PARKINSONâS DISEASE CLASSIFICATION USING HYBRID NAMIB SQUIRREL SEARCH WATER ALGORITHM-BASED DEEP LEARNING APPROACH
Bibliographic record
Abstract
Parkinson's disease (PD) is a neurological disease throughout the globe, and it is the second leading reason for death and impairment. The overall cases of PD have nearly doubled in the past 15 years. It has been defined by the absence of dopamine cells in the brain. PD affects about 1% of individuals over the age of 65, while 90% of them are affected by speech disorders like articulation, phonation, fluency, and prosody. Hence, the earlier prediction is significant in providing a good treatment for PD. In this research, the Namib squirrel search water algorithm (NSSWA) is proposed for PD classification. The voice sample is used as input and it is preprocessed using a Gaussian filter. Furthermore, feature extraction is applicable for the extraction of significant features, and the feature selection is done using the NSSWA. Moreover, the NSSWA-trained convolutional neural network (CNN) fused long short-term memory (LSTM) (CNN-LSTM), called NSSWA_CNN-LSTM, is used in PD classification. In addition, the efficacy of the model is validated via accuracy, sensitivity, specificity, loss function, mean-square error, and root-mean-square error with optimal values of 0.931, 0.934, 0.929, 0.068, 0.097, and 0.312 obtained.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".