Assessment of Vaccination Strategies Using Fuzzy Multi-criteria Decision Making

Lopez, Daphne; Gunasekaran, M.

doi:10.1007/978-3-319-27212-2_16

Cited by 52 publications

(13 citation statements)

References 17 publications

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“…The previous researches motivated us to propose a study for solving NLP problems. There does not exist any researches which solve neutrosophic linear programming problems with trapezoidal neutrosophic numbers [40][41][42][43][44][45].…”

Section: Literature Reviewmentioning

confidence: 99%

RETRACTED ARTICLE: A novel method for solving the fully neutrosophic linear programming problems

Abdel‐Basset

Gunasekaran

Mohamed

et al. 2018

Neural Comput & Applic

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The most widely used technique for solving and optimizing a real-life problem is linear programming (LP), due to its simplicity and efficiency. However, in order to handle the impreciseness in the data, the neutrosophic set theory plays a vital role which makes a simulation of the decision-making process of humans by considering all aspects of decision (i.e., agree, not sure and disagree). By keeping the advantages of it, in the present work, we have introduced the neutrosophic LP models where their parameters are represented with a trapezoidal neutrosophic numbers and presented a technique for solving them. The presented approach has been illustrated with some numerical examples and shows their superiority with the state of the art by comparison. Finally, we conclude that proposed approach is simpler, efficient and capable of solving the LP models as compared to other methods.

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Section: Literature Reviewmentioning

confidence: 99%

RETRACTED ARTICLE: A novel method for solving the fully neutrosophic linear programming problems

Abdel‐Basset

Gunasekaran

Mohamed

et al. 2018

Neural Comput & Applic

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“…Opricovic had developed VIKOR (VIsekriterijumska optimizacija iKOmpromisno Resenje) MCDM method to identify the optimal solution based on different criteria (Opricovic et al 2007) by considering the variances in the preference values of different individuals involved in the decision-making process (Wang et al 2009). Clinical diagnosis and epidemiological analysis have a high level of uncertainty and ambiguity (Broekhuizen et al 2015, Lopez et al 2015Lopez et al 2014), and decision-making models need to process and resolve this uncertainty. Fuzzy logic based VIKOR method is used in this paper to resolve uncertainties in decision making, with a focus on dengue prevention and control strategies.…”

Section: Multi-criteria Decision Makingmentioning

confidence: 99%

An intelligent decision support system to prevent and control of dengue

Lopez

Manogaran

Varatharajan

et al. 2018

J Ambient Intell Human Comput

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Demographic shifts, social behavior modification, and climate change enhance the risk of dengue outbreaks globally. The Government of India has developed prevention and control strategies for dengue fever. We propose an intelligent decision support system based on multi-criteria decision analysis to improve strategic decision making among various alternatives, such as vaccination, disease surveillance, vector control, improved sanitation and access to safe drinking water, strengthening public health activities, awareness creation, and improving nutrition for women and children. The proposed alternatives are selected based on people, space and time criteria, such as low temperature and heavy rain, high temperature and high humidity, rainfall and water accumulation, resources and facilities, sociocultural and sociodemographic variables. The selection of alternatives based on multiple criteria is a complex decision-making problem. Policy and decision makers use linguistic terms to give their opinions and perspectives. We used the fuzzy logic based VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) method to analyze the linguistic terms collected from policy and decision makers to rank the optimal alternatives based on multiple criteria.

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“…Furthermore, four big data use cases have been identified in healthcare that includes clinical decision, consumer behaviour, and support services (Hermon and Williams, 2014). In addition, recent studies describe how to reshape the healthcare system based on big data analytics to improve the patient treatment, disease control and diagnosis (Jee and Kim, 2013;Lopez et al, 2014;Lopez and Gunasekaran, 2015). Similarly a number of big data technologies have been developed to improve the healthcare system.…”

Section: Related Workmentioning

confidence: 99%

Health data analytics using scalable logistic regression with stochastic gradient descent

Manogaran

Lopez

2018

IJAIP

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As wearable medical sensors continuously generate enormous data, it is difficult to process and analyse. This paper focuses on developing scalable sensor data processing architecture in cloud computing to store and process body sensor data for healthcare applications. Proposed architecture uses big data technologies such as Apache Flume, Apache Pig and Apache HBase to collect and store huge sensor data in the Amazon web service. Apache Mahout implementation of MapReduce-based online stochastic gradient descent algorithm is used in the logistic regression to develop the scalable diagnosis model. Cleveland heart disease database (CHDD) is used to train the logistic regression model. Wearable body sensors are used to get the blood pressure, blood sugar level and heart rate of the patient to predict the heart disease status. Proposed prediction model efficiently classifies the heart disease with the accuracy of training and validation sample is 81.99% and 81.52%, respectively.

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Assessment of Vaccination Strategies Using Fuzzy Multi-criteria Decision Making

Cited by 52 publications

References 17 publications

RETRACTED ARTICLE: A novel method for solving the fully neutrosophic linear programming problems

RETRACTED ARTICLE: A novel method for solving the fully neutrosophic linear programming problems

An intelligent decision support system to prevent and control of dengue

Health data analytics using scalable logistic regression with stochastic gradient descent

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