Skip to content
Log in
Sponsored AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI

Author

S. Ramasubbareddy

h-index 22 View on Semantic Scholar

We have 4 of 120 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.

Open access Sep 2026

A unified multi-modal latent diffusion framework with modality-dropout training

A unified latent diffusion framework that addresses both limitations simultaneously, indicating that adaptive modality weighting outperforms fixed conditioning strategies and that modality-dropout training provides an effective partial-conditioning robustness mechanism.

S. Remya, Manu J. Pillai, Laveena Herman et al. · 0 citations
#generative ai Open access Sep 2026

An adaptive multi-objective differential evolution algorithm to fine-tune the constructive cost model constants for software effort estimation

This paper includes new mutation techniques with a Pareto-based differential evolution algorithm, leveraging Generative AI to increase candidate solution diversity and a non-dominated sorting technique to lower the computational complexity involved in Pareto dominance.

Sunil Kumar Gouda, S. Ramasubbareddy, Kumar Surjeet Chaudhury et al. · 0 citations
Open access Jul 2026

A deep learning perception framework for farm digital twins: weed species classification, semantic segmentation, and gradient-based visual interpretability

This paper presents a tri-component deep learning framework designed explicitly as a perception and interpretability layer for Farm Digital Twin architectures, evaluated on a five-species balanced subset of the Moving Fields Weed Dataset (MFWD)—a publicly available benchmark of 94K high-resolution images of 28 weed spe...

Anusree Manoj, Aiswarya S. Kumar, S. Remya et al. · 0 citations
Open access Jul 2026

Semantic de-identification of burned-in PHI in DICOM medical images: a deep learning–NLP pipeline validated on clinical and phantom TMM datasets

A semantic de-identification pipeline integrating YOLOv11n-based text detection, domain-optimized EasyOCR, and a hybrid natural language processing (NLP) classification module combining regular expressions, keyword matching, and named entity recognition is proposed, confirming that the pipeline preserves quantitative p...

Remya Sethulekshmi, Manu J. Pillai, Nihal Ahammed 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.