My Publications
Explore my academic research contributions in AI, machine learning, and emerging technologies. I focus on privacy-preserving AI, federated learning, and intelligent systems.
An Explainable Counterfactual and Agent-Assisted Machine Learning Framework for Emotion-Aware Digital Mental Well-Being Decision Support
A Random Forest reads seven social media usage habits to tell apart seven emotional states at 0.98 accuracy, explains each prediction with LIME and SHAP, and uses DiCE counterfactuals and a rule-based agent to suggest small changes toward a positive or stable state.
Rule-based BloodCell Interpretable and Actionable Hematological Classification using ConvNeXt-Tiny
A ConvNeXt-Tiny model classifies eight peripheral blood cell types at 0.98 accuracy, paired with LIME visual explanations and a rule-based module that turns each prediction into non-diagnostic clinical guidance.
Federated Deep Learning in Intelligent Urban Ecosystems: A Systematic Review of Advancements and Applications in Smart Cities, Homes, Buildings, and Healthcare Systems
A systematic review of privacy-preserving federated deep learning (2018-2025), mapping how distributed training keeps raw data on-device across smart cities, buildings and healthcare, and comparing the privacy mechanisms that make it trustworthy.
Interested in Research Collaboration?
I'm always open to collaborating on research projects in AI, machine learning, and emerging technologies. Let's work together to advance the field.
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