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Agrawal, Rashmi
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Electrical & Electronics Engineering

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Understanding explainable artificial intelligence techniques: a comparative analysis for practical application Bhatnagar, Shweta; Agrawal, Rashmi
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8378

Abstract

Explainable artificial intelligence (XAI) uses artificial intelligence (AI) tools and techniques to build interpretability in black-box algorithms. XAI methods are classified based on their purpose (pre-model, in-model, and post-model), scope (local or global), and usability (model-agnostic and model-specific). XAI methods and techniques were summarized in this paper with real-life examples of XAI applications. Local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) methods were applied to the moral dataset to compare the performance outcomes of these two methods. Through this study, it was found that XAI algorithms can be custom-built for enhanced model-specific explanations. There are several limitations to using only one method of XAI and a combination of techniques gives complete insight for all stakeholders.
MVC in machine learning: a decade of algorithmic advances, challenges, and applications–a systematic review Kumar, Pankaj; Agrawal, Rashmi
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.11137

Abstract

This systematic review evaluates the developments in multi-view clustering (MVC), its challenges, and applications from 2009 to 2024 and synthesizes 157 studies selected according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines. MVC overcomes the shortcomings of the traditional single-view approaches by using complementary information provided by heterogeneous data sources. We used a strict search strategy in the ACM Digital Library, IEEE Xplore, and Scopus, and then carefully examined the quality of the found articles. The significant results suggest that the MVC research has grown explosively, with China as the major contributor and IEEE/Elsevier as the leading publishers. Developments in algorithms include deep learning, graph-based models, and factorization. Ongoing issues include managing incomplete views, scalability, successful fusion strategies, and interpretability. The review points out the wide range of applications of MVC in various areas, including bioinformatics, social network analysis, and multimedia. Future research must create adaptive frameworks, improve the interpretability of models, and develop strong evaluation measures, thus unlocking the full potential of MVC in real-life data applications.
Co-Authors Bhatnagar, Shweta Kumar, Pankaj