Attention-Based Bi-LSTM Model for Arabic Depression Classification

Almars, Abdulqader M.

doi:10.32604/cmc.2022.022609

Cited by 30 publications

(11 citation statements)

References 38 publications

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“…Many prior studies have demonstrated that deep learning and EEG can be employed for depression identification ( Li et al, 2019 ; Almars, 2022 ). As we are aware, EEG signals encompass spatial topological information, yet this facet is often underestimated.…”

Section: Methodsmentioning

confidence: 99%

Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology

Liu,

Jia,

Wang

2024

Front. Neurosci.

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Depression has become the prevailing global mental health concern. The accuracy of traditional depression diagnosis methods faces challenges due to diverse factors, making primary identification a complex task. Thus, the imperative lies in developing a method that fulfills objectivity and effectiveness criteria for depression identification. Current research underscores notable disparities in brain activity between individuals with depression and those without. The Electroencephalogram (EEG), as a biologically reflective and easily accessible signal, is widely used to diagnose depression. This article introduces an innovative depression prediction strategy that merges time-frequency complexity and electrode spatial topology to aid in depression diagnosis. Initially, time-frequency complexity and temporal features of the EEG signal are extracted to generate node features for a graph convolutional network. Subsequently, leveraging channel correlation, the brain network adjacency matrix is employed and calculated. The final depression classification is achieved by training and validating a graph convolutional network with graph node features and a brain network adjacency matrix based on channel correlation. The proposed strategy has been validated using two publicly available EEG datasets, MODMA and PRED+CT, achieving notable accuracy rates of 98.30 and 96.51%, respectively. These outcomes affirm the reliability and utility of our proposed strategy in predicting depression using EEG signals. Additionally, the findings substantiate the effectiveness of EEG time-frequency complexity characteristics as valuable biomarkers for depression prediction.

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Section: Methodsmentioning

confidence: 99%

Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology

Liu,

Jia,

Wang

2024

Front. Neurosci.

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“…Deep learning models like CNNs, RNNs, and LSTMs have recently been used to detect suicide risk in social media users [31][32][33]. Compared to traditional machine learning methods, deep learning offers a more objective, accurate, and comprehensive assessment of suicide risk by leveraging extensive data and deep neural networks.…”

Section: Related Workmentioning

confidence: 99%

Predicting Users’ Latent Suicidal Risk in Social Media: An Ensemble Model Based on Social Network Relationships

Meng,

Wang,

Yang

et al. 2024

CMC

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Suicide has become a critical concern, necessitating the development of effective preventative strategies. Social media platforms offer a valuable resource for identifying signs of suicidal ideation. Despite progress in detecting suicidal ideation on social media, accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge. To tackle this, we have developed a dataset focused on Chinese suicide narratives from Weibo's Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships (TCNN-SN). This model enhances predictive performance by leveraging social network relationship features and applying correction factors within a weighted linear fusion framework. It is specifically designed to identify key individuals who can help uncover hidden suicidal users and clusters. Our model, assessed using the bespoke dataset and benchmarked against alternative classification approaches, demonstrates superior accuracy, F1-score and AUC metrics, achieving 88.57%, 88.75% and 94.25%, respectively, outperforming traditional TextCNN models by 12.18%, 10.84% and 10.85%. We assert that our methodology offers a significant advancement in the predictive identification of individuals at risk, thereby contributing to the prevention and reduction of suicide incidences.

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“…Standard LSTM can only use past context information in one direction. This can be overcome by using (BLSTM) [35] Which can learn long-range context dynamics in both input directions. BLSTM networks consist of running two LSTMs in parallel: the first network reads the input sequence from right to left and the second network in reverse from left to right.…”

Section: Recurrent Neural Network (Rnns)mentioning

confidence: 99%

An Efficient Hybrid Model for Arabic Text Recognition

Lamtougui¹,

Moubtahij²,

Fouadi³

et al. 2023

Computers, Materials &Amp; Continua

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In recent years, Deep Learning models have become indispensable in several fields such as computer vision, automatic object recognition, and automatic natural language processing. The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field, especially for the Arabic language, which, compared to other languages, has a dearth of published works. In this work, we presented an efficient and new system for offline Arabic handwritten text recognition. Our new approach is based on the combination of a Convolutional Neural Network (CNN) and a Bidirectional Long-Term Memory (BLSTM) followed by a Connectionist Temporal Classification layer (CTC). Moreover, during the training phase of the model, we introduce an algorithm of data augmentation to increase the quality of data. Our proposed approach can recognize Arabic handwritten texts without the need to segment the characters, thus overcoming several problems related to this point. To train and test (evaluate) our approach, we used two Arabic handwritten text recognition databases, which are IFN/ENIT and KHATT. The Experimental results show that our new approach, compared to other methods in the literature, gives better results.

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Attention-Based Bi-LSTM Model for Arabic Depression Classification

Cited by 30 publications

References 38 publications

Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology

Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology

Predicting Users’ Latent Suicidal Risk in Social Media: An Ensemble Model Based on Social Network Relationships

An Efficient Hybrid Model for Arabic Text Recognition

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