Skip to content
Log in
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

Dewa Ayu Defina Audrey Nathania

View on Semantic Scholar

4 papers indexed here

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

Automated Risk Assessment Framework for Real World Assets (RWA) Based on ISO 31000: A Smartcontract Governance Perspective

Blockchain technology has advanced and facilitated the emergence of Real World Asset (RWA) tokenization, which includes the digital representation of physical assets via smart contracts. RWAs have been rapidly adopted in Indonesia, especially among the younger generation. They are starting to use digital assets such as...

Jessicania Windari, Adam Fahsyah Nurzaman, Setiawan Joddy et al. · 0 citations
Conference Aug 2026

Low-Rank Adaptation Provides a Parameter-Efficient Trade-Off for Low-Data Medical Imaging

Adapting large pretrained Vision Transformers (ViTs) to medical imaging is challenging because labeled clinical data are limited and full fine-tuning is computationally expensive. This paper presents a multi-seed, multi-modality study of parameter-efficient fine-tuning (PEFT) for low-data binary medical image classific...

Alif Akbar Hafiz, Collyne Collyne, Dzaky Rizha Anargya et al. · 0 citations
Conference Aug 2026

Deep Learning-Based Detection of Disease-Associated DNA motifs in Whole Exome Sequencing Data for Inherited Genetic Disorders

This study presents a deep learning framework for detecting disease-associated DNA sequence patterns in Whole Exome Sequencing (WES) data related to inherited genetic disorders. The proposed workflow combines sequence extraction, one-hot encoding, class balancing using the Synthetic Minority Over-sampling Technique (SM...

Elisha Anastasia Irawan, Jason Alexander, Ahmad Fauzan et al. · 0 citations
Conference Aug 2026

A Dual Explainability Framework for Cardiovascular Disease Prediction Using SHAP and LIME with Tree-Based Machine Learning Models

Cardiovascular disease (CVD) remains one of the main cause of mortality worldwide and although ensemble learning models such as Random Forest, XGBoost, and LightGBM offers strong predictive performance, their black box nature limits adoption in clinical settings. This study proposes a dual explainability framework that...

Cecilia Euginia, Charles Charles, David et al. · 0 citations

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