Date Approved
6-23-2026
Embargo Period
6-23-2026
Document Type
Dissertation
Degree Name
Ph.D. Electrical and Computer Engineering
Department
Electrical and Computer Engineering
College
Henry M. Rowan College of Engineering
Advisor
Nidhal Bouaynaya, Ph.D.
Committee Member 1
Umashanger Thayasivam, Ph.D.
Committee Member 2
Ying Tang, Ph.D.
Committee Member 3
Ravi Ramachandran, Ph.D.
Committee Member 4
Srini Ramaswamy, Ph.D.
Abstract
Modern machine learning systems can perform well on benchmarks yet fail in ways that are difficult for users to anticipate, interpret, or manage. This dissertation treats reliability as more than accuracy: a reliable system performs when it can, signals when it should not be trusted, and supports abstention, fallback, escalation, or human review. Unlike robustness, which emphasizes performance under shift, corruption, adversarial perturbation, or drift, reliability also requires recognizing limited competence, preserving useful semantic structure when fine-grained certainty is unjustified, and exposing mechanisms that make failures understandable. The dissertation develops this view through four contributions. Uncertainty-aware decision heads provide portable trust signals for classifiers and detectors. A modular perception-to-risk pipeline combines detection, tracking, intention estimation, and interpretable risk fusion for aerial threat prioritization. Hierarchy-aware classification enables semantic fallback by returning meaningful parent concepts when an exact leaf class is unavailable, unsupported, or withheld. Training-time reliability control is framed as learning to reallocate effort toward difficult or under-served groups. Together, these studies argue that trust improves when systems can state when they should not be trusted, explain why decisions are made, and adapt learning where standard optimization is weakest, yielding a unified view of reliability across model architecture, algorithm design, human interpretability, and decision support.
Recommended Citation
Naddeo, Kyle, "A Holistic Framework for Enhancing Machine Learning Reliability: From Model Architecture and Algorithm Design to Human Interpretability" (2026). Theses and Dissertations. 3545.
https://rdw.rowan.edu/etd/3545
