Development of a Semantic Text Classification Mobile Application Using TensorFlow Lite and Firebase ML Kit
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
The development of neural networks in the current industrial era 4.0 should help various work fields, one of which is the scientific literature.The problem that often occurs is that scientific papers still use manual sorting of themes/semantics.The purpose of this research is to build a semantic text classification application that can allow users to sort by theme/semantics by using a neural network model, Recurrent Neural Network (RNN) embedded in a smartphone.The development of this application uses the waterfall method in which there are analysis and system design.The application implements the text recognition feature of the Firebase ML Kit.It is developed using a general machine learning cycle method or approach consisting of data identification, data preparation, algorithm selection, model training, model evaluation and model deployment.The model was built using abstract data from scientific papers from the State University of Padang Library.The total data obtained 84 training data and 21 test data using a ratio of 80:20 percent to perform the validation test.The neural network model uses the AverageWordVec specification provided by TensorFlow Lite Model Maker with three classification outputs.The model validation test reached 0.7619 accuracy values with 0.7782 loss values.The model is executed using the TensorFlow Lite interpreter embedded in the application.The application results fulfill the overall system functional requirements analysis.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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