AI / ML Models
Using large datasets, AI and advanced machine-learning solutions were created to directly align to business needs. Coding primarily through prompt engineering enabled rapid iteration, allowing complex ideas to be tested, refined, and deployed quickly in today’s AI-driven technology landscape.
Models & Downloads
Turbine Failure Prediction
Domain: Predictive Maintenance
Model Type: Deep Neural Network
Outcome: Achieved ~93% failure prediction accuracy using turbine sensor data, enabling earlier maintenance interventions that reduce unplanned downtime and operational costs
Medical Diagnosis Assist
Domain: Clinical Decision Support
Model Type: Retrieval-Augmented Generation (RAG)
Outcome: Generates cited medical responses using guideline retrieval and confidence scoring, reducing risk of unreliable AI recommendations in high-stakes clinical decisions.
Retail Revenue Forecast
Domain: Retail Demand Forecasting
Model Type: Ensemble Regression
Outcome: Achieved R² ≈ 0.93 (RMSE ≈ 291), improving revenue forecasting for demand planning, inventory optimization, and financial decision-making.
Seed Identification
Domain: Agricultural Computer Vision
Model Type: CNN with Transfer Learning
Outcome: Achieved 82% classification accuracy across 12 seed species, enabling automated crop identification and scalable agricultural quality control.
Visa Approval Prediction
Domain: Immigration Risk Analysis
Model Type: Imbalanced Ensemble Classification
Outcome: Achieved 0.77 ROC-AUC and 0.76 F1, improving consistency and speed of visa risk assessment and application review.
AI / ML Models
Using large datasets, AI and advanced machine-learning solutions were created to directly align to business needs. Coding primarily through prompt engineering enabled rapid iteration, allowing complex ideas to be tested, refined, and deployed quickly in today’s AI-driven technology landscape.
Models & Downloads
Turbine Failure Prediction
Domain: Predictive Maintenance
Model Type: Deep Neural Network
Outcome: Achieved ~93% failure prediction accuracy using turbine sensor data, enabling earlier maintenance interventions that reduce unplanned downtime and operational costs
Medical Diagnosis Assist
Domain: Clinical Decision Support
Model Type: Retrieval-Augmented Generation (RAG)
Outcome: Generates cited medical responses using guideline retrieval and confidence scoring, reducing risk of unreliable AI recommendations in high-stakes clinical decisions.
Retail Revenue Forecast
Domain: Retail Demand Forecasting
Model Type: Ensemble Regression
Outcome: Achieved R² ≈ 0.93 (RMSE ≈ 291), improving revenue forecasting for demand planning, inventory optimization, and financial decision-making.
Seed Identification
Domain: Agricultural Computer Vision
Model Type: CNN with Transfer Learning
Outcome: Achieved 82% classification accuracy across 12 seed species, enabling automated crop identification and scalable agricultural quality control.
Visa Approval Prediction
Domain: Immigration Risk Analysis
Model Type: Imbalanced Ensemble Classification
Outcome: Achieved 0.77 ROC-AUC and 0.76 F1, improving consistency and speed of visa risk assessment and application review.
AI / ML Models
Using large datasets, AI and advanced machine-learning solutions were created to directly align to business needs. Coding primarily through prompt engineering enabled rapid iteration, allowing complex ideas to be tested, refined, and deployed quickly in today’s AI-driven technology landscape.
Models & Downloads
Turbine Failure Prediction
Domain: Predictive Maintenance
Model Type: Deep Neural Network
Outcome: Achieved ~93% failure prediction accuracy using turbine sensor data, enabling earlier maintenance interventions that reduce unplanned downtime and operational costs
Medical Diagnosis Assist
Domain: Clinical Decision Support
Model Type: Retrieval-Augmented Generation (RAG)
Outcome: Generates cited medical responses using guideline retrieval and confidence scoring, reducing risk of unreliable AI recommendations in high-stakes clinical decisions.
Retail Revenue Forecast
Domain: Retail Demand Forecasting
Model Type: Ensemble Regression
Outcome: Achieved R² ≈ 0.93 (RMSE ≈ 291), improving revenue forecasting for demand planning, inventory optimization, and financial decision-making.
Seed Identification
Domain: Agricultural Computer Vision
Model Type: CNN with Transfer Learning
Outcome: Achieved 82% classification accuracy across 12 seed species, enabling automated crop identification and scalable agricultural quality control.
Visa Approval Prediction
Domain: Immigration Risk Analysis
Model Type: Imbalanced Ensemble Classification
Outcome: Achieved 0.77 ROC-AUC and 0.76 F1, improving consistency and speed of visa risk assessment and application review.