LSTM-SN: complex text classifying with LSTM fusion social network

Wei, Wei; Li, Xiaowan; Zhang, Beibei; Li, Linfeng; Damaševičius, Robertas; Scherer, Rafał

doi:10.1007/s11227-022-05034-w

Cited by 7 publications

(8 citation statements)

References 26 publications

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“…special neural network that can learn dependencies over long periods of time and remember important information from previous processing steps. This algorithm, which is especially used in processes such as natural language processing and speech recognition, gives successful results in the classification of MRI images today [25], [26]. It performs the classification process of MRI images through memory blocks connected through layers.…”

Section: Long Short-term Memorymentioning

confidence: 99%

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

Gül

Kaya

2023

Preprint

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The development of software engineering has given very successful results in the field of medical diagnosis in recent years. Deep learning and machine learning applications give remarkable results in the detection, monitoring, diagnosis, and treatment of possible tumoral regions with the analysis of the obtained medical images and data mining. Studies to diagnose brain tumors are essential because of the wide variety of brain tumors, the importance of the patient's survival time, and the brain tumor's aggressive nature. Brain tumors are defined as a disease with destructive and lethal features. Detection of a brain tumor is an essential process because of the difficulty in distinguishing between abnormal and normal tissues. With the right diagnosis, the patient can get excellent treatment, extending their lifespan. Despite all the research, there are still significant limitations in detecting tumor areas because of abnormal lesion distribution. It may be challenging to locate an area with very few tumor cells because areas with such small areas frequently appear healthy. Studies are becoming more common in which automated classification of early-stage brain tumors is performed using deep learning or machine learning approaches. This study proposes a hybrid deep learning model for the detection and early diagnosis of brain tumors via magnetic resonance imaging. The dataset images were subjected to Local Binary Pattern (LBP) and Long Short-Term Memory (LSTM) algorithms. The highest accuracy rate obtained in the hybrid model created is 98.66%.

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Section: Long Short-term Memorymentioning

confidence: 99%

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

Gül

Kaya

2023

Preprint

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“…This adaptability translates into exceptional performance when dealing with diverse time series data. LSTM has shown remarkable efficacy in various domains, including natural language processing [21], time series analysis [22], and computer vision [23]. Figure 2 illustrates the structure of an LSTM unit at time t. Its formal expression is provided in Table 1.…”

Section: Seq2seq Based On Lstmmentioning

confidence: 99%

Wave Power Prediction Based on Seasonal and Trend Decomposition Using Locally Weighted Scatterplot Smoothing and Dual-Channel Seq2Seq Model

Liu,

Wang,

Tao

et al. 2023

Energies

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Wave energy has emerged as a focal point in marine renewable energy research. Accurate prediction of wave power plays a pivotal role in enhancing power supply reliability. This paper introduces an innovative wave power prediction method that combines seasonal–trend decomposition using LOESS (STL) with a dual-channel Seq2Seq model. The decomposition model addresses the issue of component redundancy in current input decomposition methods, thereby uncovering key components. The prediction model improves upon the limitations of current prediction models that directly concatenate multiple features, allowing for a more detailed consideration of both trend and periodic features. The proposed approach begins by decomposing the power sequence based on tidal periods and optimal correlation criteria, effectively extracting both trend and periodic features. Subsequently, a dual-channel Seq2Seq model is constructed. The first channel employs temporal pattern attention to capture the trend and stochastic fluctuation information, while the second channel utilizes multi-head self-attention to further enhance the extraction of periodic components. Model validation is performed using data from two ocean buoys, each with a five-year dataset. The proposed model achieves an average 2.45% reduction in RMSE compared to the state-of-the-art method. Both the decomposition and prediction components of the model contribute to this increase in accuracy.

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“…Complex networks have attracted extensive attention because of its wide applications in sociology, biology, and other fields. [1][2][3] As one of the important collective behaviors of complex networks, synchronization plays an important role in the study of complex networks. [4][5][6] Coupled neural networks (CNNs), as a special case of complex networks, its synchronization problem has been paid more attention and some achievements have been made.…”

Section: Introductionmentioning

confidence: 99%

“…Complex networks have attracted extensive attention because of its wide applications in sociology, biology, and other fields 1–3 . As one of the important collective behaviors of complex networks, synchronization plays an important role in the study of complex networks 4–6 .…”

Section: Introductionmentioning

confidence: 99%

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Cluster synchronization of stochastic two‐layer networks with infinite distributed delays via delayed pinning impulsive control

Zhang,

Wei,

Wang

et al. 2024

Adaptive Control & Signal

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SummaryThe cluster synchronization problem of stochastic two‐layer networks with infinite distributed delays is concerned. Firstly, we study the cluster synchronization of the first layer (leader‐layer) network with the average state of each cluster of sets as synchronization target. Secondly, we design a delayed pinning impulsive controller to synchronize the second layer (follower‐layer) network to the first layer network in a mean square cluster sense. Based on stochastic impulsive analysis and Lyapunov stability theory, some sufficient conditions for cluster synchronization are obtained. Finally, the effectiveness of the theoretical results is verified through numerical simulations.

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LSTM-SN: complex text classifying with LSTM fusion social network

Cited by 7 publications

References 26 publications

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

Wave Power Prediction Based on Seasonal and Trend Decomposition Using Locally Weighted Scatterplot Smoothing and Dual-Channel Seq2Seq Model

Cluster synchronization of stochastic two‐layer networks with infinite distributed delays via delayed pinning impulsive control

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