检索结果-内蒙古大学图书馆

Iris Recognition using Multi Objective Artificial Bee Colony Optimization Algorithm with autoencoder Classifier

NEURAL PROCESSING LETTERS 2022年第4期54卷 3489-3505页

作者： Sheela, S., V Radhika, K. R. BMS Coll Engn Dept Informat Sci & Engn Bangalore Karnataka India

In recent decades, iris recognition is a trustworthy and important biometric model for human recognition. Criminal to commercial products, citizen confirmation and border control are few application areas. The research work is a deep learning based integrated model for accurate iris detection and recognition. Initially, eye images are considered from two datasets, the Chinese Academy of Sciences Institute of Automation (CASIA) and the Indian Institute of Technology (IIT) Delhi v1.0. Iris region is accurately segmented using Daugman's algorithm and Circular Hough Transform (CHT). Feature extraction is hybrid that is performed using Dual Tree Complex Wavelet Transform (DTCWT), Gabor filter, Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) from the segmented iris regions. A Multiobjective Artificial Bee Colony (MABC) algorithm is proposed to eliminate noisy and redundant feature vectors by estimating consistent information. In MABC algorithm, two multi-objective functions are formulated as reduction in number of features and classification error rate. The selected active feature vectors are given as input to autoencoder classification for iris recognition. The experimental outcome shows that MABC-autoencoder model obtained 99.67% and 98.73% accuracy on CASIA-Iris, and IIT Delhi v1.0 iris datasets. Performance evaluation is based on accuracy, specificity, Critical Success Index (CSI), sensitivity, Fowlkes Mallows (FM) index, and Mathews Correlation Coefficient (MCC).

关键词： autoencoder Circular Hough transform Daugman's algorithm Iris recognition Local binary pattern Multi objective artificial bee colony

来源：评论

学校读者我要写书评

暂无评论

Improved autoencoder for unsupervised anomaly detection

引用

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第12期36卷 7103-7125页

作者： Cheng, Zhen Wang, Siwei Zhang, Pei Wang, Siqi Liu, Xinwang Zhu, En Natl Univ Def Technol Sch Comp 109 Deya Rd Changsha 410073 Hunan Peoples R China

Deep autoencoder-based methods are the majority of deep anomaly detection. An autoencoder learning on training data is assumed to produce higher reconstruction error for the anomalous samples than the normal samples and thus can distinguish anomalies from normal data. However, this assumption does not always hold in practice, especially in unsupervised anomaly detection, where the training data is anomaly contaminated. We observe that the autoencoder generalizes so well on the training data that it can reconstruct both the normal data and the anomalous data well, leading to poor anomaly detection performance. Besides, we find that anomaly detection performance is not stable when using reconstruction error as anomaly score, which is unacceptable in the unsupervised scenario. Because there are no labels to guide on selecting a proper model. To mitigate these drawbacks for autoencoder-based anomaly detection methods, we propose an Improved autoencoder for unsupervised Anomaly Detection (IAEAD). Specifically, we manipulate feature space to make normal data points closer using anomaly detection-based loss as guidance. Different from previous methods, by integrating the anomaly detection-based loss and autoencoder's reconstruction loss, IAEAD can jointly optimize for anomaly detection tasks and learn representations that preserve the local data structure to avoid feature distortion. Experiments on five image data sets empirically validate the effectiveness and stability of our method.

关键词： autoencoder deep learning unsupervised anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Application of Extreme Learning Machine- autoencoder to Medium Term Electricity Price Forecasting

引用

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2023年第6期59卷 7214-7223页

作者： Najafi, Arsalan Homaee, Omid Golshan, Mehdi Jasinski, Michal Leonowicz, Zbigniew Wroclaw Univ Sci & Technol Fac Elect Engn Dept Elect Engn Fundamentals PL-50370 Wroclaw Poland Islamic Azad Univ Dept Comp Engn Sepidan Branch Sepidan *** Iran VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Elect Power Engn Ostrava 70800 Czech Republic

Electricity market prices are highly volatile, highly frequent, non-linear, and non-stationary which makes forecasting very complicated. Although accurate forecasting plays a crucial role in the electricity market for traders, retailers, large consumers as well as generation companies in terms of economic efficiency and power systems safety. Hence, this article proposes a new forecasting approach for medium-term electricity market prices based on an extreme learning machine-autoencoder (ELM-AE). The main idea behind this is to use trained weights for hidden layers instead of randomly generated weights. The input hidden layer weights are obtained by solving a network with the same input outputs by the autoencoder method. Then, the obtained output weights are used again as the input weights for a new ELM network. To do so, a data-set is created using input data, where the ahead 24 hours are forecasted based on the previous 168 data. The simulations have been performed on New York Independent System Operator prices and compared with the classic ELM demonstrating the high accuracy of the proposed method in both training and testing.

关键词： Forecasting electricity price extreme learning machine autoencoder electricity market

来源：评论

学校读者我要写书评

暂无评论

Bilingual autoencoder-based efficient harmonization of multi-source private data for accurate predictive modeling

引用

INFORMATION SCIENCES 2021年 568卷 403-426页

作者： Lee, Taek-Ho Lee, Junghye Jun, Chi-Hyuck Pohang Univ Sci & Technol POSTECH Dept Ind & Management Engn 77 Cheongam Ro Pohang 37673 South Korea Ulsan Natl Inst Sci & Technol UNIST Dept Ind Engn 50 UNIST Gil Ulsan 44919 South Korea

Sharing electronic health record data is essential for advanced analysis, but may put sensitive information at risk. Several studies have attempted to address this risk using contextual embedding, but with many hospitals involved, they are often inefficient and inflexible. Thus, we propose a bilingual autoencoder-based model to harmonize local embeddings in different spaces. Cross-hospital reconstruction of embeddings makes encoders map embeddings from hospitals to a shared space and align them spontaneously. We also suggest two-phase training to prevent distortion of embeddings during harmonization with hospitals that have biased information. In experiments, we used medical event sequences from the Medical Information Mart for Intensive Care-III dataset and simulated the situation of multiple hospitals. For evaluation, we measured the alignment of events from different hospitals and the prediction accuracy of a patient's diagnosis in the next admission in three scenarios in which local embeddings do not work. The proposed method efficiently harmonizes embeddings in different spaces, increases prediction accuracy, and gives flexibility to include new hospitals, so is superior to previous methods in most cases. It will be useful in predictive tasks to utilize distributed data while preserving private information. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BYNC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Distributed EHR Contextual embedding Space alignment autoencoder Predictive tasks

来源：评论

学校读者我要写书评

暂无评论

Heterogeneous domain adaptation network based on autoencoder

引用

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2018年 117卷 281-291页

作者： Wang, Xuesong Ma, Yuting Cheng, Yuhu Zou, Liang Rodrigues, Joel J. P. C. China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Natl Inst Telecommun Inatel BR-37540000 Santa Rita Do Sapucai MG Brazil Univ Beira Interior Inst Telecomunicacoes P-6201001 Covilha Portugal ITMO Univ St Petersburg 191002 Russia Univ Fortaleza UNIFOR BR-60811905 Fortaleza Ceara Brazil

Heterogeneous domain adaptation is a more challenging problem than homogeneous domain adaptation. The transfer effect is not ideally caused by shallow structure which cannot adequately describe the probability distribution and obtain more effective features. In this paper, we propose a heterogeneous domain adaptation network based on autoencoder, in which two sets of autoencoder networks are used to project the source-domain and target-domain data to a shared feature space to obtain more abstractive feature representations. In the last feature and classification layer, the marginal and conditional distributions can be matched by empirical maximum mean discrepancy metric to reduce distribution difference. To preserve the consistency of geometric structure and label information, a manifold alignment term based on labels is introduced. The classification performance can be improved further by making full use of label information of both domains. The experimental results of 16 cross-domain transfer tasks verify that HDANA outperforms several state-of-the-art methods. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Heterogeneous domain adaptation autoencoder Maximum mean discrepancy Manifold alignment

来源：评论

学校读者我要写书评

暂无评论

Neighborhood preserving embedding with autoencoder

引用

DIGITAL SIGNAL PROCESSING 2024年 145卷

作者： Rana, Ruisheng Wang, Jinping Fang, Bin Yang, Weiming Chongqing Normal Univ Coll Comp & Informat Sci Chongqing 401331 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

Neighborhood preserving embedding (NPE) is a classical method for dimensionality reduction (DR), and it is a linear version of the locally linear embedding method. However, NPE and all its variants only consider the one-way mapping from high-dimensional space to low-dimensional space. The projected low-dimensional data may not accurately and effectively "represent" the original samples. To address this problem, we improve NPE based on linear autoencoder. The conventional projection of NPE is considered as the encoding stage, and the decoder stage is a reconstruction from the low-dimensional space to the original high-dimensional space, which is the key to maintaining more significant information. Based on this, we propose a new NPE method called NPEAE (neighborhood preserving embedding with autoencoder) in this paper. NPEAE performs excellently in face recognition, handwritten character categorization, object classification, etc. The experiments on MNIST, COIL-20, the Extended Yale B, Olivetti Research Laboratory (ORL), and FERET show that NPEAE has a higher recognition accuracy than other comparative methods.

关键词： Neighborhood preserving embedding autoencoder Dimensionality reduction Manifold learning

来源：评论

学校读者我要写书评

暂无评论

Chronos: DDoS Attack Detection Using Time-Based autoencoder

引用

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第1期19卷 627-641页

作者： Salahuddin, Mohammad A. Pourahmadi, Vahid Alameddine, Hyame Assem Bari, Md Faizul Boutaba, Raouf Univ Waterloo David R Cheriton Sch Comp Sci Waterloo ON N2L 3G1 Canada Amirkabir Univ Technol Dept Elect Engn Tehran *** Iran Ericsson Canada Inc Ericsson Secur Res Montreal PQ Canada Spectrum Software & Consulting Dhaka 1215 Bangladesh

Cognitive network management is becoming quintessential to realize autonomic networking. However, the wide spread adoption of the Internet of Things (IoT) devices, increases the risk of cyber attacks. Adversaries can exploit vulnerabilities in IoT devices, which can be harnessed to launch massive Distributed Denial of Service (DDoS) attacks. Therefore, intelligent security mechanisms are needed to harden network security against these threats. In this paper, we propose Chronos, a novel time-based anomaly detection system. The anomaly detector, primarily an autoencoder, leverages time-based features over multiple time windows to efficiently detect anomalous DDoS traffic. We develop a threshold selection heuristic that maximizes the F1-score across various DDoS attacks. Further, we compare the performance of Chronos against state-of-the-art approaches. We show that Chronos marginally outperforms another time-based system using a less complex anomaly detection pipeline, while out classing flow-based approaches with superior precision. In addition, we showcase the robustness of Chronos in the face of zero-day attacks, noise in training data, and a small number of training packets, asserting its suitability for online deployment.

关键词： Denial-of-service attack Feature extraction Anomaly detection Computer crime Training Training data Supervised learning Security management distributed denial of service anomaly detection autoencoder

来源：评论

学校读者我要写书评

暂无评论

Development of a new hyperspectral imaging technology with autoencoder-assisted generative adversarial network for predicting the content of polyunsaturated fatty acids in red meat

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 220卷

作者： Cui, Jiarui Li, Kenken Lv, Yu Liu, Sijia Cai, Zhengyun Luo, Ruiming Zhang, Zhongxiong Wang, Songlei Ningxia Univ Coll Anim Sci & Technol Yinchuan 750021 Peoples R China Nanchang Univ State Key Lab Food Sci & Resources Nanchang 330031 Peoples R China Ningxia Univ Coll Food Sci & Engn Yinchuan 750021 Peoples R China

The establishment of a comprehensive predictive model for red meat polyunsaturated fatty acids holds profound significance for the food industry. However, challenges, such as intricate features and low chemical content bestow complexity upon this endeavor. In the study, an autoencoder-assisted generative adversarial network (AE-GAN) was used to address the intricacies of generative models in regression operations. Following numerous iterations, the AE-GAN generated samples akin to the original data. Upon the incorporation of these generated samples into training, the test set R 2 values of Support Vector Regression, Random Forest and Fully Convolutional Network witnessed respective enhancements of 0.1589, 0.1482 and 0.2998. The outcomes underscore the efficacy of this novel approach in ameliorating the challenges faced by generative models in regression tasks, thereby augmenting the model 's generalizability.

关键词： Meat Chemometrics autoencoder Generative adversarial network Data augmentation

来源：评论

学校读者我要写书评

暂无评论

ARCADE: Adversarially Regularized Convolutional autoencoder for Network Anomaly Detection

引用

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第2期20卷 1305-1318页

作者： Lunardi, Willian Tessaro Lopez, Martin Andreoni Giacalone, Jean-Pierre Technol & Innovat Inst Secure Syst Res Ctr Abu Dhabi U Arab Emirates

As the number of heterogenous IP-connected devices and traffic volume increase, so does the potential for security breaches. The undetected exploitation of these breaches can bring severe cybersecurity and privacy risks. Anomaly-based Intrusion Detection Systems (IDSs) play an essential role in network security. In this paper, we present a practical unsupervised anomaly-based deep learning detection system called ARCADE (Adversarially Regularized Convolutional autoencoder for unsupervised network anomaly DEtection). With a convolutional autoencoder (AE), ARCADE automatically builds a profile of the normal traffic using a subset of raw bytes of a few initial packets of network flows so that potential network anomalies and intrusions can be efficiently detected before they cause more damage to the network. ARCADE is trained exclusively on normal traffic. An adversarial training strategy is proposed to regularize and decrease the AE's capabilities to reconstruct network flows that are out-of-the-normal distribution, thereby improving its anomaly detection capabilities. The proposed approach is more effective than state-of-the-art deep learning approaches for network anomaly detection. Even when examining only two initial packets of a network flow, ARCADE can effectively detect malware infection and network attacks. ARCADE presents 20 times fewer parameters than baselines, achieving significantly faster detection speed and reaction time.

关键词： Unsupervised anomaly detection autoencoder generative adversarial networks automatic feature extraction deep learning cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Attention-Guided autoencoder for Automated Progression Prediction of Subjective Cognitive Decline With Structural MRI

引用

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第6期27卷 2980-2989页

作者： Guan, Hao Yue, Ling Yap, Pew-Thian Xiao, Shifu Bozoki, Andrea Liu, Mingxia Univ North Carolina Chapel Hill Dept Radiol Chapel Hill NC 27599 USA Univ North Carolina Chapel Hill Biomed Res Imaging Ctr Chapel Hill NC 27599 USA Shanghai Jiao Tong Univ Shanghai Mental Hlth Ctr Dept Geriatr Psychiat Sch Med Shanghai 200030 Peoples R China Univ North Carolina Chapel Hill Dept Neurol Chapel Hill NC 27599 USA

Subjective cognitive decline (SCD) is the preclinical stage of Alzheimer's disease (AD) which happens even earlier than mild cognitive impairment (MCI). Progressive SCD will convert to MCI with the potential of further evolving to AD. Therefore, early identification of progressive SCD with neuroimaging techniques (e.g., structural MRI) is of great clinical value for early intervention of AD. However, existing MRI-based machine/deep learning methods usually suffer the small-sample-size problem and lack interpretability. To this end, we propose an interpretable autoencoder model with domain transfer learning (IADT) for progression prediction of SCD. Firstly, the proposed model can leverage MRIs from both the target domain (i.e., SCD) and auxiliary domains (e.g., AD and NC) for progressive SCD identification. Besides, it can automatically locate the disease-related brain regions of interest (defined in brain atlases) through an attention mechanism, which shows good interpretability. In addition, the IADT model is straightforward to train and test with only 5 similar to 10 seconds on CPUs and is suitable for medical tasks with small datasets. Extensive experiments on the publicly available ADNI dataset and a private CLAS dataset have demonstrated the effectiveness of the proposed method.

关键词： Subjective cognitive decline Alzheimer's disease domain adaptation MRI autoencoder interpretability

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：