检索结果-内蒙古大学图书馆

Classification of Imbalanced Data Using SMOTE and autoencoder Based Deep Convolutional Neural Network

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2023年第3期31卷 437-469页

作者： Alex, Suja A. Nayahi, J. Jesu Vedha St Xaviers Catholic Coll Engn Informat Technol Nagercoil India Anna Univ Reg Campus Comp Sci & Engn Tirunelveli India

The imbalanced data classification is a challenging issue in many domains including medical intelligent diagnosis and fraudulent transaction analysis. The performance of the conventional classifier degrades due to the imbalanced class distribution of the training data set. Recently, machine learning and deep learning techniques are used for imbalanced data classification. Data preprocessing approaches are also suitable for handling class imbalance problem. Data augmentation is one of the preprocessing techniques used to handle skewed class distribution. Synthetic Minority Oversampling Technique (SMOTE) is a promising class balancing approach and it generates noise during the process of creation of synthetic samples. In this paper, autoencoder is used as a noise reduction technique and it reduces the noise generated by SMOTE. Further, Deep one-dimensional Convolutional Neural Network is used for classification. The performance of the proposed method is evaluated and compared with existing approaches using different metrics such as Precision, Recall, Accuracy, Area Under the Curve and Geometric Mean. Ten data sets with imbalance ratio ranging from 1.17 to 577.87 and data set size ranging from 303 to 284807 instances are used in the experiments. The different imbalanced data sets used are Heart-Disease, Mammography, Pima Indian diabetes, Adult, Oil-Spill, Phoneme, Creditcard, BankNoteAuthentication, Balance scale weight & distance database and Yeast data sets. The proposed method shows an accuracy of 96.1%, 96.5%, 87.7%, 87.3%, 95%, 92.4%, 98.4%, 86.1%, 94% and 95.9% respectively. The results suggest that this method outperforms other deep learning methods and machine learning methods with respect to G-mean and other performance metrics.

关键词： Unbalanced data SMOTE deep learning autoencoder convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Improved Denoising autoencoder for Maritime Image Denoising and Semantic Segmentation of USV

引用

China Communications 2020年第3期17卷 46-57页

作者： Yuhang Qiu Yongcheng Yang Zhijian Lin Pingping Chen Yang Luo Wenqi Huang Electronic Information Engineering Fuzhou UniversityFuzhou 350116China Nautical College Jimei UniversityXiamen 350116China

Unmanned surface vehicle(USV)is currently a hot research topic in maritime communication network(MCN),where denoising and semantic segmentation of maritime images taken by USV have been rarely *** former has recently researched on autoencoder model used for image denoising,but the existed models are too complicated to be suitable for real-time detection of *** this paper,we proposed a lightweight autoencoder combined with inception module for maritime image denoising in different noisy environments and explore the effect of different inception modules on the denoising ***,we completed the semantic segmentation task for maritime images taken by USV utilizing the pretrained U-Net model with tuning,and compared them with original U-Net model based on different ***,we compared the semantic segmentation of noised and denoised maritime images respectively to explore the effect of image noise on semantic segmentation *** studies are provided to prove the feasibility of our proposed denoising and segmentation ***,a simple integrated communication system combining image denoising and segmentation for USV is shown.

关键词： USV denoising autoencoder semantic segmentation U-Net

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid autoencoder

引用

NEURAL PROCESSING LETTERS 2020年第2期52卷 961-975页

作者： Zhou, Fuqiang Wang, Lin Li, Zuoxin Zuo, Wangxia Tan, Haishu Beihang Univ Sch Instrumentat Sci & Optoelect Engn Beijing 100191 Peoples R China Foshan Univ Dept Elect Informat Engn Foshan 528000 Peoples R China

Abnormal detection plays an important role in video surveillance. LSTM encoder-decoder is used to learn representation of video sequences and applied for detecting abnormal event in complex environment. The learned representation of LSTM encoder-decoder is learned from encoder, and it is crucial for decoder. However, LSTM encoder-decoder generally fails to account for the global context of the learned representation with a fixed dimension representation. In this paper, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder by the shortcut connection. The experiment shows that the hybrid model performs better than the state-of-the-art anomaly detection methods in both qualitative and quantitative ways on benchmark datasets.

关键词： autoencoder LSTM Abnormality detection

来源：评论

学校读者我要写书评

暂无评论

Domain Adaptation Network Based on autoencoder

引用

Chinese Journal of Electronics 2018年第6期27卷 1258-1264页

作者： WANG Xuesong MA Yuting CHENG Yuhu School of Information and Control Engineering China University of Mining and Technology

The domain adaptation uses labeled source domain data to train a classifier to be used in the target domain with no or small amount of labeled data. Usually there exists discrepancy in terms of marginal and conditional distributions for both source and target domains,which is of critical importance to minimize the distribution discrepancy between domains. As a classical model in deep learning, the autoencoder is capable of realizing distribution matching and enhancing classification accuracy by extracting more abstract and effective features from data. A Domain adaptation network based on autoencoder(DANA) is proposed. The DANA structure consists of a couple of encoding layers: a feature extraction layer and a classification layer. For the feature extraction layer,the marginal distributions of source and target domains are matched by using the nonparametric maximum mean discrepancy measurement. For the classification layer, the softmax regression model is applied to encode the label information of source domains meanwhile to match the conditional distribution. Experimental results on ImageNet,Corel and Leaves datasets have shown the enhanced classification accuracy by our proposed algorithm compared with the classical methods.

关键词： autoencoder Domain adaptation Distribution matching Feature extraction layer Classification layer

来源：评论

学校读者我要写书评

暂无评论

Non-Technical Losses Detection Using autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids

引用

IEEE ACCESS 2022年 10卷 56863-56875页

作者： Pamir Javaid, Nadeem Qasim, Umar Yahaya, Adamu Sani Alkhammash, Eman H. Hadjouni, Myriam COMSATS Univ Islamabad Dept Comp Sci Islamabad 44000 Pakistan Univ Technol Sydney Sch Comp Sci Ultimo NSW 2007 Australia Univ Engn & Technol & Lahore Dept Comp Sci New Campus Lahore 54000 Pakistan Taif Univ Coll Comp & Informat Technol Dept Comp Sci Taif 21944 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11671 Saudi Arabia

Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid's safety, and economic loss. Therefore, it is necessary to effectively deal with the electricity theft problem. For detecting electricity theft in smart grids (SGs), an efficient and state-of-the-art approach is designed in the underlying work based on autoencoder and bidirectional gated recurrent unit (AE-BiGRU). The proposed approach consists of six components: (1) data collection, (2) data preparation, (3) data balancing, (4) feature extraction, (5) classification and (6) performance evaluation. Moreover, bidirectional gated recurrent unit (BiGRU) is used for the identification of the anomalies in electricity consumption (EC) patterns caused due to factors like family formation changes, holidays, parties, and so on, which are referred as non-theft factors. The proposed autoencoder-bidirectional gated recurrent unit (AE-BiGRU) model employs the EC data acquired from state grid corporation of China (SGCC) for simulations. Furthermore, it is visualized from the simulation results that 90.1% accuracy and 10.2% false positive rate (FPR) are obtained by the proposed model. The results are better than different existing classifiers, i.e., logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), gated recurrent unit (GRU), etc.

关键词： Data models Support vector machines Logic gates Hardware Feature extraction Convolutional neural networks Meters autoencoder bidirectional gated recurrent unit deep learning algorithms smart grid electricity theft detection machine learning algorithms synthetic theft attacks

来源：评论

学校读者我要写书评

暂无评论

Optimizing identification of mine water inrush source with manifold reduction and semi-supervised learning using improved autoencoder

引用

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2024年第5期38卷 1701-1720页

作者： Wang, Shichao Zhai, Peihe Yu, Xiaoge Han, Jin Shi, Longqing Shandong Univ Sci & Technol Coll Earth Sci & Engn Qingdao Peoples R China Shandong Univ Sci & Technol Coll Resource Tai An Peoples R China Shandong Univ Sci & Technol Coll Informat Sci & Engn Qingdao Peoples R China

To enhance the accuracy of identifying water sources in mine inrush incidents, this study, taking the Shengquan coal mine in Shandong, China, as a case study, proposes a novel water source identification model based on an improved autoencoder-the "Masked autoencoder-based Classifier" model. This model, through a unique autoencoder framework and a custom 'masked_loss' loss function, achieves semi-supervised learning and dimensionality reduction of groundwater sample ionic data. By configuring the hidden layers, the classifier component of the model directly receives data processed by the encoder component. This not only improves the model's performance but also optimizes its complexity. Through an evaluation of the model's fitting effectiveness, our model achieved an average accuracy of 88.8% across 20 runs, with precision, recall, F1-score, and MCC reaching 88.1%, 80.6%, 0.827, and 0.833, respectively, significantly outperforming other classic models. The model successfully identified the sources of three sets of inrush water samples, with a high number of successful runs and clear average probabilities. This work contributes not only to the field of mine water inrush source identification but also offers a new perspective for the broader field of machine learning.

关键词： Mine water Water source identification Manifold reduction Semi-supervised learning autoencoder

来源：评论

学校读者我要写书评

暂无评论

Time-series forecasting based on fuzzy cognitive maps and GRU-autoencoder

引用

SOFT COMPUTING 2023年 1-17页

作者： Liu, Xiaoqian Zhang, Yingjun Wang, Jingping Qin, Jiahu Yin, Hui Yang, Yanyan Huang, Hua Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China Beijing Normal Univ Ctr Informat & Network Technol Beijing 100875 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Beijing Jiaotong Univ Sch Software Engn Beijing 100044 Peoples R China

As a powerful soft computing tool, fuzzy cognitive maps (FCMs) have been successfully employed for time-series modeling and forecasting problems. However, both the rapid time variation and the trends are still open problems when processing univariate non-stationary time-series forecasting problems via FCM-based models. In this paper, we propose a time-series forecasting model by composing FCMs, gated recurrent unit network (GRU), and autoencoder network (AE). The model is termed GAE-FCM. Firstly, a scheme based on gated recurrent unit networks and autoencoder networks is designed to learn the potential representations and capture the long-term trend of non-stationary time series while decomposing these univariate time series into a group of multivariate feature vectors. Then, the obtained multivariate feature vectors are modeled as a fuzzy cognitive map in which quantifying its connection matrix is regarded as a convex optimization problem. Finally, the time-series trend is predicted by the optimized fuzzy cognitive map and corresponding modeling mechanism. The performance of the proposed model has been validated by comparison with several representative methods on five non-stationary time-series datasets.

关键词： Fuzzy cognitive maps Gated recurrent unit autoencoder Time-series forecasting

来源：评论

学校读者我要写书评

暂无评论

Multi-modal semantic autoencoder for cross-modal retrieval

引用

NEUROCOMPUTING 2019年 331卷 165-175页

作者： Wu, Yiling Wang, Shuhui Huang, Qingming Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China

Cross-modal retrieval has gained much attention in recent years. As the research mainstream, most of existing approaches learn projections for data from different modalities into a common space where data can be compared directly. However, they neglect the preservation of feature and semantic information, so they are unable to obtain satisfactory results as expected. In this paper, we propose a two-stage learning method to learn multi-modal mappings that project multi-modal data to low dimensional embeddings that preserve both feature and semantic information. In the first stage, we combine both low-level feature and high-level semantic information to learn feature-aware semantic code vectors. In the second stage, we use encoder-decoder paradigm to learn projections. The encoder projects feature vectors to code vectors, and the decoder projects code vectors back to feature vectors. The encoder-decoder paradigm guarantees the embeddings to preserve both feature and semantic information. An alternating minimization procedure is developed to solve the multi-modal semantic autoencoder optimization problem. Extensive experiments on three benchmark datasets demonstrate that the proposed method outperforms state-of-the-art cross-modal retrieval methods. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Cross-modal retrieval Multi-modal data autoencoder

来源：评论

学校读者我要写书评

暂无评论

An Energy-Efficient Data Collection Scheme Using Denoising autoencoder in Wireless Sensor Networks

引用

Tsinghua Science and Technology 2019年第1期24卷 86-96页

作者： Guorui Li Sancheng Peng Cong Wang Jianwei Niu Ying Yuan School of Computer Science and Engineering Northeastern University School of Information Science and Technology Guangdong University of Foreign Studies State Key Laboratory of Virtual Reality Technology and Systems Beihang University

As one of the key operations in Wireless Sensor Networks(WSNs), the energy-efficient data collection schemes have been actively explored in the literature. However, the transform basis for sparsifing the sensed data is usually chosen empirically, and the transformed results are not always the sparsest. In this paper, we propose a Data Collection scheme based on Denoising autoencoder(DCDA) to solve the above problem. In the data training phase, a Denoising autoencoder(DAE) is trained to compute the data measurement matrix and the data reconstruction matrix using the historical sensed data. Then, in the data collection phase, the sensed data of whole network are collected along a data collection tree. The data measurement matrix is utilized to compress the sensed data in each sensor node, and the data reconstruction matrix is utilized to reconstruct the original data in the ***, the data communication performance and data reconstruction performance of the proposed scheme are evaluated and compared with those of existing schemes using real-world sensed data. The experimental results show that compared to its counterparts, the proposed scheme results in a higher data compression rate, lower energy consumption, more accurate data reconstruction, and faster data reconstruction speed.

关键词： wireless sensor networks data collection neural networks autoencoder data reconstruction

来源：评论

学校读者我要写书评

暂无评论

Inverse design of broadband metasurface absorber based on convolutional autoencoder network and inverse design network

引用

JOURNAL OF PHYSICS D-APPLIED PHYSICS 2020年第46期53卷 464002-464002页

作者： Ma, Ju Huang, Yijia Pu, Mingbo Xu, Dong Luo, Jun Guo, Yinghui Luo, Xiangang Chinese Acad Sci State Key Lab Opt Technol Nanofabricat & Microeng Inst Opt & Elect POB 350 Chengdu 610209 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Electromagnetic (EM) metasurfaces have attracted great attention from both engineers and researchers due to their unique physical responses. With the rapid development of complex metasurfaces, the design and optimization processes have also become extremely time-consuming and computational resource-consuming. Here we proposed a deep learning model (DLM) based on a convolutional autoencoder network and inverse design network, which can help to establish the complex relationships between the geometries of metasurfaces and their EM responses. As a typical example, a metasurface absorber consisting of polymethacrylimide foam/metal ring alternating multilayers is chosen to demonstrate the capability of the DLM. The relative spectral error of the two desired spectra is only 5.80 and 5.49, respectively. Our model shows great predictive power and may be used as an effective tool to accelerate the design and optimization of metasurfaces.

关键词： metasurfaces deep learning deep learning model autoencoder absorber

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：