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Deep behavioral and machine learning analysis explaining why mobile users systematically report lower satisfaction with AI systems. Includes SHAP explainability, cognitive load modeling, device-context effects, interaction metadata analysis, and end-to-end reproducible research code and visuals.
A deep exploration of how human psychology shapes fraud behavior and how those patterns become measurable signals in transaction data. This article reveals the behavioral, cognitive, and economic forces behind fraud, explaining how ML models detect deviations, anomalies, and intent hidden within financial transactions.
A deep exploration of loyalty as a multi-dimensional behavioral system shaped by intent, habit, and sensitivity. This article introduces a geometric framework for modeling customer behavior, predicting churn trajectories, and designing ML systems that understand loyalty as a dynamic state, not a metric.
EHTYGA is an open-source, human-centered content tool designed to synthesize persuasive messaging while critically filtering for cognitive manipulation. It algorithmically identifies rhetorical techniques—scarcity, urgency, fear—that often erode trust, nudging creators toward ethically sound communication.
The Behavioral Economics Simulator models the decisions of agents (consumers/investors) influenced by psychological biases in a dynamic, simplified market environment. It was programmed using Eclipse IDE for Java Developers codespace and uses AI bias modeling.
This repository explores the activation patterns of A2 noradrenergic neurons in fear-conditioned rats, using statistical analyses like t-tests and linear regression in R. It focuses on the differences in dopamine β-hydroxylase (DbH) neuron activation between various environmental conditions.