检索结果-内蒙古大学图书馆

An autoencoder-based recommendation framework toward cold start problem

JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-24页

作者： Zhou, Wang Tian, Ying Haq, Amin Ul Ahmad, Sultan Xihua Univ Sch Comp & Software Engn Chengdu Peoples R China Sichuan Police Coll Intelligent Policing Key Lab Luzhou Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Alkharj Saudi Arabia

Recommender methods have been effectively used in both academic and industrial settings. However, the cold start problem with scarce prior information has become the barrier hindering recommender systems from gaining further improvements. To overcome this issue, this article proposes a novel autoencoder framework referred to as CSRec, which owns the merits of both neural networks and collaborative filtering. Specifically, to search the nearest neighbors for cold start items, CSRec learns item representation and carries out clustering for items via k-means++ method. After that, with the nearest item cluster, CSRec could perform rating prediction for the cold start items through the autoencoder architecture, which could reconstruct the input space directly. Identically, CSRec could also perform cold start recommendations for users through the neural network. In practice, with the autoencoder architecture, CSRec owns powerful capability in computation and representation, which could deeply exploit the inner relationship for items and yield high performance in addressing cold start issues. Moreover, it could enhance the novelty and diversity of cold start recommendations. Experiments on CiaoDVDs and DoubanMovie certificate the superiority of CSRec in addressing the cold start issue, which could yield accurate performance in terms of RMSE, MAE and top-K and outperform other benchmark recommender approaches significantly.

关键词： Neural network Cold start autoencoder Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Stacked dynamic target regularization enhanced autoencoder for soft sensor in industrial processes

引用

CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2025年第3期103卷 1335-1348页

作者： Guo, Xiaoping Zhao, Xiaofeng Li, Yuan Shenyang Univ Chem Technol Coll Informat Engn Shenyang Peoples R China

Stacked autoencoders (SAEs) have great potential in developing soft sensors due to their excellent feature extraction capabilities. However, the pre-training stage of SAE is unsupervised and some important information related to target variables may be discarded. Meanwhile, as the depth of the network increases, reconstruction errors continue to accumulate, resulting in incomplete feature representations of the original input. In addition, the dynamic nature of the data affects the predictive results of the model. To address these issues, the stacked dynamic target regularization enhanced autoencoder (SDTR-EAE) method is proposed, which adds the DTR and the original input information layer by layer to enhance the feature extraction. To adapt to the dynamic changes in data and extract target-related features, entropy weight grey relational analysis (EW-GRA) is used as the DTR term to constrain the weight matrix and suppress irrelevant features. To reduce the accumulation of information loss during the reconstruction, an information enhancement layer is introduced, where the original inputs and the information of the hidden layers of previous DTR-EAE units are added to the follow-up DTR-EAE unit. Finally, in the regression process, the DTR term is used again to fully utilize depth features for quality prediction and prevent overfitting. Experimental verifications using the debutanizer column and thermal power plant are conducted to validate the effectiveness of the proposed modelling method.

关键词： autoencoder dynamic target regularization entropy weight grey relational analysis soft sensor

来源：评论

学校读者我要写书评

暂无评论

Baseline optimized autoencoder-based unsupervised anomaly detection in uncontrolled dynamic structural health monitoring

引用

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年

作者： Yang, Kang Liu, Tianqi Yang, Zekun Zhou, Yang Tian, Zhihui Kim, Nam H. Harley, Joel B. Univ Florida Dept Elect & Comp Engn POB 116200216 Larsen Hall Gainesville FL 32608 USA Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL USA Univ Florida Dept Mech & Aerosp Engn Gainesville FL USA

autoencoder reconstruction-based unsupervised anomaly detection is widely used in structural health monitoring. However, these methods typically require training on historical data from healthy structures, collected under environmental conditions similar to the test data. This limits their practical use, as it demands a comprehensive dataset of historical guided waves gathered across various environmental and operational conditions. Additionally, these methods fail when the training data contain a significant portion of damage-induced guided waves, as the autoencoder may reconstruct damaged waves just as effectively as normal ones. To overcome these challenges, our anomaly detection model is trained directly on current measurements, eliminating the risk of environmental discrepancies between training and test data. Furthermore, our baseline optimization strategy biases the autoencoder toward reconstructing normal guided waves, enabling reliable anomaly detection even when a large proportion of the training data are damage-induced waves. Additionally, we present a strategy to enhance the model's practical performance by optimizing the weight factor for baseline loss and the baseline set size, based on guided wave reconstruction performance, without relying on damage labels. The effectiveness of this baseline-optimized autoencoder model, even when the training data contain significant damage-induced guided waves, is validated through measurements from 10 regions, each spanning 80 days of guided wave data collected under uncontrolled and dynamic environmental conditions.

关键词： Structural health monitoring guided waves anomaly detection autoencoder baseline optimization dynamic environmental conditions

来源：评论

学校读者我要写书评

暂无评论

Multivariate Time Series Anomaly Detection Using Working Memory Connections in Bi-Directional Long Short-Term Memory autoencoder Network

引用

APPLIED SCIENCES-BASEL 2025年第5期15卷 2861-2861页

作者： Ding, Xianghua Wang, Jingnan Liu, Yiqi Jung, Uk Dongguk Univ Seoul Sch Business Dept Business Adm Seoul 04620 South Korea

"Normal" events are characterized as data patterns or behaviors that align with expected operational conditions, while "anomalies" are defined as deviations from these patterns, potentially signaling faults, errors, or unexpected system behaviors. The timely and accurate detection of anomalies plays a critical role in domains such as industrial manufacturing, financial transactions, and other related domains. In the context of Industry 4.0, the proliferation of sensors has resulted in a massive influx of time series data, making the anomaly detection of such multivariate time series data a popular research area. Long Short-Term Memory (LSTM) has been extensively recognized as an effective framework for modeling and processing time series data. Previous studies have combined Bi-directional Long Short-Term Memory (Bi-LSTM) architecture with autoencoder (AE) for multivariate time series anomaly detection. However, due to the inherent limitations of LSTM, Bi-LSTM-AE still cannot overcome these drawbacks. Our study replaces the LSTM units within the Bi-LSTM-AE architecture of existing research with Working Memory Connections for LSTM units and demonstrates that this architecture performs better in the field of multivariate time series anomaly detection compared to using standard LSTM units. The model we proposed not only outperforms the baseline models but also demonstrates greater robustness across various scenarios.

关键词： anomaly detection long short-term memory time series data autoencoder multivariate time series

来源：评论

学校读者我要写书评

暂无评论

Fault detection for high-speed train traction system using autoencoder-Fréchet inception distance

引用

MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第4期36卷 046205-046205页

作者： Zhang, Cheng Lao, Yu-Yu Deng, Cheng-Long Li, Yuan Shenyang Univ Chem Technol Coll Sci Shenyang Liaoning Peoples R China Shenyang Univ Chem Technol Coll Comp Sci & Technol Shenyang Liaoning Peoples R China Shenyang Univ Chem Technol Coll Informat Engn Shenyang Liaoning Peoples R China

The detection of the process state in the traction system plays a crucial role in ensuring the safe operation of high-speed trains. However, the running data in the traction system exhibits nonlinear and dynamic nature, which leads to the failure of traditional detection methods. To address the above problems, a novel fault detection method based on autoencoder-Fr & eacute;chet inception distance (AE-FID) is proposed in this paper. Firstly, autoencoder (AE) is used for feature extraction to capture the inherent nonlinear structure in the data. Secondly, a novel fault detection statistic is developed by combining sliding window technology and Fr & eacute;chet inception distance (FID) to eliminate the influence of data dynamics. Finally, the proposed statistic is utilized to detect faults in the traction system, and the occurrence of faults is measured in terms of structural differences to mitigate the influence of the masking effects. The proposed method is evaluated through a numerical example that simulates dynamic nonlinear systems and the Traction Drive Control System-Fault Injection Benchmark simulation platform of a high-speed train. The experimental results demonstrate the effectiveness of the proposed method in fault detection for traction systems.

关键词： traction system fault detection autoencoder Fr & eacute chet inception distance sliding window

来源：评论

学校读者我要写书评

暂无评论

iBhb-Lys: Identify lysine (3-hydroxybutyrylation sites using autoencoder feature representation and fuzzy SVM algorithm

引用

ANALYTICAL BIOCHEMISTRY 2025年 697卷 115715页

作者： Ju, Zhe Zhang, Qing-Bao Shenyang Aerosp Univ Coll Sci Shenyang 110136 Peoples R China

Lysine (3-hydroxybutyrylation (Kbhb) is newly discovered (3-hydroxybutyrylate-derived histone modification which has been associated with the pathogenesis of many human diseases. To further elucidate the biological significance and molecular mechanism of Kbhb, it is necessary to accurately identify the Kbhb sites from protein sequences. In this study, a novel computational model named iBhb-Lys is developed for the identification of Kbhb sites. Four types of features are combined to encode each Kbhb site as a 3266-dimensional feature vector. And the autoencoder network is used to reduce the dimensionality of feature space, due to the high dimensionality of the combined features. In addition, to effectively reduce the influence of noise and outlier on classification, a new fuzzy support vector machine algorithm is proposed by incorporating the density around the sample into the fuzzy membership function. As illustrated by independent test, the AUC value of iBhb-Lys has increased by 2.22 % compared to the existing predictor KbhbXG. Feature analysis shows that some amino acid composition features, such as the occurrence frequency of leucine and histidine residues around Kbhb sites, contribute profoundly to the identification of Kbhb sites. The conclusions drawn in this study may provide useful reference for studying the molecular mechanism of Kbhb.

关键词： Post-translational modification Feature extraction autoencoder Fuzzy support vector machine (3-hydroxybutyrylation

来源：评论

学校读者我要写书评

暂无评论

Intelligent evaluation method for identifying favorable shale oil areas based on improved stacked sparse autoencoder

引用

OIL SHALE 2025年第1期42卷 79-114页

作者： Xu, Rui Yan, Tie Sun, Shihui Qu, Jingyu Hou, Zhaokai Northeast Petr Univ Sanya Offshore Oil & Gas Res Inst Sanya 572025 Peoples R China Northeast Petr Univ Sch Petr Engn Daqing 163318 Peoples R China

Identifying and evaluating favorable areas is crucial for shale oil exploration and development, well-location deployment, and fracturing design. Traditional machine learning methods struggle to accurately extract the characteristics of favorable shale oil areas with limited labeled data, affecting accuracy and generalization. This study proposes an intelligent method for identifying favorable shale oil areas under semi-supervised learning (SSAEplus) to identify and evaluate favorable shale oil areas of the Qingshankou Formation in the Songliao Basin. The experimental results show that this method can effectively overcome the favorable area identification models' reliance on labeled data and can adaptively extract the characteristics of favorable shale oil areas without supervision. The accuracy of model identification is as high as 98.82%. Compared with other methods, the SSAE-plus yields higher accuracy and efficiency, while being more stable and generalizable. The SSAE-plus achieved over 95% accuracy in identifying favorable shale oil areas across six datasets. It has broad application prospects in identifying and evaluating favorable areas, and provides valuable theoretical insights for shale oil development and exploration well layout.

关键词： shale oil favorable area autoencoder batch normalization semi- supervised learning

来源：评论

学校读者我要写书评

暂无评论

AutoEffFusionNet: A New Approach for Cervical Cancer Diagnosis Using ResNet-Based autoencoder With Attention Mechanism and Genetic Feature Selection

引用

IEEE ACCESS 2025年 13卷 44107-44122页

作者： Dogan, Yahya Siirt Univ Comp Engn Dept TR-56100 Siirt Turkiye

Cervical cancer poses a significant global health challenge, necessitating accurate and efficient diagnostic solutions. This study introduces a novel hybrid framework, AutoEffFusionNet, that integrates unsupervised feature learning through ResNet-based autoencoders with attention mechanisms and supervised learning via transfer learning models. By leveraging the complementary strengths of these approaches, the proposed method achieves enhanced diagnostic accuracy in cervical cancer classification. Genetic algorithms optimize the feature selection process, retaining only the most relevant attributes, thereby addressing feature redundancy and improving computational efficiency. The selected features are then classified using a Support Vector Machine, effectively combining deep learning's feature extraction capabilities with machine learning's robust classification strengths. Additionally, Grad-CAM visualizations are incorporated to highlight critical regions influencing the classification decisions, enhancing interpretability and transparency. The framework was rigorously evaluated on two benchmark datasets, SIPaKMeD, and Mendeley LBC, achieving remarkable accuracies of 99.26% and 100%, respectively. These results demonstrate the effectiveness of the proposed model in addressing key challenges in cervical cancer diagnosis and its potential for deployment in clinical applications.

关键词： Feature extraction Cervical cancer Accuracy Cancer Transfer learning Solid modeling Support vector machines autoencoders Computational modeling Robustness Cervical cancer diagnosis autoencoder transfer learning genetic algorithm CMAB support vector machine

来源：评论

学校读者我要写书评

暂无评论

ViTAE-SL: A vision transformer-based autoencoder and spatial interpolation learner for field reconstruction

引用

COMPUTER PHYSICS COMMUNICATIONS 2025年 308卷

作者： Fan, Hongwei Cheng, Sibo de Nazelle, Audrey J. Arcucci, Rossella Imperial Coll London Data Sci Inst London England Imperial Coll London Ctr Environm Policy London England Imperial Coll London Dept Earth Sci & Engn London England Ecole Ponts & EDF R&D CEREA Ile De France France

Reliable and accurate reconstruction for large-scale and complex physical fields in real-time from limited observations has been a longstanding challenge. In recent years, sensors have been increasingly deployed in numerous physical systems. However, the locations of these sensors can shift over time, such as with mobile sensors, or when sensors are deployed and removed. These sparse and randomly located sensors further exacerbate the difficulty of reconstructing the physical field. In this paper, we present a new deep learning model called Vision Transformer-based autoencoder (ViTAE) for reconstructing large-scale and complex fields. The proposed network structure is based on a novel core design: vision transformer encoder and Convolutional Neural Network (CNN) decoder. First, we split a two-dimensional field into patches and developed a vision transformer encoder to transfer patches into latent representations. We then reshape the linear latent representations to patches before concatenation, along with a CNN decoder, to reconstruct the field. The proposed model is tested in four different numerical experiments, using generated synthetic data, spatially distributed PM2.5 data, Computational Fluid Dynamics (CFD) simulation data and National Oceanic and Atmospheric Administration (NOAA) sea surface temperature data. The numerical results highlight the strength of ViTAE-SL compared to Kriging and state-ofthe-art deep-learning models with significantly higher reconstruction accuracy, computational efficiency, and robust scaling behavior.

关键词： Spatial interpolation Field reconstruction Vision transformer autoencoder

来源：评论

学校读者我要写书评

暂无评论

An Efficient Workflow Scheduling Using Genetically Modified Golden Jackal Optimization With Recurrent autoencoder in Cloud Computing

引用

INTERNATIONAL JOURNAL OF NETWORK MANAGEMENT 2025年第1期35卷

作者： Tripathi, Saurav Tripathi, Sarsij MNNIT Allahabad Dept Comp Sci & Engn Prayagraj Uttar Pradesh India

In this paper, a novel workflow scheduling framework is proposed using genetically modified golden jackal optimization (GM-GJO) with recurrent autoencoder. An integrated autoencoder and bidirectional gated recurrent unit (iAE-BiGRU) are used to forecast the number of virtual machines (VMs) needed to manage the system's present workload. The following step involves assigning the tasks of several workflows to cloud VMs through the use of the GM-GJO method for multiworkflow scheduling. GM-GJO provides optimal workflow scheduling by considering minimal maximizing utilization rate, minimizing makespan, and minimizing the number of deadline missed workflows. The proposed approach attempts to allocate the best possible set of resources for the workflows based on objectives such as deadline, cost, and quality of service (QoS). Extensive experiments were conducted with the CloudSIM tool, and the performance is evaluated in terms of scheduling length ratio, cost, QoS, etc. The execution time of 513.45 ms is achieved with a Sipht workflow of 30 tasks. When comparing the suggested strategy to the current methodologies, the suggested approach performs better.

关键词： workflow prediction autoencoder bidirectional gated recurrent unit workflow scheduling genetically modified golden jackal optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：