Naman Soni
Biographic Data
| ID | 8967947 |
|---|---|
| NAME | Naman Soni |
| GIVEN NAMES | Naman |
| FAMILY NAME | Soni |
| SIGNATURE | SONI N |
| AFFILIATIONS | Noida International University |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Intelligent Museum Management Systems
IMMS is an innovative paradigm in the management, conservation, and mediational experience of cultural heritage organizations. IMMS can help transform museums into adaptable, responsive, knowledge-oriented places through artificial intelligence, Internet of Things, data-driven analytics, and cyber-physical infrastructures. This paper conceptualizes IMMS to be socio-technical ecosystems that integrate digital representations of objects, real time …
Explainable Ai for Style Interpretation in Contemporary Art Using Vision Transformers
This study looks into how Explainable AI (XAI) can be used to figure out style in modern art by using Vision Transformers (ViTs). As the need for interpretability in AI-driven art research has grown, models have had to be made that not only work well but also give clear, understandable reasons for the choices they make. We use ViTs, a cutting-edge deep learning system that is known for being very good at classifying images, to look at and figure …
Fake News Detection and Source Credibility Analysis Using Transformer-Based NLP Models
Because false information is spread so easily on digital platforms, we need strong ways to find fake news and check the reliability of sources. To deal with these problems, this study suggests a complete system that uses advanced transformer-based Natural Language Processing (NLP) models. Using the contextual understanding features of frameworks like BERT, RoBERTa, and XLNet, the system accurately tells whether news stories are true or false whil…
No prominent works on this page.
Explainable Ai for Style Interpretation in Contemporary Art Using Vision Transformers
This study looks into how Explainable AI (XAI) can be used to figure out style in modern art by using Vision Transformers (ViTs). As the need for interpretability in AI-driven art research has grown, models have had to be made that not only work well but also give clear, understandable reasons for the choices they make. We use ViTs, a cutting-edge deep learning system that is known for being very good at classifying images, to look at and figure …
Fake News Detection and Source Credibility Analysis Using Transformer-Based NLP Models
Because false information is spread so easily on digital platforms, we need strong ways to find fake news and check the reliability of sources. To deal with these problems, this study suggests a complete system that uses advanced transformer-based Natural Language Processing (NLP) models. Using the contextual understanding features of frameworks like BERT, RoBERTa, and XLNet, the system accurately tells whether news stories are true or false whil…
Intelligent Museum Management Systems
IMMS is an innovative paradigm in the management, conservation, and mediational experience of cultural heritage organizations. IMMS can help transform museums into adaptable, responsive, knowledge-oriented places through artificial intelligence, Internet of Things, data-driven analytics, and cyber-physical infrastructures. This paper conceptualizes IMMS to be socio-technical ecosystems that integrate digital representations of objects, real time …
3D Surveying and Cultural Heritage (1 work) · Aesthetic Perception and Analysis (1 work) · Big Data and Digital Economy (1 work) · Computational and Text Analysis Methods (1 work) · Conservation Techniques and Studies (1 work) · Contemporary art (1 work) · Credibility (1 work) · Cultural heritage (1 work) · Decision support system (1 work) · Explainable Artificial Intelligence (XAI (1 work)