LSTM-ANN &amp; BiLSTM-ANN: Hybrid deep learning models for enhanced classification accuracy

Kowsher, Md.; Tahabilder, Anik; Sanjid, Md. Zahidul Islam; Prottasha, Nusrat Jahan; Uddin, Md. Shihab; Hossain, Arman; Jilani, Md. Abdul Kader

doi:10.1016/j.procs.2021.10.013

Cited by 26 publications

(16 citation statements)

References 13 publications

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“…In terms of the specified features, it differs from the studies published in recent years [21]- [23]. With the technical progress and development of machine learning algorithms such as ANN, the deep integration of LSTM and ANN structures instead of fully connected ANN gave a better result than the ANN method alone [25].…”

Section: Resultsmentioning

confidence: 79%

Recurrent Neural Network Based Model Development for Energy Consumption Forecasting

ÇETİNER

2022

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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The world population is increasing day by day. As a result, limited resources are decreasing day by day. On the other hand, the amount of energy needed is constantly increasing. In this sense, decision makers must accurately estimate the amount of energy that society will require in the coming years and make plans accordingly. These plans are of critical importance for the peace and welfare of society. Based on the energy consumption values of Germany, it is aimed at estimating the energy consumption values with the GRU, LSTM, and proposed hybrid LSTM-GRU methods, which are among the popular RNN algorithms in the literature. The estimation performances of LSTM and GRU algorithms were obtained for MSE, RMSE, MAPE, MAE, and R2 values as 0.0014, 0.0369, 6.35, 0.0292, 0.9703 and 0.0017, 0.0375, 6.60, 0.0298, 0.9650, respectively. The performance of the proposed hybrid LSTM-GRU method, which is another RNN-based algorithm used in the study, was obtained as 0.0013, 0.0358, 5.89, 0.0275, and 0.9720 for MSE, RMSE, MAPE, MAE and R2 values, respectively. Although all three methods gave similar results, the training times of the proposed hybrid LSTM-GRU and LSTM algorithms took 7.50 and 6.58 minutes, respectively, but it took 4.87 minutes for the GRU algorithm. As can be understood from this value, it has been determined that it is possible to obtain similar values by sacrificing a very small amount of prediction performance in cases with time limitations.

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Section: Resultsmentioning

confidence: 79%

Recurrent Neural Network Based Model Development for Energy Consumption Forecasting

ÇETİNER

2022

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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“…The bidirectional long short-term memory (Bi-LSTM) model has resolved the constraints of the former LSTM by considering both the past and future context of the tracked sequence of dependencies at every time step as shown in Figure [32]- [35]. The Bi-LSTM model can learn incoming data sequentially and develop recurrent neural network models that have been relied on in the context of the previous state to save their important information [33].…”

Section: Bidirectional Long Short-term Memorymentioning

confidence: 99%

Deep learning in phishing mitigation: a uniform resource locator-based predictive model

Salah

Zuhair

2023

IJECE

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<span lang="EN-US">To mitigate the evolution of phish websites, various phishing prediction8 schemes are being optimized eventually. However, the optimized methods produce gratuitous performance overhead due to the limited exploration of advanced phishing cues. Thus, a phishing uniform resource locator-based predictive model is enhanced by this work to defeat this deficiency using deep learning algorithms. This model’s architecture encompasses pre-processing of the effective feature space that is made up of 60 mutual uniform resource locator (URL) phishing features, and a dual deep learning-based model of convolution neural network with bi-directional long short-term memory (CNN-BiLSTM). The proposed predictive model is trained and tested on a dataset of 14,000 phish URLs and 28,074 legitimate URLs. Experimentally, the performance outputs are remarked with a 0.01% false positive rate (FPR) and 99.27% testing accuracy.</span>

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“…After being combined with the extra MLP layer, SBiLSTM is expected to enhance the forecasting accuracy (34). These models have been trained using data from the same test intersection and their performance results are shown in Figure 7.…”

Section: Preliminary Performance Evaluationmentioning

confidence: 99%

Real-Time Intersection Turning Movement Flow Forecasting Using a Parallel Bidirectional Long Short-Term Memory Neural Network Model

Zhang

Pan

2023

Transportation Research Record: Journal of the Transportation R

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Real-time forecasting of intersection turning movements is a critical requirement for predictive traffic signal control at signalized intersections. Several traffic flow forecasting models have been developed in the past; however, most of them have focused only on the road segment-level traffic instead of the turning movement flow (TMF) at intersections. Therefore, in this paper, we propose a new TMF forecasting model, which consists of a combination of two neural network models, namely the stacked bidirectional long short-term memory and the traditional multi-layer perceptron model; this combination will enable effective learning for both short- and long-term time-varying patterns. Moreover, extensive computational experiments, using two years of turning movement counts at 22 intersections in the city of Milton, Ontario, Canada, explore the performance advantage of the proposed model in comparison with several state-of-the-art base models for forecasting accuracy, robustness, and transferability.

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LSTM-ANN & BiLSTM-ANN: Hybrid deep learning models for enhanced classification accuracy

Cited by 26 publications

References 13 publications

Recurrent Neural Network Based Model Development for Energy Consumption Forecasting

Recurrent Neural Network Based Model Development for Energy Consumption Forecasting

Deep learning in phishing mitigation: a uniform resource locator-based predictive model

Real-Time Intersection Turning Movement Flow Forecasting Using a Parallel Bidirectional Long Short-Term Memory Neural Network Model

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