article · 2024

Decoding Mobile App Performance with Explainable AI (XAI)

D Archana, Deepthi Sri R, Deva Dharani P, S Haripriya, Vimala Varshini C P

Cited by 0Abstract and details only

Abstract

The document discusses the development of a mobile application software for small, wireless computing devices, emphasizing the importance of a clear strategy for app success. It introduces Interpretable AI to elucidate prediction results on mobile app success factors and assist developers. An Ensemble Machine Learning Algorithm is used, along with Explainable AI models like LIME and SHAP, achieving a prediction accuracy of 96%.

Cite this paper

APA 7

Archana, D., R, D. S., P, D. D., Haripriya, S., & P, V. V. C. (2024). Decoding Mobile App Performance with Explainable AI (XAI). https://doi.org/10.1109/icsseecc61126.2024.10649407

MLA 9

Archana, D, et al. “Decoding Mobile App Performance with Explainable AI (XAI).” vol. 41, 2024, pp. 557–62, https://doi.org/10.1109/icsseecc61126.2024.10649407.

Chicago

Archana, D, Deepthi Sri R, Deva Dharani P, S Haripriya, and Vimala Varshini C P. 2024. “Decoding Mobile App Performance with Explainable AI (XAI).” https://doi.org/10.1109/icsseecc61126.2024.10649407.

Harvard

Archana, D. et al. (2024) ‘Decoding Mobile App Performance with Explainable AI (XAI)’. Available at: https://doi.org/10.1109/icsseecc61126.2024.10649407.

BibTeX

@article{archana2024decoding,
  title = {{Decoding Mobile App Performance with Explainable AI (XAI)}},
  author = {Archana, D and R, Deepthi Sri and P, Deva Dharani and Haripriya, S and P, Vimala Varshini C},
  volume = {41},
  pages = {557--562},
  year = {2024},
  doi = {10.1109/icsseecc61126.2024.10649407}
}

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