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data-advanced-machine-learning-techniques-for-building-performance-simulation---a-comparative-analys

Dataset related to the manuscript "Advanced machine learning techniques for building performance simulation - A comparative analysis"

discovering-novel-prognostic-biomarkers-of-hepatocellular-carcinoma-using-xai

A multi-model XAI and a probabilistic causal inference framework to identify and validate key genetic biomarkers for hepatocellular carcinoma prognosis.

em_xgb

Energy modeling of buildings using XGBoost

explainable-machine-learning-compressive-behavior-high-performance-concrete

fault-detection-hvac

Python Source code and datasets used in my doctoral dissertation - Detection of faults in HVAC systems using tree-based ensemble models and dynamic thresholds

ml-construction-cost-prediction

In this work, we compared the predictive capabilities of six different machine learning algorithms - linear regression, random forest, extreme gradient boosting, light gradient boosting, and natural gradient boosting - and demonstrated that a hybrid light gradient boosting and natural gradient boosting model provides the most desirable construction cost estimates in terms of the accuracy metrics, uncertainty estimates, and training speed.

prediction-concrete-compressive-strength

reliable-preliminary-cost-estimation-through-hybridization-of-natural-and-extreme-gradient-boosting

statistical_model_validation

This repository contains original data files for energy modeling using Support Vector Machine Regression. This predicted data from the model was compared with the actual data so as to analyze the limitations and advantages of various statistical performance metrics. The link to the published paper will be provided later. The source code for the project will be uploaded later.