A multi-stage learning-based fuzzy cognitive maps for tobacco use

Çiftçi, Pınar Kocabey; Durmuşoğlu, Zeynep Didem Unutmaz

doi:10.1007/s00521-020-04860-4

Cited by 5 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, 2008b). The types of learning algorithms can be categorized in three groups: Hebbian-like algorithms, evolutionary algorithms and hybrid algorithms (Kocabey Çiftçi and Unutmaz Durmuşoğlu, 2020). The presented paper uses a hybrid algorithm that integrates NHL algorithm from Hebbian-like algorithms and Extended Great Deluge Algorithm (EGDA) from evolutionary algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…The presented study focuses on examining the effects of COVID-19 pandemic on supplier’ performance for personal and home care products manufacturer. For this aim, FCM was trained with a hybrid learning system (NHL-EGDA) primarily suggested by Ren, in 2012 and then implemented on examining tobacco use behaviors of people in the study of Kocabey Çiftçi and Unutmaz Durmuşoğlu (2020). The results of the study indicated that the hybrid learning system provided better results when compared to single usage of the algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this study, FCM is supported with a hybrid learning system which integrates Non-linear Hebbian Learning Algorithm (NHL) and Extended Great Deluge Algorithm (EGDA) to increase the accuracy of the presented study and eliminate the subjectivity of using expert knowledge. The combinatory usage of these two different algorithms primarily suggested by (Ren, 2012) and then implemented on examining tobacco use behaviors of people in the study of (Kocabey Çiftçi and Unutmaz Durmuşoğlu, 2020). The results of the study indicated that the hybrid learning system provided better results when compared to single usage of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analyzing the effects of COVID-19 pandemic on supplier performance evaluation factors via fuzzy cognitive map with a hybrid learning system

Çiftçi

2022

IJOEM

View full text Add to dashboard Cite

PurposeThe COVID-19 pandemic has proven that how supply chain management (SCM) can become a crucial process for sustainability of the world's production/service. The global supply chain crisis during pandemic has affected most of the sectors. Home and personal care products manufacturers are among them. In this study (1) the problems at SCM of personal and home care products manufacturers during pandemic are discussed with the help of medium-size manufacturer and (2) the factors affecting suppliers' performance for the relevant sector during COVID-19 are analyzed comprehensively.Design/methodology/approachThe importance of the factors is evaluated using fuzzy cognitive maps that can help to reveal hidden casual relationships with the help of expert knowledge. In order to eliminate subjectivity due to usage of expert knowledge, the maps are trained with a hybrid learning approach that consists of Non-linear Learning and Extended Great Deluge Algorithms to increase robustness of the analysis.FindingsThe findings of the study indicate that the factors such as general quality level of products/services, compliance to delivery time, communication skills and total production capacity of suppliers have been crucial factors during pandemic.Originality/valueWhile the implementation of the hybrid learning approach on supply chain can fill the gap in the relevant literature, the promising results of the study can prove the convenience of the methodology to model the of complex systems like supply chain processes.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analyzing the effects of COVID-19 pandemic on supplier performance evaluation factors via fuzzy cognitive map with a hybrid learning system

Çiftçi

2022

IJOEM

View full text Add to dashboard Cite

show abstract

“…In this paper, a sigmoid activation function is considered that gives values of concepts in the range [0, 1]. As a result of its widespread application and positive outcomes (Kocabey Çiftçi & Unutmaz Durmuşoğlu, 2020 ), the sigmoid function is preferred. The mathematical formulation of the sigmoid function is given as follows: where α is a real positive number that describes its steepness, and x is the value of at the equilibrium point.…”

Section: Preliminariesmentioning

confidence: 99%

Analyzing society anti-vaccination attitudes towards COVID-19: combining latent dirichlet allocation and fuzzy association rule mining with a fuzzy cognitive map

Eligüzel

2023

Fuzzy Optim Decis Making

View full text Add to dashboard Cite

COVID-19 has been declared a pandemic and countries are tackling this disease either through preventative measures such as lockdown and sanitization or through curative ones such as medication, isolation, and so on. Some people believe that vaccination is the best way to prevent this disease, while others disagree. Society’s attitudes toward vaccination can be influenced by a variety of factors such as misunderstanding, ambiguity, lack of knowledge. The proposed study’s goal is to better understand people’s attitudes regarding vaccination by focusing on key topics related to COVID-19 anti-vaccine tweets. Tweets are obtained over a period based on the number of COVID-19 cases by utilizing the “anti-vaccine” keyword rather than the “vaccine” keyword. Furthermore, in addition to people perceptions and attitudes toward anti-vaccination, the causal relationship between each topic is investigated. As a result, latent dirichlet allocation (LDA), fuzzy association rule mining (FARM), fuzzy cognitive map (FCM), and fuzzy c-means are used to conduct a complete study. Topics are analyzed independently using clustering and scenario analysis. The findings demonstrate the most common topics in anti-vaccination tweets, as well as the influence of each topic on the others.

show abstract

A hybrid interval‐valued time series prediction model incorporating intuitionistic fuzzy cognitive map and fuzzy neural network

Zhang,

Tao,

Liu

et al. 2024

Journal of Forecasting

View full text Add to dashboard Cite

The definition of interval‐valued time series is now a valid tool that can be used to model uncertainty with known numerical bounds. However, how to provide accurate predictions of interval‐valued time series remains an open problem. The goal of this paper is to develop a hybrid interval‐valued time series prediction model that incorporates an intuitionistic fuzzy cognitive map and a fuzzy neural network. The causal relationship and adjacency matrix among nodes of the intuitionistic fuzzy cognitive map are defined and quantified using mutual subsethhood, in which the hesitation weight is added to the connection weight among concept nodes. The approach directly constructs concept nodes and a weight matrix for automatic recognition of intuitionistic fuzzy cognitive maps from original sequence data and combines the particle swarm optimization algorithm and back propagation algorithm to run with less manual intervention. The confidence intervals of forecasted interval values are also discussed. The developed prediction model is applied to forecast interval‐valued financial time series (i.e., the Nasdaq‐100 stock index), which is composed of daily minimum price and maximum price. The feasibility and validity of the proposed developed prediction model are shown through comparisons with some existing prediction models on interval‐valued time series.

show abstract

A multi-stage learning-based fuzzy cognitive maps for tobacco use

Cited by 5 publications

References 43 publications

Analyzing the effects of COVID-19 pandemic on supplier performance evaluation factors via fuzzy cognitive map with a hybrid learning system

Analyzing the effects of COVID-19 pandemic on supplier performance evaluation factors via fuzzy cognitive map with a hybrid learning system

Analyzing society anti-vaccination attitudes towards COVID-19: combining latent dirichlet allocation and fuzzy association rule mining with a fuzzy cognitive map

A hybrid interval‐valued time series prediction model incorporating intuitionistic fuzzy cognitive map and fuzzy neural network

Contact Info

Product

Resources

About