Scientific Contributions
Pioneering the future of AI through rigorous research and validation.
Architects of Intelligence
Pioneering the future of AI through rigorous research and validation.
Architects of Intelligence
Advancing AI through rigorous scientific research. Our research focuses on Bayesian neural networks, healthcare AI, autonomous systems, and optimization techniques. We collaborate with leading universities and research institutions worldwide.
The "Black Box" Problem: Conventional autonomous systems are deterministic—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.
Tested in dense traffic with dynamic agents. The BNN successfully identified high-uncertainty zones (e.g., erratic pedestrians), adjusting trajectory to maintain safety margins where standard models failed.
Evaluated under simulated fog, rain, and wind. While visual degradation caused deterministic models to crash, the BNN expanded its uncertainty bounds, triggering cautious navigation protocols.
Result: Substantial improvements in trajectory accuracy and collision avoidance. The probabilistic output provides a "trust score" for every decision, essential for regulatory compliance.
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—key for real-world fraud prevention.
Analyzed 18,000+ emails. The hybrid stacking model achieved an AUC of 0.996, effectively distinguishing subtle phishing patterns from legitimate communication.
Tackled extreme class imbalance (0.17% fraud) in 284k credit card transactions. The hybrid model maintained high recall with minimal false positives.
Integrates Naïve Bayes, Random Forest, SVM, and MLP. This ensemble approach outperforms standalone models in accuracy, generalization, and interpretability.
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.
Navigating the Rosenbrock Function: A treacherous non-convex landscape with steep ridges and a narrow, curved valley that traps traditional algorithms.
GD vs. Adaptive Methods: We visualized optimization paths and gradient norms. While Momentum smoothed trajectories, adaptive methods reigned supreme.
Adam emerged as the "Convergence Powerhouse," effectively handling varying curvature to reach the global minimum with superior speed and stability.
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.
Personalized treatment strategies for diabetes management. BNNs outperformed traditional models in predicting HbA1c changes, offering crucial uncertainty estimates for better patient care.
Early detection of Alzheimer's disease using clinical and genetic data. The BNN model provided reliable predictions with confidence intervals, reducing the risk of false diagnoses.
Predictive modeling for HbA1c levels. The study showed that BNNs deliver superior predictive accuracy and well-calibrated uncertainty estimates compared to standard regression models.
IEEE Access
View Paper →Machine Learning and Knowledge Extraction
View Paper →Frontiers in Built Environment
View Paper →Open Journal of Optimization
View Paper →