Bibliographic record
Abstract
In pursuit of Egypt's Vision 2030, which emphasizes the pivotal role of governance within state institutions and society, artificial intelligence (AI) stands as a transformative force. A central tenet of this vision involves harnessing AI technologies to accelerate the digitization of documents and their seamless integration into a unified system. This fosters more informed decision-making processes and revolutionizes processing and utilizing information. Our current research project aligns with this broader goal by deep learning capabilities to support organizations involved in enterprise applications. By incorporating AI-driven solutions, we aim to empower these organizations to manage their operations and optimize resource allocation efficiently. The proposed model eliminates manual input of handwritten invoices into ERP applications, resulting in substantive cost savings. Furthermore, the proposed model integrates an entity classification system enhanced by LSTM, significantly improving invoice data's clarity and accuracy. This streamlined approach saves valuable time and enhances the overall effectiveness of resource allocation and decision-making processes. In essence, by integrating AI into document management and enterprise operations, we are not only contributing to the realization of Egypt's vision but also spearheading a technological transformation that has far-reaching implications for governance, efficiency, and progress in the digital age.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.055 |
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".