AI-Driven Computational Frameworks: Advancing Edge Intelligence and Smart Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid advancements in Artificial Intelligence (AI) and Edge Computing are transforming modern computing paradigms by enabling real-time processing, low-latency decision-making, and enhanced intelligence in smart systems. This paper presents an AI-driven computational framework that integrates Edge Intelligence (EI) with adaptive deep learning models to optimize data processing and decision-making at the edge. The proposed framework employs federated learning, neuromorphic computing, and reinforcement learning-based optimization to improve efficiency, security, and scalability in distributed edge environments. Key components include lightweight AI models for energy-efficient edge inference, privacy-preserving techniques using homomorphic encryption and blockchain, and self-learning architectures for adaptive real-time analytics. The study evaluates the framework’s performance in diverse applications, including smart healthcare, autonomous vehicles, and industrial IoT, demonstrating significant improvements in computational efficiency, network resilience, and response time compared to traditional cloud-based architectures. Comprehensive simulations and real-world case studies validate the feasibility and effectiveness of the proposed approach, showing a 35% reduction in latency, a 30% increase in energy efficiency, and a 50% improvement in decision accuracy in edge-enabled smart systems. This research highlights the critical role of AI-driven computational frameworks in advancing next-generation intelligent computing, paving the way for autonomous, secure, and efficient edge-based smart environments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it