Optimizing deep learning predictive models: A comprehensive review of RNN and its variant architectures
Bibliographic record
Abstract
Accurate prediction of an engineering system behaviour is essential for ensuring a stable and secure long-term operation. It enables proactive problem solving, prevents disruption, enhances safety, and facilitates the seamless integration of new technologies such as digital twins. Consequently, several approaches have been employed to enhance system behaviour prediction by utilizing conventional machine learning models. Moreover, the advent of deep neural networks has proven to be more effective in several scenarios as they offer enhanced prediction accuracy and capacity in handling complex and high-dimensional data. Despite their advantages, deep neural networks encounter challenges in determining the optimal configuration for model structures. Therefore, various optimization techniques such as hyperparameter optimization, activation functions, framework search algorithms, algorithm optimizers, and hybrid frameworks have been proposed to mitigate these challenges. Hence, this study emphasizes recurrent neural networks and their variants, as one of the most popularly utilized frameworks for predictive algorithms. Also, several strategies and techniques for improving the performance of these predictive frameworks have been holistically discussed. By analyzing the state-of-the-art optimization approaches, it serves as a valuable resource for researchers, providing a comprehensive understanding of the approaches that can be employed to optimize prediction accuracy for specific applications and tasks. • Prognostics and health management frameworks predict remaining useful life in systems. • LSTM and GRU variants address RNN limitations like vanishing and exploding gradients. • BiLSTM leverages both forward and backward data flows, enhancing time-series prediction. • Transformer models use self-attention to efficiently capture long-term dependencies. • Hyperparameter tuning, activation functions, and optimization techniques are crucial for model accuracy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".