Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction

da Silva, Davi Guimarães; Meneses, Anderson Alvarenga de Moura

doi:10.1016/j.egyr.2023.09.175

Cited by 27 publications

(8 citation statements)

References 52 publications

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“…Compared to the conventional unidirectional LSTM, the Bi-LSTM’s dual-directional learning framework enables the model to encapsulate a comprehensive temporal context, thereby enhancing its predictive accuracy for sequential data recovery tasks. References substantiating this preference include seminal works that highlight the superiority of Bi-LSTMs over single-direction LSTMs in various sequential data processing tasks [ 45 , 46 ]. Upon scrutinizing the outcomes presented in Figure 5 , the effective impact of the U-net-inspired skip connections and the adaptive Huber loss function in the proposed Bi-LSTM autoencoder is evident.…”

Section: Resultsmentioning

confidence: 99%

Deep-Learning-Based Recovery of Missing Optical Marker Trajectories in 3D Motion Capture Systems

Yuhai,

Choi,

Cho

et al. 2024

Bioengineering

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Motion capture (MoCap) technology, essential for biomechanics and motion analysis, faces challenges from data loss due to occlusions and technical issues. Traditional recovery methods, based on inter-marker relationships or independent marker treatment, have limitations. This study introduces a novel U-net-inspired bi-directional long short-term memory (U-Bi-LSTM) autoencoder-based technique for recovering missing MoCap data across multi-camera setups. Leveraging multi-camera and triangulated 3D data, this method employs a sophisticated U-shaped deep learning structure with an adaptive Huber regression layer, enhancing outlier robustness and minimizing reconstruction errors, proving particularly beneficial for long-term data loss scenarios. Our approach surpasses traditional piecewise cubic spline and state-of-the-art sparse low rank methods, demonstrating statistically significant improvements in reconstruction error across various gap lengths and numbers. This research not only advances the technical capabilities of MoCap systems but also enriches the analytical tools available for biomechanical research, offering new possibilities for enhancing athletic performance, optimizing rehabilitation protocols, and developing personalized treatment plans based on precise biomechanical data.

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Section: Resultsmentioning

confidence: 99%

Deep-Learning-Based Recovery of Missing Optical Marker Trajectories in 3D Motion Capture Systems

Yuhai,

Choi,

Cho

et al. 2024

Bioengineering

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“…This is caused by the advantages of Bi-LSTM where BLSTM allows information flow in both directions, adding a new LSTM layer that inverts the sequence, and the outputs of both layers are combined, for example, with average, sum, multiplication, or concatenation. The possibility of two flow directions enables a better learning process [16].…”

Section: B Discussionmentioning

confidence: 99%

EEG-Based Depression Detection in the Prefrontal Cortex Lobe using mRMR Feature Selection and Bidirectional LSTM

Pratiwi

2023

ULTIMA Computing

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Depression can induce significant anguish and impair one's ability to perform effectively in professional, academic, and familial settings. This condition has the potential to result in suicide. Annually, the number of deaths resulting from suicide exceeds 700,000. Among individuals aged 15-29, suicide has emerged as the fourth most prevalent cause of mortality. Challenges in treating depression include limited accessibility to mental health care in rural regions and misdiagnosis resulting from subjective evaluations, wherein insufficient expertise can contribute to inaccurate diagnoses. Electroencephalography (EEG) has gained popularity as a tool for the identification and study of a number of mental illnesses in the past several years. Therefore, an automated technique is required to precisely distinguish between normal EEG signals and depression signals. This research focused on developing an EEG-based depression detection system in the prefrontal cortex lobe area (Fp1, Fpz, and Fp2). One of the developments carried out in this research is the implementation of Bidirectional Long Short-Term Memory (Bi-LSTM) as the model classification and minimum redundancy maximum relevance (mRMR) feature selection. Results suggest that the combination of mRMR feature selection with 25 features and the Bidirectional LSTM obtained 92.83% for the accuracy.

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“…It can maintain its state over time, as well as a nonlinear gating unit, which regulates the flow of information in and out of the unit. 19 Specifically, the LSTM model consists of the following four main components:…”

Section: Experimental and Machine Learningmentioning

confidence: 99%

“…Among them, LSTM (Long Short-Term Memory) is a particular type of RNN (Recurrent Neural Networks) and is a powerful tool that mitigates the long term memory problem and vanishing problem, which appear to be tricky issues in RNNs. 18,19 It has become an effective and scalable model for solving several learning problems related to sequential data. The core idea of LSTM is to replace the summation unit in the hidden layer by introducing a storage unit.…”

Section: Ensemble Hybrid Model (Eh)mentioning

confidence: 99%

Integrated hybrid modeling and SHAP (SHapley Additive exPlanations) to predict and explain the adsorption properties of thermoplastic polyurethane (TPU) porous materials

2024

RSC Adv.

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Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction

Cited by 27 publications

References 52 publications

Deep-Learning-Based Recovery of Missing Optical Marker Trajectories in 3D Motion Capture Systems

Deep-Learning-Based Recovery of Missing Optical Marker Trajectories in 3D Motion Capture Systems

EEG-Based Depression Detection in the Prefrontal Cortex Lobe using mRMR Feature Selection and Bidirectional LSTM

Integrated hybrid modeling and SHAP (SHapley Additive exPlanations) to predict and explain the adsorption properties of thermoplastic polyurethane (TPU) porous materials

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