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Vishwanath Akuthota - Founder

Vishwanath Akuthota

Founder

Vishwanath Akuthota is a distinguished deep-tech entrepreneur and AI architect with over 1.5 decades of experience in Artificial Intelligence, Machine Learning, and Generative AI. He has been at the forefront of building world-class AI products and Large Language Model (LLM) applications.

His work is characterized by a mission to develop responsible and trustworthy AI systems that align with ethical principles and industry standards.

"Creating valuable products requires an engineering mindset combined with the precision of a mathematician. Our mission is to build responsible, ethical, and trustworthy AI that empowers humanity."

Authored Books

IEEE Publication & Research

MentalLLM: A Transformer-Based Large Language Model Framework for Depression Detection.

Document ID: 11525902

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Microplastics Detection Using Deep Learning Ensemble with Vision Language Models.

Document ID: 11525939

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A Multi-Agent Quantum Chain of Thought Reasoning and Accuracy Accelerators Framework.

Document ID: 11525967

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Vulnerability Detection and Monitoring Using LLM

Document ID: 10456393

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A Multi-Agent Garage Service Search and Recommendation with Hybrid MLs and LLMs

Document ID: 10940937

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Hybrid Q-Learning with VLMs Reasoning Features.

Document ID: 11040757

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Comparative Analysis of Diverse Architectures for Accurate Blood Cancer Cell Classification.

Document ID: 10497341

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RAG-Enhanced Multi-Model Ensemble for Automated Vulnerability Detection Using SLMs.

Document ID: 11429262

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Multi-Agent Phishing Detection And Deletion via Small VLM and LLM Reasoning.

Document ID: 11429303

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Quantum-Enhanced Tax Revenue via A-Challan: ML, LLMs, and QML Approaches.

Document ID: 11526131

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Pose Detection: Integrating Machine Learning with Large Vision Models.

Document ID: 11211028

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Financial Voucher Analysis with LVMs and Financial LLMs.

Document ID: 11118347

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Hybrid ML-SLM RAG System for Large Technical PDFs .

Document ID: 11118759

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Multi-Vision LVMs Model Ensemble for Gold Jewelry Authenticity Verification.

Document ID: 11118918

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Media & Recognition

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