EAI Endorsed Transactions on e-Learning
https://publications.eai.eu/index.php/el
<p>EAI Endorsed Transactions on e-Learning is open access, a peer-reviewed scholarly journal focused on topics belonging to the variegated and engaging e-Learning landscape, ranging from various types of distance learning (e.g., online, mobile, cloud, hybrid) to virtual laboratory environments supported by sound pedagogies, cutting-edge technologies and much more. The journal publishes research, review, commentaries, editorials, technical articles, and short communications with a triannual frequency. Authors are not charged for article submission and processing.</p> <p><strong>INDEXING</strong>: DOAJ, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions</p>European Alliance for Innovation (EAI)en-USEAI Endorsed Transactions on e-Learning2032-9253<p>This is an open-access article distributed under the terms of the Creative Commons Attribution <a href="https://creativecommons.org/licenses/by/3.0/" target="_blank" rel="noopener">CC BY 3.0</a> license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.</p>EEG Emotion Recognition Based on Self-Distillation Convolutional Graph Attention Network
https://publications.eai.eu/index.php/el/article/view/4974
<p>A convolution graph attention model based on self-distillation convolutional graph attention network (SDC-GAT) is proposed for multi-channel EEG emotion recognition. Firstly, two-dimensional feature matrix based on EEG time-domain features are constructed, and the matrix is fed into the graph attention neural network to learn the internal connections between electrical brain channels located in different brain regions. Meanwhile, the three-dimensional feature matrix is constructed according to the relative positions of the electrode channels, and the self-distillation network is employed to extract local high-level abstract features containing electrode spatial position information from the three-dimensional feature matrix. Finally, outputs of the two networks are integrated to determine the emotional states. Experiments were performed on the DEAP dataset. The experimental results show that the spatial domain information of the electrode channel and the internal connection relationship between different channels are beneficial for emotion recognition. In addition, the proposed model can effectively fuse these information to improve the performance of multi-channel EEG emotion recognition.</p>Hao ChaoShuqi Feng
Copyright (c) 2024 EAI Endorsed Transactions on e-Learning
https://creativecommons.org/licenses/by/3.0/
2024-03-082024-03-081010.4108/eetel.4974Artificial Intelligence in Mathematical Modeling of Complex Systems
https://publications.eai.eu/index.php/el/article/view/5256
<p>This article introduces artificial intelligence techniques in mathematical modelling of complex systems and their applications. Mathematical modelling of complex systems is a method of studying the structure and behaviour of complex systems, aiming to understand interactions and nonlinear effects in the system. Commonly used modelling methods include system dynamics, network theory, and algebraic methods. Artificial intelligence technologies include machine learning and deep learning, which can be used for tasks such as prediction and classification, anomaly detection, optimization and decision-making. In mathematical modelling of complex systems, artificial intelligence technology can learn system patterns and laws from large amounts of data, and can be applied to image and speech recognition, time series analysis and other fields. Deep learning and machine learning are important branches of artificial intelligence. They realize the modelling and analysis of complex systems by building neural network models. Data-driven modelling is a modelling method based on actual data that, combined with traditional theoretical modelling, can better describe and predict the behaviour of complex systems. Self-control of complex systems means that the system realizes its own optimization and adjustment through adaptive control algorithms and feedback mechanisms. In summary, artificial intelligence technology has broad application prospects in mathematical modelling of complex systems and will provide new tools and methods for in-depth understanding and solving problems in complex systems.</p>Ting Zhao
Copyright (c) 2024 EAI Endorsed Transactions on e-Learning
https://creativecommons.org/licenses/by/3.0/
2024-03-262024-03-261010.4108/eetel.5256Gesture Recognition Based on Deep Learning: A Review
https://publications.eai.eu/index.php/el/article/view/5191
<p style="font-weight: 400;">Gesture recognition is an important and inevitable technology in modern times, its appearance and improvement greatly improve the convenience of people's lives, but also enrich people's lives. It has a wide range of applications in various fields. In daily life, it can carry out human-computer interaction and the use of smart home. In terms of medical treatment, it can help patients to recover and assist doctors to carry out experiments. In terms of entertainment, it allows users to interact with the game in an immersive manner. This paper chooses three technologies that deep learning plays a more prominent role in gesture recognition, namely CNNs, LSTM and transfer learning based on deep learning. They each have their own advantages and disadvantages. Because of the different principles of use, different techniques have different roles, such as CNNs can carry out feature extraction, LSTM can deal with long time series, transfer learning can transfer what is learned from another task to this task. Select different practical technologies according to different application scenarios, and make improvements in real time in practical applications. Gesture recognition based on deep learning has the advantages of good accuracy, robustness and real-time implementation, but it also bears the disadvantages of huge economic and time costs and high hardware requirements. Despite some challenges, researchers continue to optimize and improve the technology, and believe that in the future, gesture recognition technology will be more mature and valuable.</p>Meng Wu
Copyright (c) 2024 EAI Endorsed Transactions on e-Learning
https://creativecommons.org/licenses/by/3.0/
2024-03-072024-03-071010.4108/eetel.5191Liver tumor segmentation method based on U-Net architecture: a review
https://publications.eai.eu/index.php/el/article/view/5263
<p style="margin: 0cm; text-align: justify; text-justify: inter-ideograph; text-indent: 21.0pt;"><span lang="EN-US" style="font-size: 10.0pt; font-family: 'Times New Roman',serif;">Liver cancer is a disease with a high incidence and high probability of deterioration, and for the rapid diagnosis of liver disease, CT scans must be used to segment the liver tumors. For the past few years, with the rapid development of deep learning, many deep learning methods for liver tumor segmentation using abdominal computed tomography (CT) images have appeared, and the clinical application of these methods is of important significance for computer-aided diagnosis of liver tumors. The U-Net, with its unique U-shape network structure, exhibits excellent performance in medical image segmentation field and has been extensively utilized in various medical image segmentation applications. In this paper, we summarize the researches of U-Net and its improved networks in CT image segmentation of liver tumors by deep learning methods and classify various U-Net-based convolutional neural networks (CNNs) into 2D (two-dimensional), 3D (three-dimensional), and 2.5D (2.5-dimensional). In this paper, 2D, 3D, and 2.5D convolutional neural networks are summarized. In addition, this paper summarizes the advantages and disadvantages as well as the improvement methods of each type of network, which provides a useful reference for the studies of deep learning based on liver tumor segmentation field. Finally, this paper envisions future research trends for deep learning segmentation methods in the context of liver tumors.</span></p>Biao WangChunfeng Yang
Copyright (c) 2024 EAI Endorsed Transactions on e-Learning
https://creativecommons.org/licenses/by/3.0/
2024-03-182024-03-181010.4108/eetel.5263