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Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

Author

Mafaz Mohammed Nadherssa

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3 papers indexed here

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Open access Oct 2026

Interpretable Lifestyle-Only Diabetes Screening: A Clinically Grounded XAI Pipeline

This paper presents an explainable AI pipeline for diabetes screening that learns from non-laboratory, self-reported health indicators and then provides patient-specific, actionable risk rationales clinicians can audit and communicate. A deep neural classifier, gradient boosting, and a regularized logistic baseline are...

Mafaz Mohammed Nadherssa · 0 citations
Open access Oct 2026

Trustworthy Contracts AI: Explainable Multi-Label NLP with Legal-BERT, T5, and SHAP

An end-to-end explainable AI framework for contract analytics that combines multi-label clause detection with Legal-BERT, abstractive summarization via T5, and layered interpretability using SHAP, LIME, and attention visualization to generate lawyer-readable rationales is presented.

Mafaz Mohammed Nadherssa · 0 citations
Open access Aug 2026

Robust Monotone Ratings: A Bayesian, Linear-Time Scheme for Massive Ranked Competitions

This paper introduces a computer science approach to large-scale skill estimation that unifies probabilistic modeling with algorithmic efficiency. We present a Bayesian rating framework that models skills, performances, and ranked evidence, deriving closed-form MAP updates and a pseudodiffusion mechanism for temporal s...

Mafaz Mohammed Nadherssa · 0 citations

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