An integrated multiple attribute decision making methodology for quality evaluation of innovation and entrepreneurship education with interval-valued intuitionistic fuzzy information

Today’s higher vocational colleges have already put innovation and entrepreneurship education at the top of vocational education, and integrated it into the entire education and teaching work, in order to continuously improve the innovation and entrepreneurship ability of students in higher vocational colleges and improve their job competition. strength, and improve the quality of education in higher vocational colleges. The quality evaluation of innovation and entrepreneurship education in vocational colleges is a classical multiple attribute decision making (MADM) problems. In this paper, we introduced some calculating laws on interval-valued intuitionistic fuzzy sets (IVIFSs), Hamacher sum and Hamacher product and further propose the induced interval-valued intuitionistic fuzzy Hamacher power ordered weighted geometric (I-IVIFHPOWG) operator. Meanwhile, we also study some ideal properties of built operator. Then, we apply the I-IVIFHPOWG operator to deal with the MADM problems under IVIFSs. Finally, an example for quality evaluation of innovation and entrepreneurship education in vocational colleges is used to test this new approach.

Probabilistic linguistic fuzzy cognitive maps: applications to the critical factors affecting the health of rural older adults

Background

Achieving healthy ageing has become the only way for China to alleviate the pressure of ageing, especially in rural areas. However, the factors affecting the health of rural older adults are numerous and complex. It is important to identify the critical factors that affecting the health of older adults in rural areas and provide decision-making support for targeted health interventions.

Methods

To overcome some limitations of existing works, an extended probabilistic linguistic fuzzy cognitive map model is proposed in this paper as a useful tool for modeling the cause-effect relationship between factors. The proposed model integrates the advantages of probabilistic linguistic term sets and fuzzy cognitive maps. In the end, to rank and identify the critical factors affecting the health, a novel similarity measure based on Euclidean distance and Z-mapping function is proposed.

Results

The proposed model can effectively deal with the uncertainty of experts and reflect different opinions of groups well. In terms of representing uncertainty and ambiguity, the proposed method outperforms other models in modeling complex systems. In the real-world case analysis, we find that education is the most important factor affecting the health of rural older adults, followed by previous occupational experiences, psychology, and physical exercise, among other things. Intergenerational relationship has become another important factor affecting the health of rural older adults in China as the development of Chinese society.

Conclusions

From a macro perspective, social economic status, living environment, lifestyle, and health management, are the variables that have the greatest impact on the health of rural older adults. As a result, providing more precise health interventions with the characteristics of factors influencing health is a crucial guarantee for preserving and improving the health of rural older adults in China.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-02028-9.

Method for education value evaluation with regional culture integration into international students’ ideological education under fuzzy number intuitionistic fuzzy environment

For thousands of years, the Chinese people have accumulated and inherited profound cultural traditions. The uniqueness of this cultural tradition lies in its amazing creative wisdom and power. “The ideological and political education of the integration of Chinese regional culture into international students refers to the educative influence of excellent regional culture that can run through the entire international education management system, curriculum system and extracurricular practice system to achieve “all-round, full-process, full-staff” Education goals. The sustainable education value evaluation based on the integration of regional culture into international students’ ideological education is a classical multiple-attribute decision-making (MADM) issue. In this paper, we extend the geometric Heronian mean (GHM) operator to fuzzy number intuitionistic fuzzy numbers (FNIFNs) to propose the fuzzy number intuitionistic fuzzy GHM (FNIFGHM) operator. Then, the multiple-attribute decision-making (MADM) methods are built on FNIFGHM operator. Finally, a numerical example for sustainable education value evaluation based on the integration of regional culture into international students’ ideological education and some comparative analysis are used to prove the built methods’ credibility and reliability.

Methodology for building material suppliers selection based on the intuitionistic fuzzy multiple attribute decision making method

The selection of suppliers is an important part of the construction of engineering projects in supply chain management. If the partners in the supply chain are reliable enough, they can promote the continuous progress of the supply and demand sides in the cooperation, thereby achieving a win-win situation, which is conducive to the realization of a virtuous cycle process. Material suppliers provide the required products and raw materials for the production and construction of enterprises. They are an important source of construction projects and occupy a very important position in the development of enterprises. The supply of high-quality products can lay a good foundation for the subsequent production and construction of the project, thereby promoting the smooth completion of the entire project. Therefore, rational evaluation and selection of suppliers has very important practical significance. The selection and application of building material suppliers is a classic multiple attribute decision making (MADM). In this paper, we introduced some calculating laws on intuitionistic fuzzy sets (IFSs), Hamacher sum and Hamacher product and further propose the induced intuitionistic fuzzy Hamacher power ordered weighted geometric (I-IFHPOWG) operator. Meanwhile, we also study some ideal properties of built operator. Then, we apply the I-IFHPOWG operator to deal with the multiple attribute decision making (MADM) problems under IFSs. Finally, an example for physical health literacy evaluation of College students is used to test this new approach.

An integrated methodology for utilization efficiency evaluation of college stadiums based on fuzzy number intuitionistic fuzzy multiple attribute group decision-making

In recent years, with the steady development of the national economy and the continuous improvement of people’s living standards, the desire for material pursuits has gradually transformed into the pursuit of spiritual food, and the attention to health and body is highly valued. It gave birth to and promoted the development of the sports industry. High-standard college stadiums provide many conveniences for students and faculty, and the construction and management of college stadiums are also an important part of the development of my country’s sports industry. However, there are still some drawbacks in the management mode and utilization efficiency of college stadiums. The utilization efficiency evaluation of college stadiums is frequently looked as the multiple attribute group decision-making (MAGDM) problem. Depending on the VIKOR process and fuzzy number intuitionistic fuzzy sets (FNIFSs), this paper designs a novel FNIF-VIKOR process to assess the resource utilization efficiency of college stadiums. First of all, some basic theories related to FNIFSs are briefly introduced. In addition, the weights of attributes are obtained objectively by utilizing CRITIC weight method. Afterwards, the conventional VIKOR process is extended to FNIFSs to obtain the final order of the alternative. Eventually, an application case for utilization efficiency evaluation of college stadiums and some comparative analysis are fully given. The results show that the built algorithms method is useful for assessing the resource utilization efficiency of college stadiums.

TOPSIS methods for probabilistic hesitant fuzzy multiple attribute group decision-making and application to performance evaluation of public charging service quality

The performance evaluation of public charging service quality is frequently viewed as the multiple attribute group decision-making (MAGDM) issue. In this paper, an extended TOPSIS model is established to provide a new means to solve the performance evaluation of public charging service quality. The TOPSIS method integrated with FUCOM method in probabilistic hesitant fuzzy circumstance is applied to rank the optional alternatives and a numerical example for performance evaluation of public charging service quality is used to test the newly proposed method’s practicability with the comparison with other methods. The results display that the approach is uncomplicated, valid and simple to compute. The main results of this paper: (1) A novel PHF-TOPSIS method is proposed; (2) The extended TOPSIS method is developed in the probabilistic hesitant fuzzy environment; (3) The FUCOM method is used to obtain the attribute weight; (4) The normalization process of the original data has adapted the latest method to verify the precision; (5) The built models and methods are useful for other selection issues and evaluation issues.

An integrated methodology for selecting the building material suppliers with interval-valued intuitionistic fuzzy information

At present, with the continuous changes in the market situation and the continuous improvement of the supply chain network structure, the competition in all walks of life has become more and more intense, which has risen from simple enterprise competition to competition in the entire supply chain. In the construction industry, the structure of the construction supply chain is more complex and diverse, and it is more necessary to select high-quality suppliers for sincere cooperation. This requires construction companies to establish a complete supply chain management system, select high-quality suppliers to achieve win-win cooperation and improve their competitiveness. Therefore, construction enterprises need to comprehensively consider various factors, build a reasonable and feasible evaluation index system according to their own demand for materials, and use appropriate evaluation methods to select material suppliers with specific advantages, so as to ensure the entire construction supply chain of the project. of smooth operation. In this paper, we introduced some calculating laws on interval-valued intuitionistic fuzzy sets (IVIFSs), Hamacher sum and Hamacher product and further propose the induced interval-valued intuitionistic fuzzy Hamacher ordered weighted average (I-IVIFHOWA) operator. Meanwhile, we also study some ideal properties of built operator. Then, we apply the I-IVIFHOWA operator to deal with the multiple attribute decision making (MADM) problems under IVIFSs. Finally, an example for selecting the building material suppliers is used to test this new approach.

The prediction of online time series with concept drift based on dynamic intuitionistic fuzzy cognitive map

Fuzzy cognitive maps (FCMs) have widely been applied for knowledge representation and reasoning. However, in real life, reasoning is always accompanied with hesitation, which is deriving from the uncertainty and fuzziness. Especially, when processing the online data, since the internal and external interference, the distribution and characteristics of sequence data would be considerably changed along with the passage of time, which further increase the difficulty of modeling. In this article, based on intuitionistic fuzzy set theory, a new dynamic intuitionistic fuzzy cognitive map (DIFCM) scheme is proposed for online data prediction. Combined with a novel detection algorithm of concept drift, the structure of DIFCM can be adaptively updated with the online learning scheme, which can effectively improve the representation of online information by capturing the real-time changes of sequence data. Moreover, in order to tackle with the possible hesitancy in the process of modeling, intuitionistic fuzzy set is applied in the construction of dynamic FCM, where hesitation degree as a quantitative index explicitly expresses the hesitancy. Finally, a series of experiments using public data sets verify the effectiveness of the proposed method.

Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

Current machine intelligence metrics rely on a different philosophy, hindering their effective comparison. There is no standardization of what is machine intelligence and what should be measured to quantify it. In this study, we investigate the measurement of intelligence from the viewpoint of real-life difficult-problem-solving abilities, and we highlight the importance of being able to make accurate and robust comparisons between multiple cooperative multiagent systems (CMASs) using a novel metric. A recent metric presented in the scientific literature, called MetrIntPair, is capable of comparing the intelligence of only two CMASs at an application. In this paper, we propose a generalization of that metric called MetrIntPairII. MetrIntPairII is based on pairwise problem-solving intelligence comparisons (for the same problem, the problem-solving intelligence of the studied CMASs is evaluated experimentally in pairs). The pairwise intelligence comparison is proposed to decrease the necessary number of experimental intelligence measurements. MetrIntPairII has the same properties as MetrIntPair, with the main advantage that it can be applied to any number of CMASs conserving the accuracy of the comparison, while it exhibits enhanced robustness. An important property of the proposed metric is the universality, as it can be applied as a black-box method to intelligent agent-based systems (IABSs) generally, not depending on the aspect of IABS architecture. To demonstrate the effectiveness of the MetrIntPairII metric, we provide a representative experimental study, comparing the intelligence of several CMASs composed of agents specialized in solving an NP-hard problem.

On intuitionistic fuzzy order-α divergence and entropy measures with MABAC method for multiple attribute group decision-making

The development of information measures associated with fuzzy and intuitionistic fuzzy sets is an important research area from the past few decades. Divergence and entropy are two significant information measures in the intuitionistic fuzzy set (IFS) theory, which have gained wider attention from researchers due to their extensive applications in different areas. In the literature, the existing information measures for IFSs have some drawbacks, which make them irrelevant to use in application areas. In order to obtain more robust and flexible information measures for IFSs, the present work develops and studies some parametric information measures under the intuitionistic fuzzy environment. First, the paper reviews the existing intuitionistic fuzzy divergence measures in detail with their shortcomings and then proposes four new order-α divergence measures between two IFSs. It is worth mentioning that the developed divergence measures satisfy several elegant mathematical properties. Second, we define four new entropy measures called order-α intuitionistic fuzzy entropy measures in order to quantify the fuzziness associated with an IFS. We prove basic and advanced properties of the order-α intuitionistic fuzzy entropy measures for justifying their validity. The paper shows that the introduced measures include various existing fuzzy and intuitionistic fuzzy information measures as their special cases. Further, utilizing the conventional multi-attributive border approximation area comparison (MABAC) model, we develop an intuitionistic fuzzy MABAC method to solve real-life multiple attribute group decision-making problems. Finally, the proposed method is demonstrated by using a practical application of personnel selection.

On the Road: Route Proposal from Radar Self-Supervised by Fuzzy LiDAR Traversability

This is motivated by a requirement for robust, autonomy-enabling scene understanding in unknown environments. In the method proposed in this paper, discriminative machine-learning approaches are applied to infer traversability and predict routes from Frequency-Modulated Contunuous-Wave (FMCV) radar frames. Firstly, using geometric features extracted from LiDAR point clouds as inputs to a fuzzy-logic rule set, traversability pseudo-labels are assigned to radar frames from which weak supervision is applied to learn traversability from radar. Secondly, routes through the scanned environment can be predicted after they are learned from the odometry traces arising from traversals demonstrated by the autonomous vehicle (AV). In conjunction, therefore, a model pretrained for traversability prediction is used to enhance the performance of the route proposal architecture. Experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community. Our key finding is that joint learning of traversability and demonstrated routes lends itself best to a model which understands where the vehicle should feasibly drive. We show that the traversability characteristics can be recovered satisfactorily, so that this recovered representation can be used in optimal path planning, and that an end-to-end formulation including both traversability feature extraction and routes learned by expert demonstration recovers smooth, drivable paths that are comprehensive in their coverage of the underlying road network. We conclude that the proposed system will find use in enabling mapless vehicle autonomy in extreme environments.

A cognitive map integrated intuitionistic fuzzy decision-making procedure for provider selection in project management

Organizations make use of project management methodologies, which provide an effective manner to achieve managerial goals, maintain the strength of the companies in increasing competition. Efficiency in planning, budgeting, and scheduling are provided so that high quality outputs are obtained through these processes. Agile project management methodology, which has been emerged from unpredictability of customer requirements and changeable business environment, is apt to cope with the failures of traditional project management tools. Besides, lean six-sigma project management methodology has become a combination of lean and six-sigma, which were opponent methodologies previously. This paper aims to determine the most suitable outsourcing provider alternative by presenting a novel cognitive maps-based intuitionistic fuzzy decision making procedure. Interrelationships among evaluation criteria are weighted employing intuitionistic fuzzy cognitive map technique because of the causal links among evaluation criteria, vagueness, fuzziness, and hesitation in data. Moreover, the most appropriate provider alternative for both agile and lean six-sigma project management methodologies is identified by utilizing intuitionistic fuzzy TOPSIS method, which aims for minimizing the closeness to the ideal solution while maximizing the distance from the anti-ideal solution in hesitative environment. The case study is carried out in a bank that performs in Turkish banking sector.

A Discourse on the Incorporation of Organizational Factors into Probabilistic Risk Assessment: Key Questions and Categorical Review

This article presents a discourse on the incorporation of organizational factors into probabilistic risk assessment (PRA)/probabilistic safety assessment (PSA), a topic of debate since the 1980s that has spurred discussions among industry, regulatory agencies, and the research community. The main contributions of this article include (1) identifying the four key open questions associated with this topic; (2) framing ongoing debates by considering differing perspectives around each question; (3) offering a categorical review of existing studies on this topic to justify the selection of each question and to analyze the challenges related to each perspective; and (4) highlighting the directions of research required to reach a final resolution for each question. The four key questions are: (I) How significant is the contribution of organizational factors to accidents and incidents? (II) How critical, with respect to improving risk assessment, is the explicit incorporation of organizational factors into PRA? (III) What theoretical bases are needed for explicit incorporation of organizational factors into PRA? (IV) What methodological bases are needed for the explicit incorporation of organizational factors into PRA? Questions I and II mainly analyze PRA literature from the nuclear domain. For Questions III and IV, a broader review and categorization is conducted of those existing cross-disciplinary studies that have evaluated the effects of organizational factors on safety (not solely PRA-based) to shed more light on future research needs.

Building a Collaborative Model of Sacroiliac Joint Dysfunction and Pelvic Girdle Pain to Understand the Diverse Perspectives of Experts

BACKGROUND

Pelvic girdle pain (PGP) and sacroiliac joint (SIJ) dysfunction/pain are considered frequent contributors to low back pain (LBP). Like other persistent pain conditions, PGP is increasingly recognized as a multifactorial problem involving biological, psychological, and social factors. Perspectives differ between experts and a diversity of treatments (with variable degrees of evidence) have been utilized.

OBJECTIVE

To develop a collaborative model of PGP that represents the collective view of a group of experts. Specific goals were to analyze structure and composition of conceptual models contributed by participants, to aggregate them into a metamodel, to analyze the metamodel's composition, and to consider predicted efficacy of treatments.

DESIGN

To develop a collaborative model of PGP, models were generated by invited individuals to represent their understanding of PGP using fuzzy cognitive mapping (FCM). FCMs involved proposal of components related to causes, outcomes, and treatments for pain, disability, and quality of life, and their connections. Components were classified into thematic categories. Weighting of connections was summed for components to judge their relative importance. FCMs were aggregated into a metamodel for analysis of the collective opinion it represented and to evaluate expected efficacy of treatments.

RESULTS

From 21 potential contributors, 14 (67%) agreed to participate (representing six disciplines and seven countries). Participants' models included a mean (SD) of 22 (5) components each. FCMs were refined to combine similar terms, leaving 89 components in 10 categories. Biomechanical factors were the most important in individual FCMs. The collective opinion from the metamodel predicted greatest efficacy for injection, exercise therapy, and surgery for pain relief.

CONCLUSIONS

The collaborative model of PGP showed a bias toward biomechanical factors. Most efficacious treatments predicted by the model have modest to no evidence from clinical trials, suggesting a mismatch between opinion and evidence. The model enables integration and communication of the collection of opinions on PGP.

A YinYang bipolar fuzzy cognitive TOPSIS method to bipolar disorder diagnosis

BACKGROUND AND OBJECTIVE

Bipolar disorder is often mis-diagnosed as unipolar depression in the clinical diagnosis. The main reason is that, different from other diseases, bipolarity is the norm rather than exception in bipolar disorder diagnosis. YinYang bipolar fuzzy set captures bipolarity and has been successfully used to construct a unified inference mathematical modeling method to bipolar disorder clinical diagnosis. Nevertheless, symptoms and their interrelationships are not considered in the existing method, circumventing its ability to describe complexity of bipolar disorder. Thus, in this paper, a YinYang bipolar fuzzy multi-criteria group decision making method to bipolar disorder clinical diagnosis is developed.

METHODS

Comparing with the existing method, the new one is more comprehensive. The merits of the new method are listed as follows: First of all, multi-criteria group decision making method is introduced into bipolar disorder diagnosis for considering different symptoms and multiple doctors' opinions. Secondly, the discreet diagnosis principle is adopted by the revised TOPSIS method. Last but not the least, YinYang bipolar fuzzy cognitive map is provided for the understanding of interrelations among symptoms.

RESULTS

The illustrated case demonstrates the feasibility, validity, and necessity of the theoretical results obtained. Moreover, the comparison analysis demonstrates that the diagnosis result is more accurate, when interrelations about symptoms are considered in the proposed method.

CONCLUSIONS

In a conclusion, the main contribution of this paper is to provide a comprehensive mathematical approach to improve the accuracy of bipolar disorder clinical diagnosis, in which both bipolarity and complexity are considered.

A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications

BACKGROUND AND OBJECTIVE

A high percentage of medical errors, committed because of physician's lack of experience, huge volume of data to be analyzed, and inaccessibility to medical records of previous patients, can be reduced using computer-aided techniques. Therefore, designing more efficient medical decision-support systems (MDSSs) to assist physicians in decision-making is crucially important. Through combining the properties of fuzzy logic and neural networks, fuzzy cognitive maps (FCMs) are among the latest, most efficient, and strongest artificial intelligence techniques for modeling complex systems. This review study is conducted to identify different FCM structures used in MDSS designs. The best structure for each medical application can be introduced by studying the properties of FCM structures.

METHODS

This paper surveys the most important decision- making methods and applications of FCMs in the medical field in recent years. To investigate the efficiency and capability of different FCM models in designing MDSSs, medical applications are categorized into four key areas: decision-making, diagnosis, prediction, and classification. Also, various diagnosis and decision support problems addressed by FCMs in recent years are reviewed with the goal of introducing different types of FCMs and determining their contribution to the improvements made in the fields of medical diagnosis and treatment.

RESULTS

In this survey, a general trend for future studies in this field is provided by analyzing various FCM structures used for medical purposes, and the results from each category.

CONCLUSIONS

Due to the unique specifications of FCMs in integrating human knowledge and experience with computer-aided techniques, they are among practical instruments for MDSS design. In the not too distant future, they will have a significant role in medical sciences.

Fuzzy context-specific intention inference for robotic caregiving

To provide timely and appropriate assistance, robots must have the capability of proactively understanding a user’s personal needs, the so-called human intention inference. In human–human interaction, humans have a natural and implicit way to infer others’ intentions by selecting correlated context features and interpreting these features based on their life experience. However, robots do not have this capability and it is not realistic to build an explicit formula to associate human intentions with context. In this article, a novel fuzzy context-specific intention inference method is developed for human-like implicit human intention inference. With a fuzzy manner, context features are converted into discrete context statuses, which are similar to human subjective feelings. An intention-centered common sense database is developed consisting of correlated fuzzy context statuses, object affordances, and their relationship with human intentions. With this database, a Fuzzy Naïve Bayesian Network algorithm is adopted for implicit intention inference. Home scenario results validated the fuzzy context-specific intention inference methods reliability and lab scenario results validated the fuzzy context-specific intention inference methods effectiveness and robustness. This work is expected to develop intuitive and effective human–robot interaction, consequently enhancing the adoption of assistive technologies and improving the independence of the disabled and elderly in activities of daily living.

Designing for Risk Assessment Systems for Patient Triage in Primary Health Care: A Literature Review

Background

This literature review covers original journal papers published between 2011 and 2015. These papers review the current status of research on the application of human factors and ergonomics in risk assessment systems’ design to cope with the complexity, singularity, and danger in patient triage in primary health care.

Objective

This paper presents a systematic literature review that aims to identify, analyze, and interpret the application of available evidence from human factors and ergonomics to the design of tools, devices, and work processes to support risk assessment in the context of health care.

Methods

Electronic search was performed on 7 bibliographic databases of health sciences, engineering, and computer sciences disciplines. The quality and suitability of primary studies were evaluated, and selected papers were classified according to 4 classes of outcomes.

Results

A total of 1845 papers were retrieved by the initial search, culminating in 16 selected for data extraction after the application of inclusion and exclusion criteria and quality and suitability evaluation.

Conclusions

Results point out that the study of the implications of the lack of understanding about real work performance in designing for risk assessment in health care is very specific, little explored, and mostly focused on the development of tools.

An integrated breast cancer risk assessment and management model based on fuzzy cognitive maps

BACKGROUND

There is a growing demand for women to be classified into different risk groups of developing breast cancer (BC). The focus of the reported work is on the development of an integrated risk prediction model using a two-level fuzzy cognitive map (FCM) model. The proposed model combines the results of the initial screening mammogram of the given woman with her demographic risk factors to predict the post-screening risk of developing BC.

METHODS

The level-1 FCM models the demographic risk profile. A nonlinear Hebbian learning algorithm is used to train this model and thus to help on predicting the BC risk grade based on demographic risk factors identified by domain experts. The risk grades estimated by the proposed model are validated using two standard BC risk assessment models viz. Gail and Tyrer-Cuzick. The level-2 FCM models the features of the screening mammogram concerning normal, benign and malignant cases. The data driven Hebbian learning algorithm (DDNHL) is used to train this model in order to predict the BC risk grade based on these mammographic image features. An overall risk grade is calculated by combining the outcomes of these two FCMs.

RESULTS

The main limitation of the Gail model of underestimating the risk level of women with strong family history is overcome by the proposed model. IBIS is a hard computing tool based on the Tyrer-Cuzick model that is comprehensive enough in covering a wide range of demographic risk factors including family history, but it generates results in terms of numeric risk score based on predefined formulae. Thus the outcome is difficult to interpret by naive users. Besides these models are based only on the demographic details and do not take into account the findings of the screening mammogram. The proposed integrated model overcomes the above described limitations of the existing models and predicts the risk level in terms of qualitative grades. The predictions of the proposed NHL-FCM model comply with the Tyrer-Cuzick model for 36 out of 40 patient cases. With respect to tumor grading, the overall classification accuracy of DDNHL-FCM using 70 real mammogram screening images is 94.3%. The testing accuracy of the proposed model using 10-fold cross validation technique outperforms other standard machine learning based inference engines.

CONCLUSION

In the perspective of clinical oncologists, this is a comprehensive front-end medical decision support system that assists them in efficiently assessing the expected post-screening BC risk level of the given individual and hence prescribing individualized preventive interventions and more intensive surveillance for high risk women.

Application of evolutionary fuzzy cognitive maps for prediction of pulmonary infections

In this paper, a new evolutionary-based fuzzy cognitive map (FCM) methodology is proposed to cope with the forecasting of the patient states in the case of pulmonary infections. The goal of the research was to improve the efficiency of the prediction. This was succeeded with a new data fuzzification procedure for observables and optimization of gain of transformation function using the evolutionary learning for the construction of FCM model. The approach proposed in this paper was validated using real patient data from internal care unit. The results emerged had less prediction errors for the examined data records than those produced by the conventional genetic-based algorithmic approaches.