Fuzzy min–max neural networks: a bibliometric and social network analysis

Kenger, Ömer Nedim; Özceylan, Eren

doi:10.1007/s00521-023-08267-9

Cited by 7 publications

(2 citation statements)

References 164 publications

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“…45 Previous studies demonstrated the effectiveness of the gradient boosting model in predicting the death of COVID-19 patients. 46 By and large, the established gradient boosting classification model proved to be a valuable tool for the binary classification of different urine samples.…”

Section: Analysis Of Urinementioning

confidence: 99%

Desorption Separation Ionization Mass Spectrometry (DSI-MS) for Rapid Analysis of COVID-19

Wang,

Ma,

et al. 2024

Anal. Chem.

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During the coronavirus disease 2019 (COVID-19) pandemic, which has witnessed over 772 million confirmed cases and over 6 million deaths globally, the outbreak of COVID-19 has emerged as a significant medical challenge affecting both affluent and impoverished nations. Therefore, there is an urgent need to explore the disease mechanism and to implement rapid detection methods. To address this, we employed the desorption separation ionization (DSI) device in conjunction with a mass spectrometer for the efficient detection and screening of COVID-19 urine samples. The study encompassed patients with COVID-19, healthy controls (HC), and patients with other types of pneumonia (OP) to evaluate their urine metabolomic profiles. Subsequently, we identified the differentially expressed metabolites in the COVID-19 patients and recognized amino acid metabolism as the predominant metabolic pathway involved. Furthermore, multiple established machine learning algorithms validated the exceptional performance of the metabolites in discriminating the COVID-19 group from healthy subjects, with an area under the curve of 0.932 in the blind test set. This study collectively suggests that the small-molecule metabolites detected from urine using the DSI device allow for rapid screening of COVID-19, taking just three minutes per sample. This approach has the potential to expand our understanding of the pathophysiological mechanisms of COVID-19 and offers a way to rapidly screen patients with COVID-19 through the utilization of machine learning algorithms.

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Section: Analysis Of Urinementioning

confidence: 99%

Desorption Separation Ionization Mass Spectrometry (DSI-MS) for Rapid Analysis of COVID-19

Wang,

Ma,

et al. 2024

Anal. Chem.

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“…2) Hyperbox-brain toolbox: The Hyperbox-brain toolbox [13] is an open-source Python library implementing the fuzzy min-max machine learning algorithms. The toolbox currently supports 18 fuzzy min-max models, encompassing the original and improved versions of the Fuzzy min-max neural network (FMNN) and General fuzzy min-max neural network (GFMMNN) models [30], [31]. Among these 18 algorithms, 3 are mixed-data learners, and 15 are designed for numerical data.…”

Section: Evolving Neuro-fuzzy Systemsmentioning

confidence: 99%

Evolving Neuro-Fuzzy Systems for Real-Time Data Processing in Internet of Medical Things

Nyirenda,

Chilipa

2024

Preprint

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Evolving neuro-fuzzy systems (ENFS) have shown great promise in analysis of streaming data, integrating artificial neural networks and fuzzy logic to create self-learning, data-driven models suitable for online (real-time) applications. In healthcare, ENFS have the potential to transform the Internet of Medical Things (IoMT) by enabling real-time data processing, decision support, and timely emergency response. In this study six evolving fuzzy min-max neural network models have been implemented using Python and Kafka, and evaluated on the MIT-BIH Arrhythmia dataset comprising 109,446 samples corresponding to shapes of heartbeats for the normal case and the cases affected by different arrhythmias and myocardial infarction. To achieve online continual learning, the prequential test-then-train method has been used to sequentially classify the data instances from scratch and update the system structure along with the arrival of new data samples. The Enhanced Fuzzy Min-Max Neural Network (EFMMNN) achieved the highest accuracy of 98.11%. Future work will explore deploying these evolving fuzzy systems in real-world healthcare settings using edge-fog computing.

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Uncertain 4D-transportation problem with maximum profit and minimum carbon emission

Samanta,

Chakraborty,

Jana

2023

J Anal

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Fuzzy min–max neural networks: a bibliometric and social network analysis

Cited by 7 publications

References 164 publications

Desorption Separation Ionization Mass Spectrometry (DSI-MS) for Rapid Analysis of COVID-19

Desorption Separation Ionization Mass Spectrometry (DSI-MS) for Rapid Analysis of COVID-19

Evolving Neuro-Fuzzy Systems for Real-Time Data Processing in Internet of Medical Things

Uncertain 4D-transportation problem with maximum profit and minimum carbon emission

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