Post Graduate Admission Prediction Using ANN
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
Abstract: Nowadays, we see many students showing interest in higher studies away from their home countries. Generally, students often lack sufficient knowledge about the requirements, procedures, specific details of universities in countries like the USA, UK, Canada, etc. As a result, they often turn to education consultancy firms for assistance in securing admission to universities that best align with their profiles. However, this process typically requires significant financial investment in consultancy fees. The objective of this project is to create a system using Artificial Neural Network (ANN) which helps to predict the percentage of chance of admittance of students by utilizing the various test attributes like GRE, TOEFL, Research papers etc., that will assist students in assessing the likelihood of their university applications being accepted i.e., it helps them to know about what is the chance of getting admission in reputed Foreign Universities. An intuitive user-friendly interface will be developed for the users to determine their chances of admission to a university by entering their various scores.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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