Predictive maintenance as an analytics problem: sensor and telemetry pipelines, failure-mode modelling, and the shift from scheduled servicing to condition-based intervention in Manufacturing 4.0 environments.
Recommended citation: G. Parab. (2024). "Manufacturing 4.0: AI-Driven Analytics for Predictive Maintenance." International Journal for Multidisciplinary Research. 6(6).
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A framework for agentic AI in enterprise analytics: autonomous agents that plan, query, and act against governed data platforms rather than returning static reports β covering architecture, orchestration, and the guardrails autonomy requires.
Recommended citation: G. Parab. (2024). "Agentic AI in Data Analytics: Transforming Autonomous Insights and Decision-Making." International Journal of Scientific Research in Computer Science, Engineering and Information Technology. 10(6), 1752β1759.
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Applied AI across the healthcare analytics stack β predictive risk stratification, clinical decision support, and operational forecasting β with attention to the data quality, interoperability, and regulatory constraints specific to the domain.
Recommended citation: G. Parab. (2024). "AI in Healthcare Analytics: Transforming Patient Care and Operational Efficiency." International Journal of Computer Engineering and Technology. 15(6), 1539β1546.
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How machine learning and real-time behavioural data reshape retail personalization β from recommendation and dynamic pricing to inventory alignment β and what the underlying analytics architecture has to support.
Recommended citation: G. Parab. (2024). "AI-Driven Personalization in Retail Analytics: Transforming Customer Experiences." International Journal of Research in Computer Applications and Information Technology. 7(2), 2387β2396.
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A simulation-based queueing framework that assigns passengers from different risk classes to the often-underutilized selectee lane in real time, raising the probability of correctly identifying high-risk passengers by more than 4% over then-current practice with minimal added processing time.
Recommended citation: X. Nie, G. Parab, R. Batta, and L. Lin. (2012). "Simulation-Based Selectee Lane Queueing Design for Passenger Checkpoint Screening." European Journal of Operational Research. 219(1), 146β155.
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A 25-page protocol and four plain-text, model-agnostic skills β task-contract, state-compiler, adversarial-verifier, failure-router β for the four failure modes that make coding agents unreliable: no bounded spec, lost decisions, unverified completion claims, and blind retry loops. Every empirical claim, including the ones that came out lower than expected, is reported as measured: a failure-classifier accuracy that drops from 88.0% on a self-authored corpus to 45.0% on an independent one, and a live-agent pilot that surfaced a real, previously-unknown bug in the enforcement hook it was testing.
Recommended citation: G. Parab. (2026). "STRAP: A Structured Task and Recovery Agent Protocol for Reliable AI Agent Harnesses." Technical report. github.com/gparab/STRAP.
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Formerly Absolute Self-Governance β a multi-agent orchestrator whose own pre-registered analysis found no significant benefit from its roster-voting mechanism (+3.9 points, 95% CI [β2.7, +10.6], p = 0.247). Renamed and reduced to what the evidence supported: Escalation-Gated Retry, a single coding-assistant skill with no server or API key, plus the full telemetry the paper recomputes from.
Recommended citation: G. Parab. (2026). "Earned Consensus." Technical report. github.com/gparab/earned-consensus.
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M.S. thesis, Department of Industrial and Systems Engineering, University at Buffalo (SUNY). Simulation and stochastic modelling of airport security screening, and the basis for the subsequent European Journal of Operational Research paper.
Recommended citation: G. Parab. (2011). "Optimization of an Airport Security Selectee Lane System Using Simulation." M.S. Thesis, State University of New York at Buffalo. 56 pages. ProQuest 1488938.
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