AI-Powered Robotic Cloud Automation-Based Dynamic Task Allocation and Process Optimization Using E-WFO and C <sup>2</sup> DRBM
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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