INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS国际智能系统杂志
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS(英文缩写 INT J INTELL SYST),ISSN 0884-8173,eISSN 1098-111X,中文译名:国际智能系统杂志 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：2025-09-27 至 2026-09-27，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 | 8.993 | Q1 |
| 2022 | 7.000 | Q1 |
| 2023 | 5.000 | Q1 |
| 2024 | 3.700 | Q2 |
| 2025 | 4.800 | Q2 |
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 最新收录文献
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1. A hybrid adaptive approach for instance transfer learning with dynamic and imbalanced data.
PMID:日期:2022-12-01Machine learning has demonstrated success in clinical risk prediction modeling with complex electronic health record data. However, the evolving nature of clinical practices can dynamically change the underlying data distribution over time, leading to model performance drift. Adopting an outdated model is potentially risky and may result in unintentional losses. In this paper, we propose a novel ybrid daptive oosting approach (HA-Boost) for transfer learning. HA-Boost is characterized by the domain similarity-based and class imbalance-based adaptation mechanisms, which simultaneously address two critical limitations of the classical TrAdaBoost algorithm. We validated HA-Boost in predicting hospital-acquired acute kidney injury using real-world longitudinal electronic health records data. The experiment results demonstrate that HA-Boost stably outperforms the competing baselines in terms of both AUROC and AUPRC across a 7-year time span. This study has confirmed the effectiveness of transfer learning as a superior model updating approach in dynamic environment.
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2. An intelligent forecast for COVID-19 based on single and multiple features.
PMID:日期:2022-11-01It is urgent to identify the development of the Corona Virus Disease 2019 (COVID-19) in countries around the world. Therefore, visualization is particularly important for monitoring the COVID-19. In this paper, we visually analyze the real-time data of COVID-19, to monitor the trend of COVID-19 in the form of charts. At present, the COVID-19 is still spreading. However, in the existing works, the visualization of COVID-19 data has not established a certain connection between the forecast of the epidemic data and the forecast of the epidemic. To better predict the development trend of the COVID-19, we establish a logistic growth model to predict the development of the epidemic by using the same data source in the visualization. However, the logistic growth model only has a single feature. To predict the epidemic situation in an all-round way, we also predict the development trend of the COVID-19 based on the Susceptible Exposed Infected Removed epidemic model with multiple features. We fit the data predicted by the model to the real COVID-19 epidemic data. The simulation results show that the predicted epidemic development trend is consistent with the actual epidemic development trend, and our model performs well in predicting the trend of COVID-19.
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3. Performance of exponential similarity measures in supply of commodities in containment zones during COVID-19 pandemic under Pythagorean fuzzy sets.
PMID:日期:2022-09-02Following the breakout of the novel coronavirus disease 2019 (COVID-19), the government of India was forced to prohibit all forms of human movement. It became important to establish and maintain a supply of commodities in hotspots and containment zones in different parts of the country. This study critically proposes new exponential similarity measures to understand the requirement and distribution of commodities to these zones during the rapid spread of novel coronavirus (COVID-19) across the globe. The primary goal is to utilize the important aspect of similarity measures based on exponential function under Pythagorean fuzzy sets, proposed by Yager. The article aims at finding the most required commodity in the affected areas and ensures its distribution in hotspots and containment zones. The projected path of grocery delivery to different residences in containment zones is determined by estimating the similarity measure between each residence and the various necessary goods. Numerical computations have been carried out to validate our proposed measures. Moreover, a comparison of the result for the proposed measures has been carried out to prove the efficacy.
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4. {"_":"Intuitionistic fuzzy set of -submodules and its application in modeling spread of viral diseases, mutated COVID-, via flights.","mml:math":[{"xmlns:mml":["http://www.w3.org/1998/Math/MathML"],"mml:mi":["Γ"]}],"i":["n"]}
PMID:日期:2022-08-01In this study, we generalize fuzzy -module, as intuitionistic fuzzy -submodule of -module (IF M), and utilize it for modeling the spread of coronavirus in air travels. Certain fundamental features of intuitionistic fuzzy -submodule are provided, and it is proved that IF M can be considered as a complete lattice. Some elucidatory examples are demonstrated to explain the properties of IF M. The relevance between the upper and lower -level cut and intuitionistic fuzzy -submodules are presented and the characteristics of upper and lower under image and inverse image of IF M are acquired. It is verified that the image and inverse image of intuitionistic fuzzy -submodule are preserved under the module homomorphism. The obtained IF M is used to model the aerial transition of viral diseases, that is, COVID-, via flights.
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5. Rise of multiattribute decision-making in combating COVID-19: A systematic review of the state-of-the-art literature.
PMID:日期:2022-06-01Considering the coronavirus disease 2019 (COVID-19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision-making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID-19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID-19 by presenting a systematic literature review of the state-of-the-art COVID-19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical ( = 30), social ( = 4), economic ( = 13) and technological ( = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID-19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.
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6. Using self-organising maps to predict and contain natural disasters and pandemics.
PMID:日期:2022-04-01The unfolding coronavirus (COVID-19) pandemic has highlighted the global need for robust predictive and containment tools and strategies. COVID-19 continues to cause widespread economic and social turmoil, and while the current focus is on both minimising the spread of the disease and deploying a range of vaccines to save lives, attention will soon turn to future proofing. In line with this, this paper proposes a prediction and containment model that could be used for pandemics and natural disasters. It combines selective lockdowns and protective cordons to rapidly contain the hazard while allowing minimally impacted local communities to conduct "business as usual" and/or offer support to highly impacted areas. A flexible, easy to use data analytics model, based on Self Organising Maps, is developed to facilitate easy decision making by governments and organisations. Comparative tests using publicly available data for Great Britain (GB) show that through the use of the proposed prediction and containment strategy, it is possible to reduce the peak infection rate, while keeping several regions (up to 25% of GB parliamentary constituencies) economically active within protective cordons.
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7. A comprehensive review of federated learning for COVID-19 detection.
PMID:日期:2022-03-01The coronavirus of 2019 (COVID-19) was declared a global pandemic by World Health Organization in March 2020. Effective testing is crucial to slow the spread of the pandemic. Artificial intelligence and machine learning techniques can help COVID-19 detection using various clinical symptom data. While deep learning (DL) approach requiring centralized data is susceptible to a high risk of data privacy breaches, federated learning (FL) approach resting on decentralized data can preserve data privacy, a critical factor in the health domain. This paper reviews recent advances in applying DL and FL techniques for COVID-19 detection with a focus on the latter. A model FL implementation use case in health systems with a COVID-19 detection using chest X-ray image data sets is studied. We have also reviewed applications of previously published FL experiments for COVID-19 research to demonstrate the applicability of FL in tackling health research issues. Last, several challenges in FL implementation in the healthcare domain are discussed in terms of potential future work.
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8. NAGNN: Classification of COVID-19 based on neighboring aware representation from deep graph neural network.
PMID:日期:2022-02-01COVID-19 pneumonia started in December 2019 and caused large casualties and huge economic losses. In this study, we intended to develop a computer-aided diagnosis system based on artificial intelligence to automatically identify the COVID-19 in chest computed tomography images. We utilized transfer learning to obtain the image-level representation (ILR) based on the backbone deep convolutional neural network. Then, a novel neighboring aware representation (NAR) was proposed to exploit the neighboring relationships between the ILR vectors. To obtain the neighboring information in the feature space of the ILRs, an ILR graph was generated based on the -nearest neighbors algorithm, in which the ILRs were linked with their -nearest neighboring ILRs. Afterward, the NARs were computed by the fusion of the ILRs and the graph. On the basis of this representation, a novel end-to-end COVID-19 classification architecture called neighboring aware graph neural network (NAGNN) was proposed. The private and public data sets were used for evaluation in the experiments. Results revealed that our NAGNN outperformed all the 10 state-of-the-art methods in terms of generalization ability. Therefore, the proposed NAGNN is effective in detecting COVID-19, which can be used in clinical diagnosis.
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9. Generalized two-tailed hypothesis testing for quantiles applied to the psychosocial status during the COVID-19 pandemic.
PMID:日期:2021-12-01Nonparametric tests do not rely on data belonging to any particular parametric family of probability distributions, which makes them preferable in case of doubt about the underlying population. Although the two-tailed sign test is likely the most common nonparametric test for location problems, practitioners face serious drawbacks, such as its lack of statistical power and its inapplicability when information regarding data and hypotheses is uncertain or imprecise. In this paper, we generalize the two-tailed sign test by embedding fuzzy hypotheses caused by uncertainty/imprecision regarding linguistic statements on fractions of underlying quantiles. By achieving this objective, (1) crucial limitations of the common two-tailed sign test are mitigated/overcome, (2) various further strengths are incorporated into the sign test (e.g., meeting the trade-off between point- and interval-valued hypotheses, facilitated formulation of fuzzy hypotheses, standardization of membership functions), and (3) shortcomings that often come along with fuzzy hypothesis testing are avoided (e.g., higher complexity, fuzzy test decision, possibilistic interpretation of test results). In addition, we conduct a comprehensive case study using a real data set on the psychosocial status during the COVID-19 pandemic. The results of the case study clearly indicate that the generalized two-tailed sign test is preferable to the two-tailed sign test with point- or interval-valued hypotheses.
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10. Machine learning for medical imaging-based COVID-19 detection and diagnosis.
PMID:日期:2021-09-01The novel coronavirus disease 2019 (COVID-19) is considered to be a significant health challenge worldwide because of its rapid human-to-human transmission, leading to a rise in the number of infected people and deaths. The detection of COVID-19 at the earliest stage is therefore of paramount importance for controlling the pandemic spread and reducing the mortality rate. The real-time reverse transcription-polymerase chain reaction, the primary method of diagnosis for coronavirus infection, has a relatively high false negative rate while detecting early stage disease. Meanwhile, the manifestations of COVID-19, as seen through medical imaging methods such as computed tomography (CT), radiograph (X-ray), and ultrasound imaging, show individual characteristics that differ from those of healthy cases or other types of pneumonia. Machine learning (ML) applications for COVID-19 diagnosis, detection, and the assessment of disease severity based on medical imaging have gained considerable attention. Herein, we review the recent progress of ML in COVID-19 detection with a particular focus on ML models using CT and X-ray images published in high-ranking journals, including a discussion of the predominant features of medical imaging in patients with COVID-19. Deep Learning algorithms, particularly convolutional neural networks, have been utilized widely for image segmentation and classification to identify patients with COVID-19 and many ML modules have achieved remarkable predictive results using datasets with limited sample sizes.