A new fuzzy learning vector quantization method for classification problems based on a granular approach

Amezcua, Jonathan; Melín, Patricia

doi:10.1007/s41066-018-0120-7

Cited by 15 publications

(2 citation statements)

References 46 publications

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“…The main emphasis is to develop a novel fuzzy approach using ridge regression for time series prediction. In yet another novel work [35] a new approach Fuzzy Learning Vector Quantization is developed which is the amalgamation of Learning Vector Quantization Neural Network and Fuzzy Colson et al 2020 [26] Chloroquine and hydroxychloroquine 9.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 prediction using AI analytics for South Korea

Sinha

Rathi

2021

Appl Intell

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The severe spread of the COVID-19 pandemic has created a situation of public health emergency and global awareness. In our research, we analyzed the demographical factors affecting the global pandemic spread along with the features that lead to death due to the infection. Modeling results stipulate that the mortality rate increase as the age increase and it is found that most of the death cases belong to the age group 60-80. Cluster-based analysis of age groups is also conducted to analyze the maximum targeted age-groups. An association between positive COVID-19 cases and deceased cases are also presented, with the impact on male and female death cases due to corona. Additionally, we have also presented an artificial intelligence-based statistical approach to predict the survival chances of corona infected people in South Korea with the analysis of the impact on the exploratory factors, including age-groups, gender, temporal evolution, etc. To analyze the coronavirus cases, we applied machine learning with hyperparameters tuning and deep learning models with an autoencoder-based approach for estimating the influence of the disparate features on the spread of the disease and predict the survival possibilities of the quarantined patients in isolation. The model calibrated in the study is based on positive corona infection cases and presents the analysis over different aspects that proven to be impactful to analyze the temporal trends in the current situation along with the exploration of deceased cases due to coronavirus. Analysis delineates key points in the outbreak spreading, indicating that the models driven by machine intelligence and deep learning can be effective in providing a quantitative view of the epidemical outbreak.

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Section: Related Workmentioning

confidence: 99%

COVID-19 prediction using AI analytics for South Korea

Sinha

Rathi

2021

Appl Intell

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“…Several applications have used the FLVQ method for classification. Amezcua and Melin [24] said that the advantage of the FLVQ method is that it can solve complex problems. Damayanti and Wediningsih [25] also proved the performance of the FLVQ method.…”

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confidence: 99%

Success factors for citizen-based government decision making using K-means fuzzy learning vector quantization

Utomo,

Adi,

Nurhayati

2023

IJEECS

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<span>The Indonesian government often needs assistance in making citizen-based decisions, for example selecting work program plans. Residents have their criteria in the forum to choose a work program plan. This study proposes the K-means fuzzy learning vector quantization (FLVQ) methods to select citizen-based government decision-making criteria. The K-means FLVQ method has never been used to assist government decision-making. However, citizen criteria can be a success factor for government decision-making. The selection of criteria begins with data collection from forum participants. The results of data collection get 11 criteria. Then, the K-means FLVQ method carries out labeling and classification. The addition of the K-means process in the selection criteria can provide optimal results. Citizens can give assessment criteria freely. Then the assessment of citizens is classified by FLVQ. The classification results obtained seven criteria, namely: i) urgency, ii) sustainability, iii) priority, vi) usability, v) prosperity, vi) comfortability, and vii) artistic. Governments can use these criteria to make decisions about planned work programs. The criteria selection algorithm was also evaluated using the confusion matrix method with an accuracy of 88% and an error of 12%.</span>

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