Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

Journal on Artificial Intelligence, Vol.6, pp. 301-360, 2024, DOI:10.32604/jai.2024.054314 - 20 November 2024

Abstract Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent… More >

Xi Li^1,2, Weiwei Zhu², Qian Li^3,*, Changhui Hou^1,*, Yaozong Zhang¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 369-391, 2024, DOI:10.32604/cmc.2024.054982 - 15 October 2024

Abstract Automatically detecting learners’ engagement levels helps to develop more effective online teaching and assessment programs, allowing teachers to provide timely feedback and make personalized adjustments based on students’ needs to enhance teaching effectiveness. Traditional approaches mainly rely on single-frame multimodal facial spatial information, neglecting temporal emotional and behavioural features, with accuracy affected by significant pose variations. Additionally, convolutional padding can erode feature maps, affecting feature extraction’s representational capacity. To address these issues, we propose a hybrid neural network architecture, the redistributing facial features and temporal convolutional network (RefEIP). This network consists of three key components:… More >

Farhan Ullah¹, Xuexia Zhang^1,*, Mansoor Khan², Muhammad Abid^3,*, Abdullah Mohamed⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3373-3395, 2024, DOI:10.32604/cmc.2024.048656 - 15 May 2024

Abstract Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows. Traditional approaches frequently struggle with complex data and non-linear connections. This article presents a novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts. The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-Era Retrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms using in-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model, while a temporal convolutional network handles time-series complexities and data… More >

Haoran Huang, Weiting Chen^*, Zheming Fan

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3665-3681, 2024, DOI:10.32604/cmc.2024.048008 - 26 March 2024

Abstract Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address… More >

Kaicheng Liu^1,3,*, Chen Liang², Xiaoyang Dong², Liping Liu¹

Energy Engineering, Vol.121, No.4, pp. 933-949, 2024, DOI:10.32604/ee.2023.043658 - 26 March 2024

Abstract Due to the unpredictable output characteristics of distributed photovoltaics, their integration into the grid can lead to voltage fluctuations within the regional power grid. Therefore, the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios. This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic (PV) generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM). To begin with, an analysis of the spatiotemporal distribution patterns of More >

Zhenxiang He^*, Xunxi Wang, Chunwei Li

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 845-871, 2024, DOI:10.32604/cmc.2023.046607 - 30 January 2024

Abstract In the fast-evolving landscape of digital networks, the incidence of network intrusions has escalated alarmingly. Simultaneously, the crucial role of time series data in intrusion detection remains largely underappreciated, with most systems failing to capture the time-bound nuances of network traffic. This leads to compromised detection accuracy and overlooked temporal patterns. Addressing this gap, we introduce a novel SSAE-TCN-BiLSTM (STL) model that integrates time series analysis, significantly enhancing detection capabilities. Our approach reduces feature dimensionality with a Stacked Sparse Autoencoder (SSAE) and extracts temporally relevant features through a Temporal Convolutional Network (TCN) and Bidirectional Long… More >

Tao Liu¹, Kejia Zhang^1,*, Jingsong Yin¹, Yan Zhang¹, Zihao Mu¹, Chunsheng Li¹, Yanan Hu²

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2563-2582, 2023, DOI:10.32604/csse.2023.041228 - 28 July 2023

Abstract Spatio-temporal heterogeneous data is the database for decision-making in many fields, and checking its accuracy can provide data support for making decisions. Due to the randomness, complexity, global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions, traditional detection methods can not guarantee both detection speed and accuracy. Therefore, this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks. Firstly, the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted… More >

Muhammad Ishaq¹, Mustaqeem Khan^1,2, Soonil Kwon^1,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3355-3369, 2023, DOI:10.32604/csse.2023.037373 - 03 April 2023

Abstract Speech signals play an essential role in communication and provide an efficient way to exchange information between humans and machines. Speech Emotion Recognition (SER) is one of the critical sources for human evaluation, which is applicable in many real-world applications such as healthcare, call centers, robotics, safety, and virtual reality. This work developed a novel TCN-based emotion recognition system using speech signals through a spatial-temporal convolution network to recognize the speaker’s emotional state. The authors designed a Temporal Convolutional Network (TCN) core block to recognize long-term dependencies in speech signals and then feed these temporal More >

Hao Wu^1,*, Arun Kumar Sangaiah²

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 121-132, 2021, DOI:10.32604/iasc.2021.016457 - 17 March 2021

Abstract In oral English teaching in China, teachers usually improve students’ pronunciation by their subjective judgment. Even to the same student, the teacher gives different suggestions at different times. Students’ oral pronunciation features can be obtained from the reconstructed acoustic and natural language features of speech audio, but the task is complicated due to the embedding of multimodal sentences. To solve this problem, this paper proposes an English speech recognition based on enhanced temporal convolution network. Firstly, a suitable UNet network model is designed to extract the noise of speech signal and achieve the purpose of… More >