Engineering Applications of Artificial Intelligence最新文献

Adaptive model-agnostic meta-learning network for cross-machine fault diagnosis with limited samples 有限样本跨机故障诊断的自适应模型不可知元学习网络

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-12-03 DOI: 10.1016/j.engappai.2024.109748

Mingzhe Mu, Hongkai Jiang, Xin Wang, Yutong Dong

Deep learning-based methods have been extensively studied in rotating machinery defect diagnosis. However, training an accurate and robust diagnostic model is still a challenge under severe domain bias and limited samples. For this reason, a new adaptive model-agnostic meta-learning (AMAML) is proposed for cross-machine fault diagnosis with limited samples. First, a novel adaptive feature encode network is built, incorporating lightweight spatial-bilateral channel attention. This enables the network to extract critical fault information in multiple dimensions adaptively within limited samples, which improves the learning efficiency of generalized diagnostic knowledge. Then, an adaptive loss computation (ALC) method is devised, which inventively realizes the interaction between loss computation and model performance. The underfitting and overfitting dilemmas under few-shot conditions are tackled by ALC. Finally, an adaptive meta-optimization strategy is proposed for dynamically adapting the update strategy of the base learner, so that the model is always optimized in the direction of strong generalizability while obtaining high performance. Six cross-machine diagnosis tasks are conducted to verify the effectiveness of AMAML. The average diagnostic accuracy of the AMAML under the 5-shot setting reached 97.42%. Experiments confirm that AMAML is superior to other prevailing methods and is potentially promising for engineering applications.

基于深度学习的方法在旋转机械缺陷诊断中得到了广泛的研究。然而，在严重的领域偏差和有限的样本下，训练一个准确和鲁棒的诊断模型仍然是一个挑战。为此，提出了一种新的自适应模型不可知元学习（AMAML），用于有限样本的跨机器故障诊断。首先，构建了一种新的自适应特征编码网络，该网络结合了轻量级的空间双边信道关注。这使得网络能够在有限的样本范围内自适应提取多维度的关键故障信息，提高了广义诊断知识的学习效率。然后，设计了一种自适应损失计算（ALC）方法，创造性地实现了损失计算与模型性能之间的交互。该算法解决了少弹次条件下的欠拟合和过拟合问题。最后，提出了一种自适应元优化策略，对基础学习器的更新策略进行动态调整，使模型在获得高性能的同时始终朝着强泛化方向优化。通过六个跨机诊断任务来验证AMAML的有效性。5针组AMAML的平均诊断准确率为97.42%。实验证实，AMAML方法优于其他主流方法，具有潜在的工程应用前景。

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引用次数: 0

A multi-scale feature fusion network based on semi-channel attention for seismic phase picking 基于半信道关注的地震相位提取多尺度特征融合网络

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-12-03 DOI: 10.1016/j.engappai.2024.109739

Shuguang Zhao , Jiang Wang , Ping Huang , Fa Zhao , Fudong Zhang , Yadongyang Zhu

In the field of seismic data processing, deep learning technologies have been widely used for seismic phase picking. However, it is difficult to take full advantage of the features extracted at different stages in existing models. In this paper, a multi-scale feature fusion network was proposed for seismic phase picking to address this problem. In the stage of feature extraction, semi-channel attention is introduced. It improves the representation ability of the model by efficiently utilizing the feature information extracted from the encoder. In the stage of decoding, a channel compression module is designed to reduce the number of feature channels. It improves the receptive field of channels. Additionally, a multi-feature fusion module is presented to integrate features at multiple scales. It reduces the loss of useful information and improves the accuracy of phase picking. The effectiveness of our network is validated on Stanford earthquake dataset, where the picking errors for phase picking are 2 ms. The parameter of our network is only 52,100. Compared with earthquake transformer, it has 42.1% fewer time costs to process 12,656 test samples on Graphics Processing Unit.

在地震数据处理领域，深度学习技术已被广泛应用于地震相位提取。然而，现有模型很难充分利用在不同阶段提取的特征。为了解决这一问题，本文提出了一种多尺度特征融合网络用于地震相位提取。在特征提取阶段，引入了半通道关注。通过有效地利用从编码器中提取的特征信息，提高了模型的表示能力。在解码阶段，设计了信道压缩模块，减少特征信道的数量。它改善了通道的接受区。此外，提出了多特征融合模块，实现了多尺度特征的融合。减少了有用信息的丢失，提高了相位选择的精度。在斯坦福地震数据集上验证了我们网络的有效性，其中相位拾取的拾取误差为2 ms。我们的网络参数只有52,100。与地震变压器相比，在图形处理单元上处理12656个测试样本的时间成本降低了42.1%。

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引用次数: 0

Deep interval type-2 generalized fuzzy hyperbolic tangent system for nonlinear regression prediction 用于非线性回归预测的深度区间2型广义模糊双曲正切系统

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-12-03 DOI: 10.1016/j.engappai.2024.109737

Jianjian Zhao, Tao Zhao

Recently, due to the rapid rise of artificial intelligence (AI), considerable progress has been made in the field of nonlinear regression prediction. However, many existing methods suffer from the issues of rule and parameter explosion and poor accuracy, particularly for high-dimensional data with uncertainty. To address these limitations, this paper proposes a deep interval type-2 generalized fuzzy hyperbolic tangent system (DIT2GFHS). First, a novel neural network-based implementation of the interval type-2 fuzzy generalized fuzzy hyperbolic tangent system (IT2GFHS) is introduced to improve the efficiency of system parameter updates and optimization. Then, using a hierarchical and block-based framework, multiple IT2GFHSs are stacked layer by layer from bottom to top to construct the DIT2GFHS, with each layer’s fuzzy subsystems being independent of the others. Additionally, DIT2GFHS incorporates optimization algorithms and the Adam optimizer for training, thereby avoiding the tedious manual parameter tuning process. The detailed analysis of the construction manner and internal mechanisms for DIT2GFHS indicates that it features a reduced number of parameters, a transparent and clear structure, strong capability in handling uncertainty, and favorable accuracy. Notably, the small number of parameters and the explicit structure reduce computational and hardware burdens while maintaining interpretability. Finally, extensive experimental studies on both relatively low-dimensional and high-dimensional datasets are conducted. The results demonstrate that DIT2GFHS achieves excellent performance with fewer parameters compared to many deep-structured models, including deep fuzzy systems and deep learning models. This highlights its potential impact in addressing practical nonlinear regression problems.

近年来，由于人工智能（AI）的迅速崛起，非线性回归预测领域取得了长足的进展。然而，现有的许多方法存在规则和参数爆炸以及精度差的问题，特别是对于具有不确定性的高维数据。针对这些局限性，本文提出了一种深区间2型广义模糊双曲切线系统（DIT2GFHS）。首先，提出了一种基于神经网络的区间2型模糊广义模糊双曲切线系统（IT2GFHS），提高了系统参数更新和优化的效率。然后，采用层次化、分块化的框架，将多个it2gfhs从下到上逐层堆叠构成DIT2GFHS，各层模糊子系统相互独立；此外，DIT2GFHS结合了优化算法和Adam优化器进行训练，从而避免了繁琐的手动参数调整过程。对DIT2GFHS的构造方式和内部机理进行了详细分析，结果表明，其参数数量少，结构透明清晰，处理不确定性的能力强，精度好。值得注意的是，少量的参数和明确的结构在保持可解释性的同时减少了计算和硬件负担。最后，在相对低维和高维数据集上进行了广泛的实验研究。结果表明，与许多深度结构化模型（包括深度模糊系统和深度学习模型）相比，DIT2GFHS在参数较少的情况下取得了优异的性能。这突出了它在解决实际非线性回归问题方面的潜在影响。

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引用次数: 0

Enhancing camouflaged object detection through contrastive learning and data augmentation techniques 通过对比学习和数据增强技术增强伪装目标检测

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-12-02 DOI: 10.1016/j.engappai.2024.109703

Cunhan Guo , Heyan Huang

Camouflaged object detection (COD) aims to locate and segment objects that blend into their surroundings, presenting significant challenges due to the high similarity between the objects and their background. This work introduces a novel approach, Contrastive Learning with Augmented Data (CLAD), which enhances COD performance by leveraging contrastive learning and data augmentation. Our method formulates a simplified task by placing camouflaged objects in new environments, creating positive and negative samples for contrast learning. This process strengthens the model’s ability to differentiate camouflaged objects from complex backgrounds. Furthermore, we introduce a concatenated feature enhancement module to integrate and enrich multi-scale features, improving the overall expressive power of the model. Extensive experiments on four benchmark datasets demonstrate that CLAD outperforms state-of-the-art COD methods, and its effectiveness extends to salient object detection tasks, achieving competitive results across multiple metrics.

伪装目标检测（COD）旨在定位和分割融入周围环境的物体，由于物体与其背景之间的高度相似性，这带来了重大挑战。这项工作引入了一种新的方法，增强数据对比学习（CLAD），它通过利用对比学习和数据增强来提高COD性能。我们的方法制定了一个简化的任务，将伪装的物体放置在新的环境中，为对比学习创建积极和消极的样本。这个过程增强了模型从复杂背景中区分伪装物体的能力。此外，我们引入了一个串联的特征增强模块来整合和丰富多尺度特征，提高模型的整体表达能力。在四个基准数据集上进行的大量实验表明，CLAD优于最先进的COD方法，其有效性扩展到突出的目标检测任务，在多个指标上取得了具有竞争力的结果。

引用次数: 0

Ship fuel consumption prediction based on transfer learning: Models and applications 基于迁移学习的船舶燃料消耗预测：模型与应用

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-12-02 DOI: 10.1016/j.engappai.2024.109769

Xi Luo , Mingyang Zhang , Yi Han , Ran Yan , Shuaian Wang

Data-driven fuel consumption rate (FCR) prediction models largely depend on the amount of training data, which can be scarce for new ships with limited operating time. To tackle this issue, we implement three transfer learning strategies to leverage knowledge from another seven container ships to construct artificial neural network (ANN)-based FCR prediction models for a target ship with limited data. Numerical experiments reveal that the ANN models incorporating the three transfer strategies outperform the model trained solely on the target ship data, reducing mean absolute percentage error by 12.57%, 6.44%, and 16.03%, respectively. This study also investigates the impacts of target dataset size on the performance of transfer strategies using ship FCR prediction as an example, revealing that the smaller amount of available data, the greater improvement in prediction accuracy using the transfer strategy. These insights contribute to the development of effective operational solutions for enhancing ship energy efficiency and promoting sustainable shipping practices.

数据驱动的燃油消耗率（FCR）预测模型在很大程度上依赖于训练数据的数量，对于运营时间有限的新船来说，训练数据可能是稀缺的。为了解决这一问题，我们实施了三种迁移学习策略，利用其他七艘集装箱船的知识构建基于人工神经网络（ANN）的目标船FCR预测模型。数值实验结果表明，结合三种转移策略的人工神经网络模型比仅针对目标船数据训练的模型性能更好，平均绝对百分比误差分别降低了12.57%、6.44%和16.03%。本研究还以船舶FCR预测为例，探讨了目标数据集大小对迁移策略性能的影响，发现可用数据量越少，使用迁移策略的预测精度提高越大。这些见解有助于制定有效的运营解决方案，以提高船舶能源效率和促进可持续航运实践。

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引用次数: 0

A heterogeneous transfer learning method for fault prediction of railway track circuit 铁路轨道电路故障预测的异构迁移学习方法

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-12-01 DOI: 10.1016/j.engappai.2024.109740

Lan Na , Baigen Cai , Chongzhen Zhang , Jiang Liu , Zhengjiao Li

Prediction and identification of faults in track circuits are crucial for improving the safety and efficiency of railway transportation. However, due to the absence of real data, the task of track circuit fault prediction through deep learning methods facing significant challenges. This paper proposed a novel heterogeneous transfer learning network structure for track circuit deep learning fault prediction. The proposed transfer learning network can reduce the reliance on track circuit data in the process of deep learning models training by utilizing public datasets from other similar tasks. In this paper, an index describing the data distribution is used to demonstrate the transferability between heterogeneous data firstly. Then a heterogeneous transfer learning network structure is proposed to help the deep learning model training on the track circuit fault prediction task. Finally, the effect of transfer learning is comprehensively examined. The simulation experimental results show that the proposed heterogeneous transfer learning network structure can transfer useful knowledge in other similar fields for tasks in track circuit fault prediction, and the resulting model can distinguish between nine different classes with a high accuracy level over 99% on the test dataset while reducing the amount of required training data to 10% of the traditional training methods.

轨道电路故障的预测和识别对于提高铁路运输的安全性和效率至关重要。然而，由于缺乏真实数据，通过深度学习方法进行轨道电路故障预测的任务面临着很大的挑战。提出了一种新的异构迁移学习网络结构，用于轨道电路深度学习故障预测。所提出的迁移学习网络可以通过利用来自其他类似任务的公共数据集来减少深度学习模型训练过程中对轨道电路数据的依赖。本文首先用一个描述数据分布的索引来说明异构数据之间的可移植性。然后，提出了一种异构迁移学习网络结构，以帮助深度学习模型训练轨道电路故障预测任务。最后，全面考察了迁移学习的效果。仿真实验结果表明，所提出的异构迁移学习网络结构可以将其他类似领域的有用知识转移到轨道电路故障预测任务中，所得到的模型可以在测试数据集上区分9个不同的类别，准确率超过99%，同时将所需的训练数据量减少到传统训练方法的10%。

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引用次数: 0

Multimodal transformer for early alarm prediction 多模态变压器早期报警预测

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-30 DOI: 10.1016/j.engappai.2024.109643

Nika Strem , Devendra Singh Dhami , Benedikt Schmidt , Kristian Kersting

Alarms are an essential part of distributed control systems designed to help plant operators keep the processes stable and safe. In reality, however, alarms are often noisy and thus can be easily overlooked. Early alarm prediction can give the operator more time to assess the situation and introduce corrective actions to avoid downtime and negative impact on human safety and environment. Existing studies on alarm prediction typically rely on signals directly coupled with these alarms. However, using more sources of information could benefit early prediction by letting the model learn characteristic patterns in the interactions of signals and events. Meanwhile, multimodal deep learning has recently seen impressive developments. Combination (or fusion) of modalities has been shown to be a key success factor, yet choosing the best fusion method for a given task introduces a new degree of complexity, in addition to existing architectural choices and hyperparameter tuning. This is one of the reasons why real-world problems are still typically tackled with unimodal approaches. To bridge this gap, we introduce a multimodal Transformer model for early alarm prediction based on a combination of recent events and signal data. The model learns the optimal representation of data from multiple fusion strategies automatically. The model is validated on real-world industrial data. We show that our model is capable of predicting alarms with the given horizon and that the proposed multimodal fusion method yields state-of-the-art predictive performance while eliminating the need to choose among conventional fusion techniques, thus reducing tuning costs and training time.

警报是分布式控制系统的重要组成部分，旨在帮助工厂操作员保持过程的稳定和安全。然而，在现实中，警报往往是嘈杂的，因此很容易被忽视。早期报警预测可以给操作人员更多的时间来评估情况，并采取纠正措施，以避免停机和对人类安全和环境的负面影响。现有的警报预测研究通常依赖于与这些警报直接耦合的信号。然而，使用更多的信息来源可以让模型在信号和事件的相互作用中学习特征模式，从而有利于早期预测。与此同时，多模态深度学习最近取得了令人印象深刻的进展。模式的组合（或融合）已被证明是成功的关键因素，然而，除了现有的架构选择和超参数调优之外，为给定任务选择最佳的融合方法还引入了新的复杂性程度。这就是为什么现实世界的问题通常仍然用单模方法来解决的原因之一。为了弥补这一差距，我们引入了一种基于近期事件和信号数据组合的多模态变压器模型，用于早期报警预测。该模型从多种融合策略中自动学习数据的最优表示。该模型在实际工业数据上得到了验证。我们表明，我们的模型能够预测具有给定视界的警报，并且所提出的多模态融合方法产生了最先进的预测性能，同时消除了在传统融合技术中进行选择的需要，从而减少了调谐成本和训练时间。

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引用次数: 0

A large-scale group decision making model with a clustering algorithm based on a locality sensitive hash function 基于位置敏感哈希函数的聚类算法的大规模群体决策模型

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-30 DOI: 10.1016/j.engappai.2024.109697

Zhangqian Mu , Yuanyuan Liu , Youlong Yang

With the development of science and technology, an expanding array of decision-makers across various fields, including engineering and medicine, have been participating in collaborative decision-making for complex scenarios, such as earthquake relief and disease containment. The rapidly changing dynamics of real-world decision-making and the high complexity of consensus reaching among decision-makers require the development of more sophisticated models to handle these challenges. Considering the diversity and stability of group categories, this study proposes a large-scale group decision-making model based on a locality sensitive hash function. First, the volatility of attributes in real scenarios is considered, and a time-series decision matrix is constructed based on the average growth rates to make the results closer to reality. Then, hash functions are used to map the decision opinions to different dimensions and express the similarity through the Hamming distance, yielding clustering results with high stability and cohesion. To determine whether the decision-making group can reach a consensus, this study conducts hypothesis testing, adopting the idea of small probability counterfactuals to provide objective and fair standards for threshold judgment. Finally, through the case study and comparative analysis, it is proved that the proposed method improved 26.4% and 4.2% under the criteria of integrated cohesion and global consensus degree, respectively, with better clustering effect.

随着科学技术的发展，包括工程和医学在内的各个领域越来越多的决策者已经参与到地震救援和疾病控制等复杂情景的协同决策中。现实世界决策的快速变化动态和决策者之间达成共识的高度复杂性需要开发更复杂的模型来处理这些挑战。考虑到群体类别的多样性和稳定性，本文提出了一种基于局部敏感哈希函数的大规模群体决策模型。首先，考虑属性在真实场景中的波动性，基于平均增长率构造时间序列决策矩阵，使结果更接近真实；然后，利用哈希函数将决策意见映射到不同维度，并通过汉明距离表示相似度，得到具有高稳定性和高内聚性的聚类结果。为了确定决策群体是否能够达成共识，本研究进行假设检验，采用小概率反事实的思想，为阈值判断提供客观公正的标准。最后，通过案例研究和对比分析，证明该方法在综合凝聚力和全局共识度标准下分别提高了26.4%和4.2%，具有较好的聚类效果。

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引用次数: 0

Solving dynamic multi-objective optimization problem of immersed tunnel elements via multi-source evolutionary information clustering method 采用多源进化信息聚类方法求解沉管构件动态多目标优化问题

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-30 DOI: 10.1016/j.engappai.2024.109741

Qinqin Fan , Wentao Huang , Moduo Yu , Qirong Tang , Qingchao Jiang

Dynamic multi-objective optimization problems (DMOPs) are time- and space-varying, thus maintaining/improving the uncertainty degree of evolutionary information (i.e., information entropy) in the population and providing useful knowledge are two important tasks to make dynamic multi-objective evolutionary algorithms (DMOEAs) adapt to changing environments. To achieve the above objectives, a multi-source population clustering (MPC) method is proposed to assist DMOEAs in improving their tracking performance during the full-cycle optimization in the current study. In the MPC, three different information sources are used to provide diverse spatiotemporal evolutionary information, aiding DMOEAs in adapting to various changing environments. Subsequently, an enhanced spectral clustering approach is employed to group all evolutionary individuals from different information sources into many clusters/subspaces. Finally, the selected DMOEA is employed to search all subspaces in parallel via the high-performing computing method. The MPC is incorporated into a regularity model-based multi-objective estimation of distribution algorithm (called as MPC-RM-MEDA) and is compared with six famous DMOEAs on 14 10- and 30-dimensional DMOPs, which are proposed in IEEE Congress on Evolutionary computation 2018. Experimental results demonstrate that the overall tracking performance of the proposed MPC-RM-MEDA is significantly superior to that of other selected competitors in various dynamic environments. Additionally, the MPC-RM-MEDA is utilized to address a real-world DMOP involving an immersed tunnel element. The obtained results and comparison with the knee point-based transfer learning method verify that the MPC is an efficient and dependable approach for enhancing the tracking performance of other DMOEAs in solving actual DMOPs.

动态多目标优化问题具有时变和空变的特点，维持/提高种群中进化信息的不确定性（即信息熵）和提供有用的知识是动态多目标进化算法适应环境变化的两大重要任务。为了实现上述目标，本研究提出了一种多源种群聚类（MPC）方法，以帮助dmoea在全周期优化过程中提高跟踪性能。在MPC中，利用三种不同的信息源提供不同的时空演化信息，帮助dmoea适应各种变化的环境。随后，采用一种改进的谱聚类方法，将来自不同信息源的进化个体划分到多个聚类/子空间中。最后，利用选择的DMOEA，通过高性能计算方法对所有子空间进行并行搜索。将MPC纳入基于规则模型的多目标分布估计算法（称为MPC- rm - meda），并与IEEE进化计算大会2018上提出的6个著名的基于14个10维和30维dmop的dmoea进行比较。实验结果表明，在各种动态环境下，所提出的MPC-RM-MEDA的整体跟踪性能明显优于其他选定的竞争对手。此外，MPC-RM-MEDA还可用于解决涉及浸入式隧道元件的真实DMOP问题。通过与基于膝点的迁移学习方法的比较，验证了MPC在求解实际dmop时，是一种有效、可靠的方法，可以提高其他dmoea的跟踪性能。

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引用次数: 0

Lightweight advanced deep-learning models for stress detection on social media 用于社交媒体压力检测的轻量级高级深度学习模型

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-30 DOI: 10.1016/j.engappai.2024.109720

Mohammed Qorich, Rajae El Ouazzani

Nowadays, stress reveals itself as a ubiquitous presence, manifesting in novel forms in our modern daily life. Indeed, digital platforms and social media collect various impressions, reactions, and feelings that could provide valuable real-time sentiment data. Nevertheless, understanding stress and mental states among people is difficult because it relies on self-reporting and detecting related expressions, statements, and articulations. In this paper, we consider extracting nuanced insights and stress expressions from Reddit and Twitter posts using lightweight advanced deep-learning methods and Bidirectional Encoder Representations from Transformers (BERT) embeddings. Our findings highlight the potency of transformer BERT models, whether utilized as embedding feature extractors or as text sentiment classifiers. Moreover, the proposed lightweight deep architectural models promoted the field of stress detection in social media, achieving high classification performance. Practically, the BERT Electra model reached 85.67% accuracy on the small Reddit dataset, while our Convolutional Neural Network (CNN) model obtained 97.62% on the large Twitter dataset. Our contributions are not only restricted to the scientific understanding of stress but also extend to the well-being of individuals and global mental health.

如今，压力无处不在，在我们的现代日常生活中以新的形式表现出来。事实上，数字平台和社交媒体收集了各种各样的印象、反应和感受，可以提供有价值的实时情绪数据。然而，理解人们的压力和精神状态是困难的，因为它依赖于自我报告和检测相关的表达、陈述和发音。在本文中，我们考虑使用轻量级高级深度学习方法和来自变形金刚（BERT）嵌入的双向编码器表示从Reddit和Twitter帖子中提取细微的见解和压力表达式。我们的研究结果强调了变形BERT模型的效力，无论是用作嵌入特征提取器还是用作文本情感分类器。此外，提出的轻量级深度架构模型推动了社交媒体应力检测领域的发展，实现了较高的分类性能。实际上，BERT Electra模型在小型Reddit数据集上的准确率达到了85.67%，而我们的卷积神经网络（CNN）模型在大型Twitter数据集上的准确率达到了97.62%。我们的贡献不仅限于对压力的科学理解，而且还扩展到个人和全球心理健康的福祉。

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引用次数: 0