INFORMATION SCIENCES信息科学
INFORMATION SCIENCES(英文缩写 INFORM SCIENCES),ISSN 0020-0255,eISSN 1872-6291,中文译名:信息科学 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 8.233 | Q1 |
| 2022 | 8.100 | Q1 |
| 2023 | 未收录 | N/A |
| 2024 | 6.800 | Q1 |
| 2025 | 6.000 | Q1 |
INFORMATION SCIENCES 最新收录文献
-
1. Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations.
PMID:日期:2026-02-10An unprecedented amount of existing functional Magnetic Resonance Imaging (fMRI) data provides a new opportunity to understand how functional fluctuations relate to human cognition/behavior using data-driven approaches. To this end, tremendous efforts have been made in machine learning to decode cognitive states from evolving volumetric images of blood-oxygen-level-dependent (BOLD) signals. However, due to the complex nature of brain function, the performance and findings of current deep learning models remain inconsistent across tasks, datasets, and evaluation settings. In this work, by capitalizing on large-scale existing neuroimaging data (39,784 fMRI samples from seven databases), we seek to establish a well-founded empirical guideline for designing deep models in functional neuroimaging by linking the methodology underpinning with neuroscientific understanding. Specifically, we put the spotlight on (1) What is the performance landscape of various models in cognitive task recognition and disease diagnosis? (2) What are the key limitations and trade-offs of current deep models? and (3) What is the general guideline for selecting the suitable machine learning backbone for specific neuroimaging applications? We have conducted comprehensive evaluations and statistical analysis across cognitive and clinical scenarios, to answer the above outstanding questions. Our findings demonstrate that no universal model dominates all scenarios; instead, model effectiveness depends on factors such as demographics, task type, and disease stage. Furthermore, we introduce an attention-based interpretability method to reveal spatial patterns of brain activation associated with tasks and disorders.
-
2. A multimodal machine learning approach to predict Fugl-Meyer scores and motor recovery potential in stroke rehabilitation: Toward precision-based therapies.
PMID:日期:2025-08-12Stroke is a leading cause of long-term disability, with highly variable recovery trajectories and challenges in prediction and monitoring. Frequently used measures (e.g., National Institute of Health Stroke Scale (NIHSS) and Fugl-Meyer (FM) assessment of motor impairment) have significant limitations. As the societal burden of stroke increases, developing robust methodologies for assessing and predicting recovery is essential to optimize treatment plans and improve outcomes. This paper presents our Integrated Motion Analysis Suite (IMAS), which leverages multimodal data (clinical, sensor, and neuroimaging inputs) and multimodal machine learning (MML) to predict FM scores and motor recovery in stroke. Its potential is demonstrated via analysis of 28 stroke patients in acute and subacute phases of recovery, where features extracted from a set of motor tasks were used to predict FM scores and motor recovery, achieving a coefficient of determination (R) of 0.75 and Mean Absolute Error (MAE) of 2.83 and R = 0.83 and MAE = 2.6 %, respectively. IMAS is designed to continuously improve through its integration with a Big Data database, allowing for ongoing refinement of predictive algorithms as new data is collected in real-world clinical environments. Its ability to complement inherently limited clinical scales, handle incomplete data, and adapt to diverse applications highlights its potential for broader use in recovery after stroke, including long-term monitoring and precision rehabilitation.
-
3. Causality-aware Social Recommender System with Network Homophily Informed Multi-treatment Confounders.
3. 基于因果感知与网络同质性多处理混杂因素的社交推荐系统PMID:日期:2024-05-15Typical recommender systems utilize observed ratings of users as inputs to learn their preferences and aim to output recommendations of new items that users will like by predicting their potential ratings. The real world is driven by causality given that whether or not a user is made exposed to the item may significantly affect the rating. While some recent methods have taken a causal view to mitigate the confounding bias in observed rating data, few have recognized recommendation as a multiple causal inference problem that concerns numerous items simultaneously in practice. By framing the recommendation as a multiple causal inference problem, this paper develops a causality-aware social recommender that incorporates underlying social network structures with matrix factorization methods for deconfounding in networked observational data to enhance social recommendations. Leveraging social network information, which inherently confounds with the preferences of users, is particularly crucial but not trivial for inferring unobserved confounders in social recommender systems. Considering that connected users in a social network share similar attributes, we propose to incorporate the network homophily into the matrix factorization models through regularization to better learn the latent variables. The extracted latent variables then capture the network informed multi-treatment confounders to mitigate the confounding bias and improve the rating prediction accuracy. The models are estimated through a proximal gradient-based optimization framework, which not only eases the incorporation of the network structure constraints but also improves the computational efficiency of the proposed method. Simulation and case studies are conducted to validate the proposed method.
-
4. An optimal Bayesian intervention policy in response to unknown dynamic cell stimuli.
4. 应对未知动态细胞刺激的最优贝叶斯干预策略PMID:日期:2024-03-07Interventions in gene regulatory networks (GRNs) aim to restore normal functions of cells experiencing abnormal behavior, such as uncontrolled cell proliferation. The dynamic, uncertain, and complex nature of cellular processes poses significant challenges in determining the best interventions. Most existing intervention methods assume that cells are unresponsive to therapies, resulting in stationary and deterministic intervention solutions. However, cells in unhealthy conditions can dynamically respond to therapies through internal stimuli, leading to the recurrence of undesirable conditions. This paper proposes a Bayesian intervention policy that adaptively responds to cell dynamic responses according to the latest available information. The GRNs are modeled using a Boolean network with perturbation (BNp), and the fight between the cell and intervention is modeled as a two-player zero-sum game. Assuming an incomplete knowledge of cell stimuli, a recursive approach is developed to keep track of the posterior distribution of cell responses. The proposed Bayesian intervention policy takes action according to the posterior distribution and a set of Nash equilibrium policies associated with all possible cell responses. Analytical results demonstrate the superiority of the proposed intervention policy against several existing intervention techniques. Meanwhile, the performance of the proposed policy is investigated through comprehensive numerical experiments using the p53-MDM2 negative feedback loop regulatory network and melanoma network. The results demonstrate the empirical convergence of the proposed policy to the optimal Nash equilibrium policy.
-
5. HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control.
5. HRL4EC：用于多模式疫情控制的层次强化学习PMID:日期:2023-05-09Infectious diseases, such as Black Death, Spanish Flu, and COVID-19, have accompanied human history and threatened public health, resulting in enormous infections and even deaths among citizens. Because of their rapid development and huge impact, laying out interventions becomes one of the most critical paths for policymakers to respond to the epidemic. However, the existing studies mainly focus on epidemic control with a single intervention, which makes the epidemic control effectiveness severely compromised. In view of this, we propose a ierarchical einforcement earning decision framework for multi-mode pidemic ontrol with multiple interventions called . We devise an epidemiological model, referred to as , to describe multiple interventions' impact on transmission explicitly, and use it as the environment for HRL4EC. Besides, to address the complexity introduced by multiple interventions, this work transforms the multi-mode intervention decision problem into a multi-level control problem, and employs hierarchical reinforcement learning to find the optimal strategies. Finally, extensive experiments are conducted with real and simulated epidemic data to validate the effectiveness of our proposed method. We further analyze the experiment data in-depth, conclude a series of findings on epidemic intervention strategies, and make a visualization accordingly, which can provide heuristic support for policymakers' pandemic response.
-
6. Modeling the spread dynamics of multiple-variant coronavirus disease under public health interventions: A general framework.
6. 公共卫生干预下多变异冠状病毒疾病传播动力学建模：一个通用框架PMID:日期:2023-02-06The COVID-19 pandemic was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is a single-stranded positive-stranded RNA virus with a high multi-directional mutation rate. Many new variants even have an immune-evading property, which means that some individuals with antibodies against one variant can be reinfected by other variants. As a result, the realistic is still suffering from new waves of COVID-19 by its new variants. How to control the transmission or even eradicate the COVID-19 pandemic remains a critical issue for the whole world. This work presents an epidemiological framework for mimicking the multi-directional mutation process of SARS-CoV-2 and the epidemic spread of COVID-19 under realistic scenarios considering multiple variants. The proposed framework is used to evaluate single and combined public health interventions, which include non-pharmaceutical interventions, pharmaceutical interventions, and vaccine interventions under the existence of multi-directional mutations of SARS-CoV-2. The results suggest that several combined intervention strategies give optimal results and are feasible, requiring only moderate levels of individual interventions. Furthermore, the results indicate that even if the mutation rate of SARS-CoV-2 decreased 100 times, the pandemic would still not be eradicated without appropriate public health interventions.
-
7. MIC-Net: A deep network for cross-site segmentation of COVID-19 infection in the fog-assisted IoMT.
PMID:日期:2022-12-13The automatic segmentation of COVID-19 pneumonia from a computerized tomography (CT) scan has become a major interest for scholars in developing a powerful diagnostic framework in the Internet of Medical Things (IoMT). Federated deep learning (FDL) is considered a promising approach for efficient and cooperative training from multi-institutional image data. However, the nonindependent and identically distributed (Non-IID) data from health care remain a remarkable challenge, limiting the applicability of FDL in the real world. The variability in features incurred by different scanning protocols, scanners, or acquisition parameters produces the learning drift phenomena during the training, which impairs both the training speed and segmentation performance of the model. This paper proposes a novel FDL approach for reliable and efficient multi-institutional COVID-19 segmentation, called MIC-Net. MIC-Net consists of three main building modules: the down-sampler, context enrichment (CE) module, and up-sampler. The down-sampler was designed to effectively learn both local and global representations from input CT scans by combining the advantages of lightweight convolutional and attention modules. The contextual enrichment (CE) module is introduced to enable the network to capture the contextual representation that can be later exploited to enrich the semantic knowledge of the up-sampler through skip connections. To further tackle the inter-site heterogeneity within the model, the approach uses an adaptive and switchable normalization (ASN) to adaptively choose the best normalization strategy according to the underlying data. A novel federated periodic selection protocol (FED-PCS) is proposed to fairly select the training participants according to their resource state, data quality, and loss of a local model. The results of an experimental evaluation of MIC-Net on three publicly available data sets show its robust performance, with an average dice score of 88.90% and an average surface dice of 87.53%.
-
8. ECG-COVID: An end-to-end deep model based on electrocardiogram for COVID-19 detection.
8. ECG-COVID：一种基于心电图的端到端深度学习模型用于COVID-19检测PMID:日期:2022-11-18The early and accurate detection of COVID-19 is vital nowadays to avoid the vast and rapid spread of this virus and ease lockdown restrictions. As a result, researchers developed methods to diagnose COVID-19. However, these methods have several limitations. Therefore, presenting new methods is essential to improve the diagnosis of COVID-19. Recently, investigation of the electrocardiogram (ECG) signals becoming an easy way to detect COVID-19 since the ECG process is non-invasive and easy to use. Therefore, we proposed in this paper a novel end-to-end deep learning model (ECG-COVID) based on ECG for COVID-19 detection. We employed several deep Convolutional Neural Networks (CNNs) on a dataset of 1109 ECG images, which is built for screening the perception of COVID-19 and cardiac patients. After that, we selected the most efficient model as our model for evaluation. The proposed model is end-to-end where the input ECG images are fed directly to the model for the final decision without using any additional stages. The proposed method achieved an average accuracy of 98.81%, Precision of 98.8%, Sensitivity of 98.8% and, F1-score of 98.81% for COVID-19 detection. As cases of corona continue to rise and hospitalizations continue again, hospitals may find our study helpful when dealing with these patients who did not get significantly worse.
-
9. An integrated interval-valued intuitionistic fuzzy technique for resumption risk assessment amid COVID-19 prevention.
9. 一种用于COVID-19防控期间复工风险评估的集成区间直觉模糊技术PMID:日期:2022-11-15Currently, China has achieved a remarkable achievement on the containment of COVID-19, which creates a favorable condition for the gradual resumption of normal life. However, COVID-19 infections continue to rise in many nations and some sporadic cases occur from time to time in China, which still poses some risks to the resumption. Hence, it is imperative to develop some reasonable techniques to assess the resumption risk. This paper aims to investigate an integrated interval-valued intuitionistic fuzzy (IVIF) technique to adroitly assess the resumption risk based on DEMATEL (decision making trial and evaluation laboratory), BWM (best-worst method) and SPA (set pair analysis). This integrated technique is called IVIF-DBWM-SPA, where the IVIF-DBWM (combined by the IVIF-DEMATEL and IVIF-BWM) is used to determine the global criteria weights and the IVIF-SPA is employed to generate the ranking order of the alternatives. The IVIF-DEMATEL and IVIF-BWM are used to determine the weights of dimensions and the weights of criteria under each dimension, respectively. In this IVIF-BWM, two bi-objective programming models are constructed by regarding experts' pessimistic and optimistic attitudes, respectively. Combined experts' intrapersonal and interpersonal uncertainties simultaneously, a bi-objective programming model is proposed to derive the dynamic weights of experts. Based on the determined weights of experts and criteria, an IVIF-SPA is developed to assess the risk levels of all alternatives. The validity of the proposed technique is demonstrated with a real case of college resumption risk assessment amid COVID-19. Some sensitivity and comparison analyses are provided to show the merits of the proposed technique.
-
10. A centralized contact-tracing protocol for the COVID-19 pandemic.
10. 新冠肺炎大流行的集中式接触追踪协议PMID:日期:2022-10-27Digital contact tracing (DCT) is one of the weapons to be used against the COVID-19 pandemic, especially in a post-lockdown phase, to prevent or block foci of infection. As DCT systems can handle highly private information about people, great care must be taken to prevent misuse of the system and actions detrimental to people's privacy, up to mass surveillance. This paper presents a new centralized DCT protocol, called ZE2-P3T (Zero Ephemeral Exchanging Privacy-Preserving Proximity Protocol), which relies on smartphone localization but does not give any information about the user's location and identity to the server. Importantly, the fact that no exchange of ephemeral identities among users is required is the basis of the strong security of the protocol, which is proven to be more secure than the state-of-the-art protocol DP-3T/GAEN.