Evolving Long Short-Term Memory Network-Based Text Classification

Singh, Arjun; Dargar, Shashi Kant; Gupta, Amit; Kumar, Ashish; Srivastava, Atul; Srivastava, Mitali; Tiwari, Pradeep Kumar; Ullah, Mohammad Aman

doi:10.1155/2022/4725639

Cited by 14 publications

(9 citation statements)

References 38 publications

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“…In addition, there is a problem of gradient disappearance during backpropagation, mainly because the value of the gradient update neural network weight changes little, resulting in an inability to learn more text series information. To eliminate this issue, the long short-term memory (LSTM) [27] and GRU [28] text classification methods are used. The LSTM approach has a significant quantity of parameters and is computationally sophisticated.…”

Section: Shallow Bidirectional Gru Network Designmentioning

confidence: 99%

Integrated Model Text Classification Based on Multineural Networks

Hu,

Xiong,

Wang

et al. 2024

Electronics

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Based on the original deep network architecture, this paper replaces the deep integrated network by integrating shallow FastText, a bidirectional gated recurrent unit (GRU) network and the convolutional neural networks (CNNs). In FastText, word embedding, 2-grams and 3-grams are combined to extract text features. In recurrent neural networks (RNNs), a bidirectional GRU network is used to lessen information loss during the process of transmission. In CNNs, text features are extracted using various convolutional kernel sizes. Additionally, three optimization algorithms are utilized to improve the classification capabilities of each network architecture. The experimental findings using the social network news dataset demonstrate that the integrated model is effective in improving the accuracy of text classification.

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Section: Shallow Bidirectional Gru Network Designmentioning

confidence: 99%

Integrated Model Text Classification Based on Multineural Networks

Hu,

Xiong,

Wang

et al. 2024

Electronics

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“…In sequence prediction problems, classifiers take an input instance and assign it to one of the available classes. Long short-term memory (LSTM) is a type of recurrent neural network (RNN) that has found extensive use in applications such as speech recognition [ 31 ], text classification [ 32 ], and ECG biometrics [ 33 ]. In this field, a hybrid model called CNN-LSTM is utilized.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System

Fard

Agrawal

Gebali

et al. 2023

Sensors

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Advancements in digital communications that permit remote patient visits and condition monitoring can be attributed to a revolution in digital healthcare systems. Continuous authentication based on contextual information offers a number of advantages over traditional authentication, including the ability to estimate the likelihood that the users are who they claim to be on an ongoing basis over the course of an entire session, making it a much more effective security measure for proactively regulating authorized access to sensitive data. Current authentication models that rely on machine learning have their shortcomings, such as the difficulty in enrolling new users to the system or model training sensitivity to imbalanced datasets. To address these issues, we propose using ECG signals, which are easily accessible in digital healthcare systems, for authentication through an Ensemble Siamese Network (ESN) that can handle small changes in ECG signals. Adding preprocessing for feature extraction to this model can result in superior results. We trained this model on ECG-ID and PTB benchmark datasets, achieving 93.6% and 96.8% accuracy and 1.76% and 1.69% equal error rates, respectively. The combination of data availability, simplicity, and robustness makes it an ideal choice for smart healthcare and telehealth.

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“…The hidden layer contains one or more neurons capable of storing long term memory, and each neuron determines the input, output or forget constant error conveyor (CEC) through a gate function. Long short-term memory (LSTM) networks have been widely used to solve text classification problems (Singh et al, 2022).…”

Section: Neural Networkmentioning

confidence: 99%

Prediction of protein-protein interaction sites in intrinsically disordered proteins

Chen

Li²,

Yang³

et al. 2022

Front. Mol. Biosci.

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Intrinsically disordered proteins (IDPs) participate in many biological processes by interacting with other proteins, including the regulation of transcription, translation, and the cell cycle. With the increasing amount of disorder sequence data available, it is thus crucial to identify the IDP binding sites for functional annotation of these proteins. Over the decades, many computational approaches have been developed to predict protein-protein binding sites of IDP (IDP-PPIS) based on protein sequence information. Moreover, there are new IDP-PPIS predictors developed every year with the rapid development of artificial intelligence. It is thus necessary to provide an up-to-date overview of these methods in this field. In this paper, we collected 30 representative predictors published recently and summarized the databases, features and algorithms. We described the procedure how the features were generated based on public data and used for the prediction of IDP-PPIS, along with the methods to generate the feature representations. All the predictors were divided into three categories: scoring functions, machine learning-based prediction, and consensus approaches. For each category, we described the details of algorithms and their performances. Hopefully, our manuscript will not only provide a full picture of the status quo of IDP binding prediction, but also a guide for selecting different methods. More importantly, it will shed light on the inspirations for future development trends and principles.

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Evolving Long Short-Term Memory Network-Based Text Classification

Cited by 14 publications

References 38 publications

Integrated Model Text Classification Based on Multineural Networks

Integrated Model Text Classification Based on Multineural Networks

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System

Prediction of protein-protein interaction sites in intrinsically disordered proteins

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