Scientific Contributions

The Hallucination Problem: Large language models excel at generating plausible responses but often produce factually incorrect answers in high-stakes financial analysis, leading to regulatory violations and financial losses.

Our Solution: We introduce Bayesian RAG, a principled probabilistic framework that integrates epistemic uncertainty quantification directly into retrieval using Monte Carlo Dropout. Our Bayesian scoring function S_i = μ_i−λ·σ_i balances semantic relevance against uncertainty, enabling risk-calibrated AI decisions for financial services.

The "Black Box" Problem: Conventional autonomous systems are deterministic and they don't know when they are confused. This leads to dangerous overconfidence in fog, rain, or novel traffic scenarios.

Our Solution: We introduce a Bayesian Neural Network (BNN) framework that quantifies uncertainty. By learning probability distributions over weights, our system can express "doubt," enabling safer, conservative decision-making when sensor data is noisy or ambiguous.

Online scams continue to escalate in scale and sophistication, surpassing traditional detection systems. This study revisits the Naïve Bayes algorithm, introducing hybrid architectures that integrate it with deep learning and ensemble methods. Our empirical results reveal that a strategically optimized Naïve Bayes model can deliver competitive accuracy while maintaining transparency, which is key for real-world fraud prevention.

Optimization is the silent engine of AI. This study rigorously benchmarks 6 major algorithms (including Adam, RMSprop, and SGD) against the notoriously deceptive Rosenbrock "Banana" Function. We uncover critical insights into convergence speed, stability, and gradient behavior, identifying the ultimate "Convergence Powerhouse" for complex non-convex landscapes.

Traditional AI models often lack mechanisms to address uncertainty, leading to overconfident predictions that may not be reliable in high-stakes clinical contexts. This study explores the efficacy of Bayesian Neural Networks (BNNs) in enhancing predictive modeling for healthcare. By leveraging a probabilistic framework, we demonstrate how BNNs provide not only enhanced predictive accuracy but also uncertainty quantification, a critical factor in clinical decision making.