Investigation of COVID-19 and scientific analysis big data analytics with the help of machine learning

Chang, Victor; Ali, Mohamed Aleem; Hossain, Alamgir

doi:10.1016/b978-0-323-90054-6.00007-6

Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 2022

DOI: 10.1016/b978-0-323-90054-6.00007-6

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Investigation of COVID-19 and scientific analysis big data analytics with the help of machine learning

Victor Chang¹,

Mohamed Aleem Ali²,

Alamgir Hossain³

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2024

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A Local Explainability Technique for Graph Neural Topic Models

Rajendran,

Vidya,

Sanil

et al. 2024

Hum-Cent Intell Syst

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Topic modelling is a Natural Language Processing (NLP) technique that has gained popularity in the recent past. It identifies word co-occurrence patterns inside a document corpus to reveal hidden topics. Graph Neural Topic Model (GNTM) is a topic modelling technique that uses Graph Neural Networks (GNNs) to learn document representations effectively. It provides high-precision documents-topics and topics-words probability distributions. Such models find immense application in many sectors, including healthcare, financial services, and safety-critical systems like autonomous cars. This model is not explainable. As a matter of fact, the user cannot comprehend the underlying decision-making process. The paper introduces a technique to explain the documents-topics probability distributions output of GNTM. The explanation is achieved by building a local explainable model such as a probabilistic Naïve Bayes classifier. The experimental results using various benchmark NLP datasets show a fidelity of 88.39% between the predictions of GNTM and the local explainable model. This similarity implies that the proposed technique can effectively explain the documents-topics probability distribution output of GNTM.

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A Local Explainability Technique for Graph Neural Topic Models

Rajendran,

Vidya,

Sanil

et al. 2024

Hum-Cent Intell Syst

View full text Add to dashboard Cite

show abstract

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Investigation of COVID-19 and scientific analysis big data analytics with the help of machine learning

Cited by 1 publication

References 14 publications

A Local Explainability Technique for Graph Neural Topic Models

A Local Explainability Technique for Graph Neural Topic Models

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