都拥有丰富的语义层次结构信息。此外，我们还提出了一种基于双曲神经网络的域解缠方法，该方法采用成对双曲编码器将每个域的表征分解为域不变特征和域特定特征，以减少域之间的信息干扰。此外，我们还设计了一种先进的跨域融合方法，通过重建和生成跨域样本来促进多域信息的融合与交换。它大大增强了域不变特征的表示和泛化能力。综合实验证明，我们的 DDFUS 模型的 mAP@all 在 Sketchy 数据集上超过基于 CNN 的模型 18.99%，在难度更大的 TU-Berlin 数据集上超过 1.93%，在难度更高的 QuickDraw 数据集上超过 11.4%。

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引用次数: 0

Enhancing video rumor detection through multimodal deep feature fusion with time-sync comments 通过多模态深度特征融合与时间同步评论加强视频谣言检测

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-11-14 DOI: 10.1016/j.ipm.2024.103935

Ming Yin , Wei Chen , Dan Zhu , Jijiao Jiang

Rumors in videos have a stronger propagation compared to traditional text or image rumors. Most current studies on video rumor detection often rely on combining user and video modal information while neglecting the internal multimodal aspects of the video and the relationship between user comments and local segment of the video. To address this problem, we propose a method called Time-Sync Comment Enhanced Multimodal Deep Feature Fusion Model (TSC-MDFFM). It introduces time-sync comments to enhance the propagation structure of videos on social networks, supplementing missing contextual or additional information in videos. Time-sync comments focus on expressing users' views on specific points in time in the video, which helps to obtain more valuable segments from videos with high density information. The time interval from one keyframe to the next in a video is defined as a local segment. We thoroughly described this segment using time-sync comments, video keyframes, and video subtitle texts. The local segment sequences are ordered based on the video timeline and assigned time information, then fused to create the local feature representation of the video. Subsequently, we fused the text features, video motion features, and visual features of video comments at the feature level to represent the global features of the video. This feature not only captures the overall propagation trend of video content, but also provides a deep understanding of the overall features of the video. Finally, we will integrate local and global features for video rumor classification, to combine the local and global information of the video. We created a dataset called TSC-VRD, which includes time-sync comments and encompasses all visible information in videos. Extensive experimental results have shown superior performance of our proposed model compared to existing methods on the TSC-VRD dataset.

与传统的文字或图像谣言相比，视频中的谣言具有更强的传播性。目前大多数关于视频谣言检测的研究往往依赖于用户和视频模态信息的结合，却忽视了视频内部的多模态方面以及用户评论与视频局部片段之间的关系。针对这一问题，我们提出了一种名为时间同步评论增强多模态深度特征融合模型（TSC-MDFFM）的方法。它引入时间同步评论来增强视频在社交网络上的传播结构，补充视频中缺失的上下文信息或附加信息。时间同步评论侧重于表达用户对视频中特定时间点的看法，有助于从高密度信息的视频中获取更有价值的片段。视频中从一个关键帧到下一个关键帧的时间间隔被定义为局部片段。我们利用时间同步注释、视频关键帧和视频字幕文本对这一片段进行全面描述。局部片段序列根据视频时间轴和指定的时间信息进行排序，然后融合以创建视频的局部特征表示。随后，我们将文本特征、视频运动特征和视频评论的视觉特征在特征级别上进行融合，以表示视频的全局特征。这一特征不仅能捕捉视频内容的整体传播趋势，还能深入理解视频的整体特征。最后，我们将整合本地和全局特征进行视频谣言分类，将视频的本地信息和全局信息结合起来。我们创建了一个名为 TSC-VRD 的数据集，其中包括时间同步评论，涵盖了视频中所有可见信息。广泛的实验结果表明，在 TSC-VRD 数据集上，与现有方法相比，我们提出的模型具有更优越的性能。

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引用次数: 0

Study of technology communities and dominant technology lock-in in the Internet of Things domain - Based on social network analysis of patent network 物联网领域的技术社群和主导技术锁定研究--基于专利网络的社会网络分析

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-11-14 DOI: 10.1016/j.ipm.2024.103959

Xueting Yang , Bing Sun , Shilong Liu

The evolution of technology communities and the lock-in process of dominant technologies influence the advancement of the Internet of Things (IoT) in achieving its goal of connecting everything. This study aims to identify and analyze IoT technology communities and main technology trajectories, and to trace and explore the lock-in and unlocking process of IoT dominant technologies. A directed citation network was constructed using 9,464 IoT patent families as nodes and 23,604 inter-patent citation relationships as directed links. We used the Louvain algorithm and Latent Dirichlet Allocation (LDA) modeling technique to divide the communities and extract their themes, and the SPLC algorithm and key-route global main path search method to identify the dominant technology trajectories. The results show that first, technologies that emerged during the embryonic stage of IoT exhibit a declining trend as the standardization process of IoT progresses; technologies introduced during IoT's growing stage continue to increase, benefiting from the positive cyclical effect of application and integrated innovation. Second, major developments in IoT involve device risk assessment, machine learning, and machine-to-machine technologies. Third, the lock-in of IoT dominant technologies is accompanied by a 'learning by using' effect and an incremental succession of innovations. The novelty of this study lies in the combination of both community analysis and main path analysis methods, which help researchers and participators grasp the IoT technology development holistically from both horizontal - technology categorization and vertical - time perspectives. Meanwhile, we also analyzed the lock-in and unlocking process of IoT dominant technologies to provide a reference for participators to develop technological strategies.

技术社群的演变和主导技术的锁定过程影响着物联网（IoT）在实现连接万物目标方面的进展。本研究旨在识别和分析物联网技术群落和主要技术轨迹，并追踪和探索物联网主导技术的锁定和解锁过程。我们以9,464个物联网专利族为节点，以23,604个专利间的引用关系为有向链接，构建了一个有向引用网络。我们使用卢万算法和潜在德里希特分配（LDA）建模技术划分群落并提取其主题，使用SPLC算法和关键路径全局主路径搜索方法识别主导技术轨迹。研究结果表明：首先，随着物联网标准化进程的推进，物联网萌芽阶段出现的技术呈现出下降趋势；而物联网成长阶段引入的技术受益于应用和集成创新的正向循环效应，持续上升。其次，物联网的主要发展涉及设备风险评估、机器学习和机器对机器技术。第三，物联网主导技术的锁定伴随着 "边用边学 "效应和创新的渐进式继承。本研究的新颖之处在于结合了社区分析和主路径分析两种方法，有助于研究者和参与者从横向的技术分类和纵向的时间角度全面把握物联网技术的发展。同时，我们还分析了物联网主导技术的锁定和解锁过程，为参与者制定技术战略提供参考。

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引用次数: 0

Metaverse-based distance learning as a transactional distance mitigator and memory retrieval stimulant 基于元数据的远程学习是一种交易距离缓解剂和记忆检索刺激剂

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-11-11 DOI: 10.1016/j.ipm.2024.103957

Cheong Kim , Francis Joseph Costello , Jungwoo Lee , Kun Chang Lee

This present study explores Metaverse-based Distance Learning (MDL) as a mitigative strategy of transactional distance (TD) and an enhancer of memory retrieval in an educational setting. We conducted two experimental studies. In the first study (n = 367 participants), we found that MDL significantly reduced perceived TD, leading to positive learner attitudes and increased intentions for repeat learning. The second study utilized functional Near-Infrared Spectroscopy (fNIRS) to assess hemodynamic responses in the prefrontal cortex of 30 participants, comparing brain activity during lectures in MDL and e-learning environments. Results indicated that MDL elicited higher oxy-Hb activation in the prefrontal cortex, particularly during cognitively challenging tasks, correlating with improved memory retrieval. Grounded in both Transactional Distance Theory (TDT) and context-dependent memory (CDM) frameworks, we found that the technological and educational potential of MDL not only reduces psychological barriers in distance learning but also shows how it can improve cognitive engagement and retention. These findings underscore the potential of MDL in distance education and suggest pathways for future research to explore its implications further, particularly in conjunction with other emerging technologies.

本研究探讨了基于元数据的远程学习（MDL）在教育环境中作为事务性距离（TD）的缓解策略和记忆检索增强器的作用。我们进行了两项实验研究。在第一项研究中（n = 367 名参与者），我们发现 MDL 显著降低了感知 TD，从而使学习者态度积极，并增加了重复学习的意愿。第二项研究利用功能性近红外光谱（fNIRS）评估了 30 名参与者前额叶皮层的血流动力学反应，比较了在 MDL 和电子学习环境中授课时的大脑活动。结果表明，MDL 在前额叶皮层引起了更高的氧-血活化，尤其是在具有认知挑战性的任务中，这与记忆检索的改善相关。在交易距离理论（TDT）和情境依赖记忆（CDM）框架的基础上，我们发现 MDL 的技术和教育潜力不仅能减少远程学习中的心理障碍，还能显示它是如何提高认知参与度和记忆保持率的。这些发现强调了 MDL 在远程教育中的潜力，并为今后的研究提出了进一步探索其影响的途径，特别是与其他新兴技术相结合。

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引用次数: 0

Detecting and regulating sentiment reversal and polarization in online communities 检测和调节网络社区中的情绪逆转和两极分化

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-11-11 DOI: 10.1016/j.ipm.2024.103965

Yuqi Tao , Bin Hu , Zilin Zeng , Xiaomeng Ma

Sentiment reversals and polarizations can disrupt the harmony within a legitimate and peaceful online communication environment. To fill the research gaps, this paper introduces detection methods grounded in catastrophe theory and proposes two innovative regulatory strategies: reversal control strategy (RCS) and polarization control strategy (PCS). Experiments and empirical analysis are conducted on a self-built dataset encompassing approximately 50,000 user groups from Baidu Tieba. In the detection phase, the stochastic catastrophe model achieves an

R^{2}

of 0.57, a reversal index of 0.18 and a polarization index of 0.29, indicating the existence of sentiment reversal and polarization. In the regulation phase, RCS outperforms control groups by up to 53% and PCS outperforms control groups by up to 63%. Our empirical analysis reveals two insights. Firstly, an excessive regulation intensity does not proportionally increase benefits but instead diminishes the effectiveness of regulation. Secondly, strategies aim to preventing sentiment reversals can lead to sentiment polarizations and vice versa. This study holds theoretical and practical significance for the decision-making of online communities’ regulation, and also contributes to the management application of catastrophe theory.

情绪逆转和极化会破坏合法、和平的网络交流环境的和谐。为了填补研究空白，本文介绍了基于灾难理论的检测方法，并提出了两种创新的监管策略：逆转控制策略（RCS）和极化控制策略（PCS）。本文在百度铁算盘资料管家婆自建的约 50,000 个用户组数据集上进行了实验和实证分析。在检测阶段，随机灾难模型的 R2 为 0.57，反转指数为 0.18，极化指数为 0.29，表明存在情绪反转和极化现象。在监管阶段，RCS 的表现优于对照组达 53%，PCS 的表现优于对照组达 63%。我们的实证分析揭示了两点。首先，过高的监管强度不会按比例增加收益，反而会降低监管的有效性。其次，旨在防止情绪逆转的策略会导致情绪极化，反之亦然。本研究对网络社区的监管决策具有理论和实践意义，同时也有助于灾难理论的管理应用。

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引用次数: 0

Spatial network disintegration based on ranking aggregation 基于排序聚合的空间网络分解

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-11-09 DOI: 10.1016/j.ipm.2024.103955

Zhigang Wang , Ye Deng , Yu Dong , Jürgen Kurths , Jun Wu

Disintegrating harmful networks presents a significant challenge, especially in spatial networks where both topological and geospatial features must be considered. Existing methods that rely on a single metric often fail to capture the full complexity of such networks. To address these limitations, we propose a novel ranking aggregation-based algorithm for spatial network disintegration. Our approach integrates multiple region centrality metrics, providing a comprehensive evaluation of region importance. The algorithm operates in two stages: first, multiple rankings based on different centrality metrics are aggregated into a composite ranking to refine the candidate regions for disintegration. In the second stage, an exact target enumeration method is applied within this candidate set to determine the optimal combination of regions that maximizes disintegration impact. This interconnected approach effectively combines ranking aggregation with targeted enumeration to ensure both efficiency and accuracy. Extensive experiments are conducted on synthetic and real-world spatial networks of different network configurations. The results demonstrate that our method consistently achieves superior disintegration performance compared to traditional approaches, effectively addressing the challenges associated with spatial network disintegration. This study provides a contribution to understanding and improving spatial network disintegration strategies by leveraging a comprehensive, multi-criteria approach.

分解有害网络是一项重大挑战，尤其是在必须考虑拓扑和地理空间特征的空间网络中。依赖单一指标的现有方法往往无法捕捉此类网络的全部复杂性。为了解决这些局限性，我们提出了一种基于排序聚合的新型空间网络分解算法。我们的方法整合了多个区域中心度量，提供了对区域重要性的综合评估。该算法分两个阶段运行：第一阶段，将基于不同中心度量的多个排名聚合成一个综合排名，以完善解体的候选区域。在第二阶段，在这个候选集中应用精确目标枚举法，以确定区域的最佳组合，从而使解体影响最大化。这种相互关联的方法有效地将排序聚合与目标枚举相结合，确保了效率和准确性。我们在不同网络配置的合成网络和真实世界的空间网络上进行了广泛的实验。实验结果表明，与传统方法相比，我们的方法始终能实现卓越的分解性能，有效地解决了空间网络分解所面临的挑战。这项研究通过利用综合、多标准的方法，为理解和改进空间网络分解策略做出了贡献。

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引用次数: 0

Embracing the power of ensemble forecasting: A novel hybrid approach for advanced predictive modeling 利用集合预测的力量：先进预测模型的新型混合方法

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management

Pub Date : 2024-11-07 DOI: 10.1016/j.ipm.2024.103954

Isha Malhotra, Nidhi Goel

Amidst the persistent threat of epidemics, effectively managing their complexities requires accurate forecasting to anticipate their trajectory, thus enabling the preparation and implementation of effective mitigation strategies. With a special emphasis on COVID-19, the present work focuses on the Omicron variant, recognizing its significance in the global context of infectious diseases. The proposed research evaluates the effectiveness of both univariate and multivariate frameworks utilizing statistical and deep learning approaches to forecast the spread of the epidemic. Forecasting robustness is boosted by effectively correlating linear and non-linear components with the original series. To improve the performance, correlation is facilitated using correlation-driven weights within the statistically enforced deep learning model (WD-ensemble framework). The modeling process utilizes 493 data points and multivariate time-series records, including infected cases, vaccinated cases, and stringency index. The training dataset spans from November 1, 2021, to January 17, 2023, while the testing dataset covers the period from January 18, 2023, to March 8, 2023. The proposed WD-ensemble framework, incorporating stochasticity, outperforms all other state-of-the-art models, yielding highly reliable forecasts with remarkably low RMSE of 907.54, MAPE of 0.0008, and MAE of 670.78. It demonstrates a reduction in error percentages compared to the top-performing existing model, with decreases of 30.0267% in RMSE, 20% in MAPE, and 24.9411% in MAE. A pivotal revelation in this research is the robust negative correlation (-0.86) between vaccinated and confirmed cases as compared to the stringency index, implying that widespread vaccination could warrant the relaxation of stringent measures, including business and school closures.

在流行病的持续威胁下，要有效管理其复杂性，就必须进行准确的预测，预知其发展轨迹，从而制定和实施有效的缓解战略。考虑到 COVID-19 在全球传染病中的重要性，本研究将重点放在 Omicron 变异上。拟议的研究评估了利用统计和深度学习方法预测流行病传播的单变量和多变量框架的有效性。通过有效地将线性和非线性成分与原始序列相关联，提高了预测的稳健性。为了提高性能，在统计强化深度学习模型（WD-ensemble 框架）中使用相关性驱动权重来促进相关性。建模过程利用了 493 个数据点和多元时间序列记录，包括感染病例、接种病例和严格指数。训练数据集的时间跨度为 2021 年 11 月 1 日至 2023 年 1 月 17 日，测试数据集的时间跨度为 2023 年 1 月 18 日至 2023 年 3 月 8 日。所提出的 WD-ensemble 框架结合了随机性，其预测结果优于所有其他最先进的模型，具有极高的可靠性，RMSE 为 907.54，MAPE 为 0.0008，MAE 为 670.78。与表现最好的现有模型相比，它的误差百分比有所降低，RMSE 降低了 30.0267%，MAPE 降低了 20%，MAE 降低了 24.9411%。这项研究的一个重要启示是，与严格指数相比，接种疫苗的病例与确诊病例之间存在稳健的负相关（-0.86），这意味着广泛的疫苗接种可能需要放宽严格措施，包括关闭企业和学校。

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引用次数: 0