Analysis of Artificial Intelligence Methods and Algorithms for Processing Data as a Series of Signals
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 paper deals with the issues of improving the accuracy when performing the task of classifying several signals, where the main task is to determine the class of an object based on the data of the time series of this object. Examples of such signals are ECG signals, sounds, vibrations, and others. To successfully solve the classification problem, it is important to select the appropriate method correctly and qualitatively prepare the data for model training, including pre-processing of data and selection of model parameters. This study includes a review of artificial intelligence methods in the field of data analysis based on several signals, including machine learning algorithms. The datasets used for the study are Sonar, Doppler, and Winnipeg. Based on the comparison of the studied methods, SVM, Random Forest, AdaBoost, KNN showed the highest accuracy. The average accuracy of the classification was 0.9.
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 | 0.000 |
| 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