Intelligent Software Bug Prediction Framework with Parameter-Tuned LSTM with Attention Mechanism Using Adaptive Target-Based Pooling Deep Features

Gupta, Anurag; Sharma, Mayank; Srivastava, A. K.

doi:10.1142/s0218539323500055

Cited by 2 publications

(3 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This enables the model to capture the time series information in the input data and automatically select the most relevant features for prediction [43][44][45]. The superior precision of the CLAP prediction model stems from two fundamental components: the incorporation of the attention mechanism and the synergistic combination of CNN and LSTM neural network architectures [46][47][48][49]. The attention mechanism plays a pivotal role by assigning varying attention weights to different segments of the input data.…”

Section: Resultsmentioning

confidence: 99%

“…The attention mechanism plays a pivotal role by assigning varying attention weights to different segments of the input data. This enables the model to effectively diminish the influence of irrelevant information and instead prioritize relevant and important details during the processing and learning tasks [46][47][48][49]. The attention mechanism was integrated into CLAP, allowing it to selectively concentrate on significant aspects within the input experimental data [46][47][48][49].…”

Section: Resultsmentioning

confidence: 99%

“…This enables the model to effectively diminish the influence of irrelevant information and instead prioritize relevant and important details during the processing and learning tasks [46][47][48][49]. The attention mechanism was integrated into CLAP, allowing it to selectively concentrate on significant aspects within the input experimental data [46][47][48][49]. By leveraging the attention mechanism, the model becomes adept at identifying and focusing on the most informative features in the input, leading to enhanced predictive capabilities [50,51].…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

CLAP: Gas Saturation Prediction in Shale Gas Reservoir Using a Cascaded Convolutional Neural Network–Long Short-Term Memory Model with Attention Mechanism

Yang,

Zhang,

Zhao

et al. 2023

Processes

View full text Add to dashboard Cite

Gas saturation prediction is a crucial area of research regarding shale gas reservoirs, as it plays a vital role in optimizing development strategies and improving the efficiency of exploration efforts. Despite the advancements in deep learning techniques, accurately modeling the complex nonlinear relationships involved in gas saturation prediction remains a challenge. To address this issue, we propose a novel cascaded model, CLAP, combining convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) with an attention mechanism. It effectively captures and visualizes the intricate nonlinear relationships, enabling accurate gas saturation prediction in shale gas reservoirs. In this study, nine logging curves from 27 shale gas wells in the Changning area of the Sichuan Basin were used to train the CLAP model for predicting the gas saturation of the Wufeng-Longmaxi Formation shale. Compared to the Archie and random forest models, the CLAP model exhibited enhanced accuracy in predicting shale gas saturation. Promisingly, the CLAP model demonstrates outstanding statistical performance in gas saturation prediction, achieving an impressive R2 score of 0.762 and a mean square error (MSE) score of 0.934. These positive results highlight the effectiveness and potential utility of our proposed CLAP model in accurately predicting gas saturation in shale gas reservoirs. The application of deep learning techniques, such as CNNs, LSTM, and attention mechanisms, presents a promising avenue for further advancements in this field.

show abstract