Mohammed Hadwan^1,2,*, Hamzah A. Alsayadi^3,4, Salah AL-Hagree⁵

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3471-3487, 2023, DOI:10.32604/cmc.2023.033457

Abstract The attention-based encoder-decoder technique, known as the trans-former, is used to enhance the performance of end-to-end automatic speech recognition (ASR). This research focuses on applying ASR end-to-end transformer-based models for the Arabic language, as the researchers’ community pays little attention to it. The Muslims Holy Qur’an book is written using Arabic diacritized text. In this paper, an end-to-end transformer model to building a robust Qur’an vs. recognition is proposed. The acoustic model was built using the transformer-based model as deep learning by the PyTorch framework. A multi-head attention mechanism is utilized to represent the encoder and… More >

Akram Ali Alhadad^1,2,*, Omar Tarawneh³, Reham R. Mostafa¹, Hazem M. El-Bakry¹

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3333-3350, 2023, DOI:10.32604/cmc.2023.033413

Abstract COVID-19 is the common name of the disease caused by the novel coronavirus (2019-nCoV) that appeared in Wuhan, China in 2019. Discovering the infected people is the most important factor in the fight against the disease. The gold-standard test to diagnose COVID-19 is polymerase chain reaction (PCR), but it takes 5–6 h and, in the early stages of infection, may produce false-negative results. Examining Computed Tomography (CT) images to diagnose patients infected with COVID-19 has become an urgent necessity. In this study, we propose a residual attention deep support vector data description SVDD (RADSVDD) approach… More >

Nada Ali Hakami¹, Hanan Ahmed Hosni Mahmoud^2,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3697-3710, 2023, DOI:10.32604/cmc.2023.031525

Abstract A worthy text summarization should represent the fundamental content of the document. Recent studies on computerized text summarization tried to present solutions to this challenging problem. Attention models are employed extensively in text summarization process. Classical attention techniques are utilized to acquire the context data in the decoding phase. Nevertheless, without real and efficient feature extraction, the produced summary may diverge from the core topic. In this article, we present an encoder-decoder attention system employing dual attention mechanism. In the dual attention mechanism, the attention algorithm gathers main data from the encoder side. In the More >

Wei Wu¹, Weigong Zhang^1,*, Dong Wang¹, Lydia Zhu², Xiang Song³

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3811-3828, 2023, DOI:10.32604/cmc.2023.029787

Abstract An increase in car ownership brings convenience to people’s life. However, it also leads to frequent traffic accidents. Precisely forecasting surrounding agents’ future trajectories could effectively decrease vehicle-vehicle and vehicle-pedestrian collisions. Long-short-term memory (LSTM) network is often used for vehicle trajectory prediction, but it has some shortages such as gradient explosion and low efficiency. A trajectory prediction method based on an improved Transformer network is proposed to forecast agents’ future trajectories in a complex traffic environment. It realizes the transformation from sequential step processing of LSTM to parallel processing of Transformer based on attention mechanism. More >

Peizhu Gong¹, Jin Liu¹, Zhongdai Wu², Bing Han², Y. Ken Wang³, Huihua He^4,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4203-4220, 2023, DOI:10.32604/cmc.2023.028291

Abstract Speech emotion recognition, as an important component of human-computer interaction technology, has received increasing attention. Recent studies have treated emotion recognition of speech signals as a multimodal task, due to its inclusion of the semantic features of two different modalities, i.e., audio and text. However, existing methods often fail in effectively represent features and capture correlations. This paper presents a multi-level circulant cross-modal Transformer (MLCCT) for multimodal speech emotion recognition. The proposed model can be divided into three steps, feature extraction, interaction and fusion. Self-supervised embedding models are introduced for feature extraction, which give a… More >

Zhiyun Yang^1,#, Qi Liu^1,#,*, Hao Wu¹, Xiaodong Liu², Yonghong Zhang³

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 45-64, 2023, DOI:10.32604/cmes.2022.022045

Abstract Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain. Recent relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation, where radar echo maps were used to predict their consequent moment, so as to recognize potential severe convective weather events. However, these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation, due to the size limitation of convolution filter, lack of global… More > Graphic Abstract

Hong’an Li¹, Min Zhang^1,*, Dufeng Chen², Jing Zhang¹, Meng Yang³, Zhanli Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 779-794, 2023, DOI:10.32604/cmes.2022.022369

Abstract Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis. To overcome the limitations of the color rendering method based on deep learning, such as poor model stability, poor rendering quality, fuzzy boundaries and crossed color boundaries, we propose a novel hinge-cross-entropy generative adversarial network (HCEGAN). The self-attention mechanism was added and improved to focus on the important information of the image. And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models. In this study, we implement the HCEGAN More > Graphic Abstract

Tongping Shen^1,2,*, Huanqing Xu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 539-560, 2023, DOI:10.32604/cmes.2022.022312

Abstract For the problems of complex model structure and too many training parameters in facial expression recognition algorithms, we proposed a residual network structure with a multi-headed channel attention (MCA) module. The migration learning algorithm is used to pre-train the convolutional layer parameters and mitigate the overfitting caused by the insufficient number of training samples. The designed MCA module is integrated into the ResNet18 backbone network. The attention mechanism highlights important information and suppresses irrelevant information by assigning different coefficients or weights, and the multi-head structure focuses more on the local features of the pictures, which More >

Hyun-Sam Shin¹, Jun-Ki Hong^2,*

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 235-248, 2023, DOI:10.32604/iasc.2023.033082

Abstract Recently, artificial-intelligence-based automatic customer response system has been widely used instead of customer service representatives. Therefore, it is important for automatic customer service to promptly recognize emotions in a customer’s voice to provide the appropriate service accordingly. Therefore, we analyzed the performance of the emotion recognition (ER) accuracy as a function of the simulation time using the proposed chunk-based speech ER (CSER) model. The proposed CSER model divides voice signals into 3-s long chunks to efficiently recognize characteristically inherent emotions in the customer’s voice. We evaluated the performance of the ER of voice signal chunks More >

Shoushi Wang¹, Jingping Shi², Ruike Sheng¹, Si Yu¹, Wei Xu^1,*

International Journal of Mental Health Promotion, Vol.24, No.6, pp. 933-944, 2022, DOI:10.32604/ijmhp.2022.023083

Abstract Academic procrastination among adolescents is an increasingly prominent problem. It is important to look for influences behind academic procrastination in the adolescent population. The present study aimed to reveal the explanatory mechanisms underlying the association between hope and academic procrastination behaviors among Chinese adolescents by testing the mediating role of attentional control and the moderating role of trait mindfulness. Participants in the current study were 1156 Chinese adolescents who completed self-report questionnaires on hope, attentional control, academic procrastination, and trait mindfulness. The results indicated that adolescent hope was negatively related to academic procrastination and that More >