Skip to content
Log in
Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Deepanshu Yadav

h-index 6 View on Semantic Scholar

We have 4 of 22 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Conference Aug 2026

Machine-DM: A Machine-Based Decision-Maker for Benchmarking Interactive MCDM Procedures

Interactive multi-criterion decision-making (iM-CDM) procedures typically require a human decision-maker (DM) to iteratively compute a preferred Pareto-optimal solution by incorporating her/his preference information. Many iMCDM procedures involve classifying the objectives according to whether they need to be improved...

Deepanshu Yadav, Kalyanmoy Deb · 0 citations
Open access Aug 2026

A Machine-based Decision-maker Toward Benchmarking Interactive Multi-criterion Decision-making Procedures

This paper proposes a machine-based decision-maker (Machine-DM) in terms of pre-trained machine learning (ML) models to provide decision-making information and describes the development process in detail and demonstrates its working through a specific iMCDM procedure – NIMBUS.

Deepanshu Yadav, Kalyanmoy Deb · 0 citations
Book Open access Jul 2026

pMachine-DM: A Stochastic Machine Learning-Based Decision-Maker for Interactive Multi-Criterion Decision-Making

This paper proposes a stochastic machine learning-based decision-maker: pMachine-DM, which emulates human preference articulation using two stochastic ANNs, enabling systematic and reproducible benchmarking without involving human DMs.

Deepanshu Yadav, Kalyanmoy Deb · 1 citation
Book Open access Jul 2026

Benchmarking Interactive Multi-Criterion Decision-Making Procedures Following Machine Learning-Based Decision-Maker: Benchmarking Interactive MCDM Procedures

A Machine-based Decision Maker that replaces human DMs with pre-trained machine learning models capable of performing the key iMCDM tasks automatically and a Bench-iMCDM framework for benchmarking iMCDM procedures is proposed.

Deepanshu Yadav, Kalyanmoy Deb · 2 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.