03. Research
First-author and co-author work across conferences and journals in machine learning, NLP, optimization, and applied AI. Filter by topic below.
Raised the best F1-score for text-based depression detection from 93.24% to 98.04% using sentence transformers and Bayesian-optimized classifier ensembles.
A hybridized music-recommendation system combining deep contrastive learning with collaborative and content-based filtering — a 24.2% performance gain over baselines.
A deep neural network framework for ODE-based dynamic optimization that outperformed metaheuristic and gradient-based methods, setting a new best solution for the ethanol-production problem.
A PINN framework modeling ODE systems of control and state vectors, achieving new best values for the batch-reactor and tubular-reactor problems.
An NLP-based depression-detection model achieving a 93.24% F1-score on a curated dataset of 989 Reddit posts.
A Random Forest rainfall-classification model on INSAT-3D satellite OLR data, improving the Critical Success Index by 30.2% over existing meteorological methods.
An analysis of real-world student use of ChatGPT in computing education, identifying three recurring experiences and best practices for AI-assisted learning.
A historical review of Human Cognitive Behaviour Analysis, mapping the shift from 1960s symbolic reasoning to 1990s data-driven learning.
A Random Forest Proximity-based Oversampling technique that outperformed SMOTE across 18 benchmark datasets.
A PID-control-inspired feature-selection algorithm with adaptive penalty-based selection, outperforming BHFS on 10-plus datasets.
A synthesis of 40-plus studies on AI-driven consumer behaviour across recommendation systems, virtual assistants, and predictive analytics.