Trust Model Based Data Fusion in Explainable Artificial Intelligence for Edge Computing Using Secure Sequential Discriminant Auto Encoder with Lightweight Optimization Algorithm

Prabakar, D.; Sundarrajan, M.; Ramachandran, Manikandan; Gupta, Deepak

doi:10.1007/978-3-031-18292-1_9

Studies in Computational Intelligence

2022

DOI: 10.1007/978-3-031-18292-1_9

|View full text |Cite

Trust Model Based Data Fusion in Explainable Artificial Intelligence for Edge Computing Using Secure Sequential Discriminant Auto Encoder with Lightweight Optimization Algorithm

D. Prabakar

M. Sundarrajan²,

Manikandan Ramachandran³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Secure and Reliable Framework for Explainable Artificial Intelligence (XAI) in Smart City Applications

Algarni,

Mishra

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Living in a smart city has many advantages, such as improved waste and water management, access to quality healthcare facilities, effective and safe transportation systems, and personal protection. Explainable AI (XAI) is called a system that is capable of providing explanations for its judgments or predictions. This term describes a model, its expected impacts, and any potential biases that may be present. XAI tools and frameworks can aid in comprehending and trusting the output and outcomes generated by machine-learning algorithms. This study used XAI methods to classify cities based on smart city metrics. The logistic regression method with LIME achieved perfect accuracy, precision, recall, and F1-score, predicting correctly all cases.

show abstract

A Secure and Reliable Framework for Explainable Artificial Intelligence (XAI) in Smart City Applications

Algarni,

Mishra

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Trust Model Based Data Fusion in Explainable Artificial Intelligence for Edge Computing Using Secure Sequential Discriminant Auto Encoder with Lightweight Optimization Algorithm

Cited by 1 publication

References 15 publications

A Secure and Reliable Framework for Explainable Artificial Intelligence (XAI) in Smart City Applications

A Secure and Reliable Framework for Explainable Artificial Intelligence (XAI) in Smart City Applications

Contact Info

Product

Resources

About