In response to the complex treatment process and evolving medical needs of multimorbidity , multidisciplinary team (MDT) is dedicated to integrating the diagnosis opinions of experts and providing optimal treatment plans. Reaching consensus on disease treatment plans involves a dynamic and iterative group decision-making process, in which traditional methods for MDT meetings fail to address the standardized decision-making procedure, interactive trust relationships, and fuzzy information integration. Given the challenges, this study proposes a dynamic consensus framework based on dual-path feedback mechanism with q -rung orthopair fuzzy set ( q -ROFS). A hybrid trust evolution model is first established within MDT, in which the trust degree is composed of inherent trust and preference similarity in each round. Then the opinion dynamics model is also introduced to the fuzzy environment. Based on trust evolution and opinion dynamics, the dual-path feedback mechanism is employed to provide references for preference adjustment and weight adjustment. Correspondingly, the calculation methods for consensus measure, preference similarity and alternative selection with q -ROFS are proposed. Additionally, a case study about vascular MDT meeting is used to illustrate the effectiveness of the proposed method. The simulation experiments are performed to verify the impact of consensus threshold, group size, individual self-confidence, and trust evolution on the proposed method. The results of the comparative analysis show that increasing the q value can expand the fuzzy information expression space while ensuring the consensus level, and the proposed method is superior to other methods in terms of more efficient and high-quality consensus results.

Semantic textual similarity (STS) is a fundamental task in the field of natural language processing (NLP). Recent advances demonstrate that deep-learning-based approaches can achieve excitingly accurate STS measurement. However, existing studies cannot capture the spatial location of important information by attention mechanisms, fail to model sentences from the perspective of overall sentences, and neglect to deal with semantic fuzziness. In this article, we propose a novel double attentive fuzzy convolutional neural network (DAFCNN) to measure STS more accurately with the consideration of semantic fuzziness. This article first introduces the spatial attention module and combines it with the improved attentive convolutions to create a multigranularity convolutional neural network in DAFCNN, which not only extracts critical spatial location information but also models sentences from multiple perspectives at word and sentence levels. Second, DAFCNN pioneers a fuzzy learning module (FLM) to fulfill the extraction of fuzzy semantic features. By using the fuzzy membership function, fuzzy aggregation operator, and trainable parameters and weights, FLM can map sentence representations to fuzzy space to constitute representations with more accurate and rich semantics. Third, compared with various state-of-the-art STS models, DAFCNN decreases by 14.57% mean-square error, increases by 4.61% Pearson's γ and 8.57% Spearman's ρ on STS score datasets, and increases by 3.39% accuracy and 2.41%F1-score on semantic classification dataset. The ablation experiment demonstrates the effectiveness of each module of DAFCNN. Finally, the experimental results also indicate that FLM is a promising new attempt to incorporate fuzzy set theory in the NLP field.

Few-shot segmentation (FSS) aims to segment the novel class with a few annotated images. Due to CLIP's advantages of aligning visual and textual information, the integration of CLIP can enhance the generalization ability of FSS model. However, even with the CLIP model, the existing CLIP-based FSS methods are still subject to the biased prediction towards base class, which is caused by the class-specific feature level interactions. To solve this issue, we propose a visual and textual Prior Guided Mask Assemble Network (PGMA-Net). It employs a class-agnostic mask assembly process to alleviate the bias, and formulates diverse tasks into a unified manner by assembling the prior through affinity. Specifically, the class-relevant textual and visual features are first transformed to class-agnostic prior in the form of probability map. Then, a Prior-Guided Mask Assemble Module (PGMAM) including multiple General Assemble Units (GAUs) is introduced. It considers diverse and plug-and-play interactions, such as visual-textual, inter- and intra-image, training-free, and high-order ones. Lastly, to ensure the class-agnostic ability, a Hierarchical Decoder with Channel-Drop Mechanism (HDCDM) is proposed to flexibly exploit the assembled masks and low-level features, without relying on any class-specific information. It achieves new state-of-the-art results in the FSS task, with mIoU of 77.6 on$\rm{PASCAL-}5^{i}$and 59.4 on$\rm{COCO-}20^{i}$in 1-shot scenario. Beyond this, we show that without extra re-training, the proposed PGMA-Net can solve bbox-level and cross-domain FSS, co-segmentation, zero-shot segmentation (ZSS) tasks, leading an any-shot segmentation framework capable of accommodating diverse weak or pixel annotations.

Conventional knowledge graphs (KGs) are composed solely of entities, attributes, and relationships, which poses challenges for enhancing multimodal knowledge representation and reasoning. To address the issue, this article proposes a multimodal deep learning-based approach to build a multimodal knowledge base (MMKB) for better multimodal feature (MMF) utilization. First, we construct a multimodal computation sequence (MCS) model for structured multimodal data storage. Then, we propose multimodal node, relationship, and dictionary models to enhance multimodal knowledge representation. Various feature extractors are used to extract MMFs from text, audio, image, and video data. Finally, we leverage generative adversarial networks (GANs) to facilitate MMF representation and update the MMKB dynamically. We examine the performance of the proposed method by using three multimodal datasets. BOW-, LBP-, Volume-, and VGGish-based feature extractors outperform the other methods by reducing at least 1.13%, 22.14%, 39.87, and 5.65% of the time cost, respectively. The average time costs of creating multimodal indexes improve by approximately 55.07% and 68.60% exact matching rates compared with the baseline method, respectively. The deep learning-based autoencoder method reduces the search time cost by 98.90% after using the trained model, outperforming the state-of-the-art methods. In terms of multimodal data representation, the GAN-CNN models achieve an average correct rate of 82.70%. Our open-source work highlights the importance of flexible MMF utilization in multimodal KGs, leading to more powerful and diverse applications that can leverage different types of data.

Diagnostic error refers to a missed, delayed, or wrong diagnosis, which seriously affects diagnostic reliability and safety. Identifying diagnostic errors is crucial for diagnostic error research. As an important method of identifying diagnostic errors, electronic triggers (e-triggers) have several limitations, including the performance improvement bottleneck, the inability to predict diagnostic errors, and the narrow trigger range caused by inadequate data utilization. This article proposes a novel approach for identifying diagnostic errors in electronic medical records (EMRs) based on machine learning (ML) techniques. First, we design four stages for our approach, i.e., EMR utilization, ML, evaluation, and prediction. By learning the implicit identification pattern of diagnostic errors from historical EMRs, the proposed approach can find potential diagnostic error cases and predict the probability of upcoming diagnostic errors. Second, in the evaluation stage, we introduce various metrics to evaluate its performance accurately and effectively. Metrics for measuring the abilities to identify diagnostic errors and resist false negatives are introduced. Third, the study utilizes real-world EMRs of leukemia patients to illustrate and verify the proposed approach. The experimental results indicate that our approach accurately identifies most diagnostic error cases and efficiently reduces the number of false negatives, which will help protect diagnostic reliability and safety in the clinic.

Large-scale vision-language models (LVLMs) pre-trained on massive image-text pairs have achieved remarkable success in visual representations. However, existing paradigms to transfer LVLMs to downstream tasks encounter two primary challenges. Firstly, the text features remain fixed after being calculated and cannot be adjusted according to image features, which decreases the model’s adaptability. Secondly, the model’s output solely depends on the similarity between the text and image features, leading to excessive reliance on LVLMs. To address these two challenges, we introduce a novel two-branch model named the Instance-Wise Adaptive Tuning and Caching (ATC). Specifically, one branch implements our proposed ConditionNet, which guides image features to form an adaptive textual cache that adjusts based on image features, achieving instance-wise inference and improving the model’s adaptability. The other branch introduces the similarities between images and incorporates a learnable visual cache, designed to decouple new and previous knowledge, allowing the model to acquire new knowledge while preserving prior knowledge. The model’s output is jointly determined by the two branches, thus overcoming the limitations of existing methods that rely solely on LVLMs. Additionally, our method requires limited computing resources to tune parameters, yet outperforms existing methods on 11 benchmark datasets.

Motivated by the semi-supervised learning that uses the unlabeled data and pseudo annotations to improve the image classification, this paper proposes a new semi-supervised few-shot segmentation (FSS) framework of which the training process uses not only the annotated images, but also the unlabeled images, e.g. images from other available datasets, to enhance the training of the FSS model. Furthermore, in the test phase, more support images and pseudo-annotations can also be generated by the proposed framework to enrich the support set of novel classes and therefore benefit the inference. However, unlabeled images are not a free lunch. The noisy intra-class samples and inter-class samples existed in the unlabeled images as well as the interferences of the bad quality of pseudo annotations make it difficult to utilize the correct images and pseudo annotations for a certain class. To this end, we further propose a ranking algorithm consisting of an inter-class confidence term and an intra-class confidence term to efficiently utilize the pseudo annotations of the class with high quality. Extensive experiments on COCO-20idataset demonstrate that the proposed semi-supervised FSS framework is superior to many state-of-the-art methods.

As a proactive reliability analysis technique, the purpose of failure mode and effect analysis (FMEA) is the detection of all failures and their effects within a system, and the determination of the causes of these failures. However, the traditional FMEA method shows some important drawbacks regarding failure mode evaluations, risk factor weights and risk priority ranking, etc. This paper aims to develop a novel FMEA to improve the performance of FMEA by using spherical fuzzy sets (SFSs) and spherical fuzzy weight correlation coefficient (SF-WCC). The method mainly includes three stages. The SFS is applied to capture risk evaluation information because it comprehensively considers the information expression in different scenarios in the first stage. A spherical fuzzy projection model is introduced to calculate the expert weights objectively for reducing the information difference caused by experience, psychology and other cognitive factors of experts in the second stage. Meanwhile, the spherical fuzzy entropy is defined to determine the incomplete weights of risk factors by constructing an optimization model in the second stage. The SF-WCC is proposed to prioritize the failure modes to avoid the prioritization controversy of failure modes by using different distance formulas in the third stage. In the experiment, a practical example is presented to illustrate the applicability and effectiveness of the proposed method, and results show that the proposed approach offers a reliable tool for practical FMEA problems. The conclusion is that the proposed method is applicable to multiple actual scenarios as a reliable risk assessment technology.

Many consensus models in social network group decision making (SNGDM) have been reported to obtain a collective solution despite the initial opinions of decision makers (DMs) may be different. However, these models ignore the obstinacy of DMs to their initial opinions, which violates the sociological research results. Aiming at the consensus reaching of SNGDM where DMs are stubborn to their initial opinions, this paper proposes a novel consensus model based on the passive adjustment based on feedback mechanism (PA-FM) and active adjustment based on social interaction (AA-SI), which adaptively adopts AA-SI or PA-FM in each round according to the opinion distribution of DMs. Specially, the proposed consensus model assimilates the advantage of PA-FM and AA-SI, where adjustment intensity of DMs affects the consensus level of PA-FM, and stubbornness degree of DMs affects that of AA-SI. To elucidate the performance and advantages of the proposed consensus model, a hypothetical application and three simulation analyses are constructed. The results show that (1) In the decision-making group that is stubborn to the initial opinions, social interaction has a beneficial or harmful effect on the consensus reaching, depending on the stubbornness degree and opinion distribution of DMs; and (2) Given any stubbornness degree and adjustment intensity, the proposed consensus model outperforms existing consensus methods in consensus efficiency and success rate.

Classification is essential to the applications in the field of data mining, artificial intelligence, and fault detection. There exists a strong need in developing accurate, suitable, and efficient classification methods and algorithms with broad applicability. Random forest is a general algorithm that is often used for classification under complex conditions. Although it has been widely adopted, its combination with diverse fuzzy theory is still worth exploring. In this article, we propose the intuitionistic fuzzy random forest (IFRF), a new random forest ensemble of intuitionistic fuzzy decision trees (IFDT). Such trees in forest use intuitionistic fuzzy information gain to select features and consider hesitation in information transmission. The proposed method enjoys the power of the randomness from bootstrapped sampling and feature selection, the flexibility of fuzzy logic and fuzzy sets, and the robustness of multiple classifier systems. Extensive experiments demonstrate that the IFRF has competitive and superior performance compared to other state-of-the-art fuzzy and ensemble algorithms. IFDT is more suitable for ensemble learning with outstanding classification accuracy. This study is the first to propose a random forest ensemble based on the intuitionistic fuzzy theory.

Analyzing massive VAERS reports without medical context may lead to incorrect conclusions about vaccine adverse events (VAE). Facilitating VAE detection promotes continual safety improvement for new vaccines. This study proposes a multi-label classification method with various term-and topic-based label selection strategies to improve the accuracy and efficiency of VAE detection. Topic modeling methods are first used to generate rule-based label dependencies from Medical Dictionary for Regulatory Activities terms in VAE reports with two hyper-parameters. Multiple label selection strategies, namely one-vs-rest (OvsR), problem transformation (PT), algorithm adaption (AA), and deep learning (DL) methods, are used in multi-label classification to examine the model performance, respectively. Experimental results indicated that the topic-based PT methods improve the accuracy by up to 33.69% using a COVID-19 VAE reporting data set, which improves the robustness and interpretability of our models. In addition, the topic-based OvsR methods achieve an optimal accuracy of up to 98.88%. The accuracy of the AA methods with topic-based labels increased by up to 87.36%. By contrast, the state-of-art LSTM- and BERT-based DL methods have relatively poor performance with accuracy rates of 71.89% and 64.63%, respectively. Our findings reveal that the proposed method effectively improves the model accuracy and strengthens VAE interpretability by using different label selection strategies and domain knowledge in multi-label classification for VAE detection.

With the rapid growth in medical data, hospitals need to make enormous investments annually to expand computing resources. Cloud computing offers a platform for running medical services. However, sharing of medical data with unknown neighbors in the cloud environment may threaten the sensitive data of medical services. Private cloud provides a safety way to protect the sensitive data of medical services. But it is quite different from public cloud, since it is not easy to obtain more resources timely when the unpredictable workload is over the total amount of resources of private cloud. In addition, optimal resource allocation becomes a key issue as medical services possess distinctive features require different kinds of resource combination. In this article, an efficient resource management solution for medical services in hospital information system based on private cloud is proposed. We use intelligent control theory to adjust the resource allocation based on the dynamic workload adaptively, that effectively utilizes the limited resources of the private cloud while ensures the quality of services. The experiment results suggest that the proposed solution enables the efficient application of resources and reactions to unpredictable situations, which reduces the IT resources to hospitals.

This article investigates the feasibility of applying the broad learning system (BLS) to realize a novel Takagi–Sugeno–Kang (TSK) neuro-fuzzy model, namely a broad learning based dynamic fuzzy inference system (BL-DFIS). It not only improves the accuracy and interpretability of neuro-fuzzy models but also solves the challenging problem that models are incapable of determining the optimal architecture autonomously. BL-DFIS first accomplishes a TSK fuzzy system under the framework of BLS, in which an extreme learning machine auto-encoder is employed to obtain feature representation in a fast and analytical way, and an interpretable linguistic fuzzy rule is integrated into the enhancement node to ensure the high interpretability of the system. Meanwhile, the extended-enhancement unit is designed to achieve the first-order TSK fuzzy system. In addition, a dynamic incremental learning algorithm with internal pruning and updating mechanism is developed for the learning of BL-DFIS, which enables the system to automatically assemble the optimal structure to obtain a compact rule base and an excellent classification performance. Experiments on benchmark datasets demonstrate that the proposed BL-DFIS can achieve a better classification performance than some state-of-the-art nonfuzzy and neuro-fuzzy methods, simultaneously using the most parsimonious model structure.

The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method has been developed as one of the effective techniques to accomplish the best alternative selection of multicriteria decision-making (MCDM) problems. However, the existing PROMETHEE methods fail to adjust the representation range of uncertain information. Moreover, the weight determination and information aggregation in the PROMETHEE model still suffer from an excessive dependence of decision makers' (DMs') subjective judgments and the lack of considering the interrelationship among criteria. To solve the aforementioned drawbacks and provide DMs with a reliable and robust method of MCDM, a novel PROMETHEE method based on the q-rung orthopair fuzzy sets, that is, q-ROF-PROMETHEE model, is presented herein. First, the PROMETHEE-II method is extended to q-rung orthopair fuzzy (q-ROF) environment, which not only handles the uncertainty of human cognition but also provides DMs with a broader space to represent their decisions information. Second, a new optimization model is constructed based on the proposed cross entropy and q-ROF entropy, which is capable of leveraging the preference of DMs to obtain the optimal objective weights among criteria. In addition, the q-ROF weighted Hamy mean operator is embedded in the preference information aggregation of the PROMETHEE method so as to deal with the interrelationship among criteria. Finally, q-ROF-PROMETHEE is applied to a hospital performance evaluation case to demonstrate the proposed method's superiority, validity, and feasibility.

Class Activation Map (CAM) is the visualization of target regions generated from classification networks. However, classification network trained by class-level labels only has high responses to a few features of objects and thus the network cannot discriminate the whole target. We think that original labels used in classification tasks are not enough to describe all features of the objects. If we annotate more detailed labels like class-agnostic attribute labels for each image, the network may be able to mine larger CAM. Motivated by this idea, we propose and design common attribute labels, which are lower-level labels summarized from original image-level categories to describe more details of the target. Moreover, it should be emphasized that our proposed labels have good generalization on unknown categories since attributes (such as head, body, etc.) in some categories (such as dog, cat, etc.) are common and class-agnostic. That is why we call our proposed labels as common attribute labels, which are lower-level and more general compared with traditional labels. We finish the annotation work based on the PASCAL VOC2012 dataset and design a new architecture to successfully classify these common attribute labels. Then after fusing features of attribute labels into original categories, our network can mine larger CAMs of objects. Our method achieves better CAM results in visual and higher evaluation scores compared with traditional methods.

In this paper, a new tool, called q-rung orthopair uncertain linguistic numbers (q-ROULNs), is developed and a new multiattribute group decision making (MAGDM) method for q-ROULNs is proposed. First, the concept of q-rung orthopair uncertain linguistic sets (q-ROULSs) is introduced, and some operational laws, expected function, accuracy function, and distance measure of q-ROULSs are defined. Further, to effectively aggregate q-ROULNs, we take advantage of partitioned Heronian mean operator and power average operator and propose the q-rung orthopair uncertain linguistic power partitioned Heronian mean operator and its weighted form. The proposed operators not only deal with this situations where attributes are divided into several parts and attributes in the same part are interrelated each other, whereas attributes in different parts have no relationship, but also reduce the negative influence of unreasonable attribute values provided by decision makers on final results. Some desirable properties and special cases of the proposed operators are also investigated. Finally, a MAGDM method based on the proposed operators is developed and a numerical instance as well as comparative analysis is conducted to illustrate the effectiveness and advantages of the proposed method.

The International Classification of Diseases (ICD) not only serves as the bedrock for health statistics but also provides a holistic overview of every health aspect of life. This study aims to facilitate the computer-assisted coding of the 11th revision of the ICD (ICD-11) by leveraging the data structures of ICD-11 and semantics in WordNet. First, a computer-assisted coding framework using WordNet and ICD-11 application programming interface (API) is proposed. Secondly, a network based on entity relations in ICD-11 and synonym sets in WordNet, called CodeNet, is developed. Thirdly, an algorithm for generating ICD-11 code candidates from CodeNet with two tuning parameters on the basis of the input of disease-related text is illustrated. Finally, the discharge summaries in the Medical Information Mart for Intensive Care III database and textual information from ICD-11 entities are used to evaluate the proposed method. Experimental results indicate that the proposed coding method achieves a precision of 84% and a recall of 89% relative to a precision of 65% and a recall of 81% achieved with the existing ICD-11 API. The proposed method also outperforms other methods in the literature by reducing a failure rate of up to 8% in ICD-11 coding. The proposed thresholds of similarity and percentage can be applied to tuning the performance of our method to meet different coding needs. In sum, improving the new structures of ICD-11 with the use of semantics in WordNet can help develop more reliable computer-aided coding systems for ICD-11 coders.

The recently proposed q-rung orthopair fuzzy sets (q-ROFSs) have great powerfulness and ability to express decision makers' (DMs') fuzzy and complicated preference information over alternatives in the procedure of multiple attribute decision making (MADM). Aggregation operators (AOs) are one of the most important topics in the MADM theory. The purpose of this paper is to propose new q-rung orthopair fuzzy (q-ROF)AOs. Given the fact that attributes are usually correlated in most practical MADM problems and such kind of interrelationship should be taken into account when fusing the corresponding attribute values, we extend the powerful Hamy mean (HM) function to q-ROFSs and propose novel AOs for q-ROF information. The proposed AOs have inherent advantages and flexibility of HM function and are more suitable to deal with MADM problems in which DMs' evaluation information is expressed by q-ROF numbers (q-ROFNs). We further utilize the developed AOs to introduce a new MADM method. Finally, we use the proposed method to evaluate the quality of life (QOL) of cancer patients.

Chronic diseases are associated with high morbidity and mortality, and they cannot be quickly determined and identified during their early stages. Therefore, early identification and warning of chronic diseases are important. This study proposed an early warning model (EWM) based on electronic health records (EHRs). The model included comprehensive methods that identify whether the patient is expected to suffer from chronic disease and provide early warning based on the undiagnosed narrative text of EHRs. A professional medical terminology library called Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) was utilized to improve the accuracy of early warning. The model utilized semantic relationships in the SNOMED CT to expand the effects of related terms of renal cancer. We utilized 1,300 medical records and more than 1,400 progress notes in the EHRs in our experiments. The proposed EWM of renal cancer achieved a precision of 90%, recall of 91%, and F-measure of 91%.

Pythagorean fuzzy set (PFS) whose main feature is that the square sum of the membership degree and the non-membership degree is equal to or less than one, is a powerful tool to express fuzziness and uncertainty. The aim of this paper is to investigate aggregation operators of Pythagorean fuzzy numbers (PFNs) based on Frank t-conorm and t-norm. We first extend the Frank t-conorm and t-norm to Pythagorean fuzzy environments and develop several new operational laws of PFNs, based on which we propose two new Pythagorean fuzzy aggregation operators, such as Pythagorean fuzzy Choquet–Frank averaging operator (PFCFA) and Pythagorean fuzzy Choquet–Frank geometric operator (PFCFG). Moreover, some desirable properties and special cases of the operators are also investigated and discussed. Then, a novel approach to multi-attribute decision making (MADM) in Pythagorean fuzzy context is proposed based on these operators. Finally, a practical example is provided to illustrate the validity of the proposed method. The result shows effectiveness and flexible of the new method. A comparative analysis is also presented.

Current access control mechanisms of the hospital information system can hardly identify the real access intention of system users. A relaxed access control increases the risk of compromise of patient privacy. To reduce unnecessary access of patient information by hospital staff, this paper proposes a knowledge-constrained role-based access control (KC-RBAC) model in which a variety of medical domain knowledge is considered in access control. Based on the proposed Purpose Tree and knowledge-involved algorithms, the model can dynamically define the boundary of access to the patient information according to the context, which helps to protect patient privacy by controlling access. Compared with the role-based access control model, KC-RBAC can effectively protect patient information according to the results of the experiments.

Cognitive map is a powerful and useful tool for medical diagnosis. However, traditional fuzzy cognitive map cannot comprehensively represent experts ideas and some significant information is lost during the process of defuzzification. To overcome these drawbacks, a novel model called the interval-valued fuzzy cognitive map is introduced. In the proposed model, interval-valued fuzzy sets, rather than fuzzy sets, are employed to represent the concept nodes with their weights. A numerical example of breast cancer risk prediction is provided to illustrate the validity of the proposed model. Results show that the proposed model can enhance the diagnostic accuracy to 92.5%.

In recent years, many online health communities (OHCs) are established to provide the patients with the services of disease prevention and self-management. Patients in those online health communities discuss their health conditions and share their experiences with other patients using narrative texts in the posts. Those posts contain a vast amount of patients' information, including drugs, symptoms, conditions, etc. They are really valuable for clinical research. However, it is hard for information systems to automatically extract the clinical knowledge from those posts to provide knowledge-based services to online patients. This paper investigates the characteristics of those post contents and accordingly proposes an encoding method with Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) to process the texts into encoded and structured contents to help the analytic system in the OHCs to discover the biomedical knowledge. Based on our experimental result, the proposed method can effectively extract the biomedical knowledge from the posts and enhance the ability of clinical knowledge discovery online to improve the patient support.

The intuitionistic fuzzy set which is characterized by a membership degree and a non-membership degree, is a very powerful and useful tool to cope with fuzziness and uncertainty. Recently, the Pythagorean fuzzy set which is an extension of the intuitionistic fuzzy set has been introduced. In this paper, we focus on multi-attribute decision making with Pythagorean fuzzy information. First of all, considering the Bonferroni mean aggregation operator can take the interrelationship between arguments being fused into account, we introduce some Bonferroni mean-based aggregation operators for fusing Pythagorean fuzzy information. Then, a novel approach to multi-attribute decision making problems is proposed and discussed. Finally, an instance of supply chain management is provided to illustrate the validity of the new approach.

Access control is an important technical method to protect the sensitive data in the information system. This paper mainly focuses on the issue of privacy preserving for patients' information in HIS. On the basis of providing hospital employees necessary patient information that can support the treatment, the proposed Knowledge-Constrained Role Based Access Control (KC-RBAC) model tries to reduce the scope of patients' information that can be accessed by hospital employees. Compared with the traditional RBAC model, the medical knowledge and nonmedical knowledge lying in the process of treatment are introduced into KC-RBAC, which outline the boundary of accessible data for different users in the system.

Today's Internet is moving away from the essentially single domain and non-commercial Internet to a multi-domain, combined commercial and not-for-profit Internet. Policy is critical for each domain to protect its business interests. However, the current inter-domain routing protocol, BGP, only provides a policy mechanism where operators have to rely on mutual trust to protect themselves. This paper tries to measure and evaluate the current BGP policy model through passively logging BGP update messages. A policy-server based approach is proposed to address some of the problems discovered from the measurement.

Two of the objectives of Internet are to increase network capacity and offer high quality of differentiated services for traffic with real-time and nonreal-time requirements. Differentiated services (Diff-Serv) were established to fulfill such objectives. Up until now, several Diff-Serv schemes have been proposed which, among others, handle drop and delay priorities. These two priorities raise important optimization issues for the Internet but their relationship remains an open problem. This paper presents a fuzzy control algorithm to select packets in a fair and efficient manner. Simulation shows that the fuzzy controller is better than a crisp one when the fairness issue is raised.

Two outstanding problems of admission control and scheduling in networks with three and two workstations, respectively, are solved using fuzzy logic. Neither problem has been tackled up until now analytically, whereas the fuzzy approach provides computational solutions. In the first case, we have one workstation with two parallel ones. A reward is earned whenever the first stage accepts a customer and a holding cost is incurred by a customer in queue in the second stage. The class of customer to be next served by the first stage is dynamically selected so as to maximize an average benefit over an infinite horizon. In the second case, there are two parallel servers and three arrival processes generated by independent Poisson streams. Each server has its own queue and receives customers from its own arrival stream. A third arrival stream consists of customers with resource demand on both servers. Each customer pays a holding cost per unit time in the system. Again, the scheduling policy is specified which minimizes the average cost. The fuzzy models are new in this context and tackle computationally problems for which we have not analytical solutions.

Simple integrated media access (SIMA) is a differentiated service scheme based on drop preference bits in every packet. The basic idea of the SIMA is to maximize the exploitation of network resources with a simple control scheme while keeping the ratios of QoS levels offered to different flows unchanged under changeable traffic conditions with a aid of novel charging scheme. This paper examines the potential problems of the SIMA concept and then proposes some possible solutions in order to enhance the service efficiency.

The basic objectives for the future for the Internet are to increase network capacity, and to offer practically differentiated services for traffic with different requirements such as real-time and non-real-time services. These objectives introduce very strict requirements for the traffic control system and lead to the establishment of the differentiated services (Diff-Serv). So far, several Diff-Serv schemes have been proposed, and the concepts of drop and delay priorities are explicitly or implicitly discussed in all these schemes. These two priorities are of varying importance to different service requirements, and strongly interrelated. They expose an important issue in the optimization of the Internet. However, the relationship between these two priorities is rarely mentioned and hence it remains an open problem. This paper presents a fuzzy approach to this key but tough issue. Specifically, a differentiated service scheme with feedback preference information (FPI) is studied in more detail to illustrate the implementation of the new approach. Simulation shows that the approach is efficient and promising.

Two outstanding problems of admission control and scheduling in networks with three and two workstations respectively are solved using fuzzy logic. In the first case we have one workstation with two parallel ones. A reward is earned whenever the first stage accepts a customer and a holding cost is incurred by a customer in the queue in the second stage. The class of customer to be served next by the first stage is dynamically selected so as to maximize an average benefit over an infinite horizon. In the second case there are two parallel machines and three arrival processes generated by independent Poisson streams. Each machine has its own queue and receives parts from its own arrival stream. The third arrival stream consists of parts with resource demand on both machines. Each customer pays a holding cost per unit time in the system. Again, the scheduling policy is specified which minimizes the average cost. The fuzzy models are new in this context and tackle problems that cannot be solved by analysis.

We consider the problem of optimal control of queueing systems with heterogeneous servers in parallel. The system objective is to assign customers dynamically to idle servers based on the state of the system so as to minimize the average cost of holding customers. Three cases, either known in the literature or new, are studied in detail: queueing systems with server heterogeneity in-service rates, in-service functions, and both in-service rates and in-service functions. An approach is presented using fuzzy control to solve these problems. Simulation shows that this approach is efficient and promising, especially in cases where analytical solutions do not exist.

We consider two queues in tandem, each of which has its own input of arriving customers, which, in turn, may either be accepted or rejected. Suppose that the system receives a fixed reward for each accepted customer and pays a holding cost per customer per unit time in the system. The objective is to dynamically determine the optimal admission policies based on the state of the system so as to maximize the average profit (reward minus cost) over an infinite horizon. This model finds applications in flow control in communication systems, industrial job shops, and traffic-flow systems. In this paper, a novel approach is presented using fuzzy control. This approach explicitly finds the optimal policy, which up until now has defied analytical solutions.

We consider queueing systems in which the service rate is the controlled variable. The cost depends on the queue length and selected rate. The objective is to choose the service rate dynamically, based on the state of the system so as to minimize the average cost over an infinite horizon. Six classes, either known in the literature or new, are studied in detail: queueing systems with vacations, single-server queueing systems with and without switching costs, and tandem queueing systems with and without service costs. A novel approach is presented here using fuzzy control to solve these problems. Simulation shows that the approach is efficient and promising, especially in cases where analytical solutions do not exist.

This paper considers the problem of optimal control to tandem two-station production networks with two types of parts. Part routing, service rate selection and flow control, are simultaneously considered. We use fuzzy control to solve the problem. Simulation shows that the approach is efficient and promising.

We consider queueing systems in which the number of servers is the controlled variable. The cost structure includes service cost, switching cost and holding cost. The objective is to adjust the number of servers dynamically based on the state of the system so as to minimize the average cost over an infinite horizon. In this paper, a novel approach is presented using fuzzy control to solve this classical problem. Two cases, M/M/1 and M/M/m queueing systems with vacations, are studied in detail. Simulation shows that this new approach is efficient and promising, especially in cases where analytical solutions do not exist.

The problem of optimal control of queueing systems with heterogeneous servers is solved using fuzzy logic. Two known and one new case are tackled. Simulation shows that this approach is efficient and promising.