AI-Powered Robotic Cloud Automation-Based Dynamic Task Allocation and Process Optimization Using E-WFO and C <sup>2</sup> DRBM
Bibliographic record
Abstract
With rapid technological advancements and interconnected digital ecosystems, integrating robotic cloud automation and generative artificial intelligence is the potential factor that improves industry sustainability. Yet, none of the prevailing methodologies focused on dynamic task allocation to robots regarding their current workload, battery status and location. To address this research gap, a well-ordered framework named AI-powered robotic cloud automation-based dynamic task allocation and process optimization using E-WFO and C2DRBM is proposed in this paper. Firstly, the robots are registered in the cloud applications using the robot ID and location. Afterwards, the tasks waited in the queue, followed by LissCC-based task security. Furthermore, the features are extracted from both the robot and the task. Subsequently, the task assignment is done via E-WFO. In the pre-trained cloud model, primarily, the features are extracted from the robots. Next, the class labelling uses H-Fuzzy, followed by C2DRBM-based load prediction. After load prediction, the robot migration is carried out. Furthermore, the task status is constantly monitored through S-MT. Thus, the proposed work optimizes the robotic tasks with 98.77% accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".