Artificial Intelligence‐ (AI‐) Enabled Internet of Things (IoT) for Secure Big Data Processing in Multihoming Networks
Why this work is in the frame
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Bibliographic record
Abstract
The automated techniques enabled with Artificial Neural Networks (ANN), Internet of Things (IoT), and cloud‐based services affect the real‐time analysis and processing of information in a variety of applications. In addition, multihoming is a type of network that combines various types of networks into a single environment while managing a huge amount of data. Nowadays, the big data processing and monitoring in multihoming networks provide less attention while reducing the security risk and efficiency during processing or monitoring the information. The use of AI‐based systems in multihoming big data with IoT‐ and AI‐integrated systems may benefit in various aspects. Although multihoming security issues and their analysis have been well studied by various scientists and researchers; however, not much attention is paid towards big data security processing in multihoming especially using automated techniques and systems. The aim of this paper is to propose an IoT‐based artificial network to process and compute big data processing by ensuring a secure communication multihoming network using the Bayesian Rule (BR) and Levenberg‐Marquardt (LM) algorithms. Further, the efficiency and effect on multihoming information processing using an AI‐assisted mechanism are experimented over various parameters such as classification accuracy, classification time, specificity, sensitivity, ROC, and F ‐measure.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.004 |
| 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