检索结果-内蒙古大学图书馆

Skin cancer detection through attention guided dual autoencoder approach with extreme learning machine

SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Maurya, Ritesh Mahapatra, Satyajit Dutta, Malay Kishore Singh, Vibhav Prakash Karnati, Mohan Sahu, Geet Pandey, Nageshwar Nath Amity Univ Amity Ctr Artificial Intelligence Noida India Manipal Acad Higher Educ Manipal Inst Technol Dept Informat & Commun Technol Manipal 576104 India Motilal Nehru Natl Inst Technol Allahabad Dept Comp Sci & Engn Allahabad India Natl Inst Technol Raipur Comp Sci & Engn Dept Raipur 492010 Chhattisgarh India ITER Siksha O Anusandhan Dept Comp Sci & Engn Bhubaneswar Odisha India

Skin cancer is a lethal disease, and its early detection plays a pivotal role in preventing its spread to other body organs and tissues. Artificial Intelligence (AI)-based automated methods can play a significant role in its early detection. This study presents an AI-based novel approach, termed 'DualAutoELM' for the effective identification of various types of skin cancers. The proposed method leverages a network of autoencoders, comprising two distinct autoencoders: the spatial autoencoder and the FFT (Fast Fourier Transform)-autoencoder. The spatial-autoencoder specializes in learning spatial features within input lesion images whereas the FFT-autoencoder learns to capture textural and distinguishing frequency patterns within transformed input skin lesion images through the reconstruction process. The use of attention modules at various levels within the encoder part of these autoencoders significantly improves their discriminative feature learning capabilities. An Extreme Learning Machine (ELM) with a single layer of feedforward is trained to classify skin malignancies using the characteristics that were recovered from the bottleneck layers of these autoencoders. The 'HAM10000' and 'ISIC-2017' are two publicly available datasets used to thoroughly assess the suggested approach. The experimental findings demonstrate the accuracy and robustness of the proposed technique, with AUC, precision, and accuracy values for the 'HAM10000' dataset being 0.98, 97.68% and 97.66%, and for the 'ISIC-2017' dataset being 0.95, 86.75% and 86.68%, respectively. This study highlights the possibility of the suggested approach for accurate detection of skin cancer.

关键词： Convolution neural networks Skin lesion classification Deep learning autoencoder Extreme learning machine

来源：评论

学校读者我要写书评

暂无评论

autoencoder-based feature construction for IoT attacks clustering

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 127卷 487-502页

作者： Haseeb, Junaid Mansoori, Masood Hirose, Yuichi Al-Sahaf, Harith Welch, Ian Victoria Univ Wellington POB 600 Wellington 6140 New Zealand

Variations in commands executed as part of the attack process can be used to determine the behavioural patterns of IoT attacks. Existing approaches rely on the domain knowledge of security experts to identify the behavioural patterns, categorise and classify cyber attacks. We proposed an autoencoder (AE)-based feature construction approach to remove the dependency of manually correlating commands and generate an efficient representation by automatically learning the semantic similarity between input features extracted through commands data. We applied three clustering algorithms, i.e., K-means, Gaussian Mixture Models and Density-based spatial clustering of applications with noise, on our data set of AE features. We discussed the clustering arrangements for understanding the impact of changes in commands on behavioural patterns of attacks and how attacks are grouped in the same or different clusters. Evaluation of our feature construction approach shows that the clustering algorithm grouped attacks with more common features values compared to clustering with original features. Moreover, we performed a comparative analysis of two existing feature extraction approaches on our data set considering the type of analysis in the process, generalisability of applying features, coverage to the data set and clustering arrangements. We found that challenges identified in applying existing approaches can be addressed with our proposed approach and improving features with AE resulted in providing meaningful clustering interpretations. (c) 2021 Elsevier B.V. All rights reserved.

关键词： IoT attacks Feature construction autoencoder Clustering

来源：评论

学校读者我要写书评

暂无评论

autoencoder-Based PPG Compression for Resource-Constrained Device 22

Autoencoder-Based PPG Compression for Resource-Constrained D...

引用

22nd IEEE Statistical Signal Processing Workshop (SSP)

作者： Santiwongkarn, Suvichak Kornworakarn, Natakorn Suksompong, Prapun Choksatchawathi, Tanut Kaewlee, Thitikorn Wilaiprasitporn, Theerawit Thammasat Univ Sirindhorn Int Inst Technol Sch Informat Comp & Commun Technol Pathum Thani Thailand Vidyasirimedhi Inst Sci & Technol Sch Informat Sci & Technol Rayong Thailand Sense AI Co Ltd Rayong Thailand

ISBN: (纸本)9781665452458

PPG-enabled wearables are increasingly prevalent and have the potential to improve remote healthcare systems significantly. However, their implementation can be challenging under resource constraints. To alleviate these constraints, this article proposed a lightweight autoencoder-based lossy PPG compression system for data streaming applications. The system reduces the amount of transmitted data by a factor of 8 while maintaining the information with the signal RMSE at 0.026 and peak MAE at 0.020. This is possible through the loss-conditional training approach on specific task learning. Its practical functionality and advantages are verified on a resource-constrained Docker container and an Arduino Nano 33 BLE microcontroller board. This system cut down transmission energy consumption by 84%.

关键词： Signal Compression Photoplethysmograph Feature Extraction autoencoder Loss-conditional Training

来源：评论

学校读者我要写书评

暂无评论

Denoising method of weak fault acoustic emission signal under strong background noise of engine based on autoencoder and wavelet packet decomposition

引用

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2023年第5期22卷 3206-3224页

作者： Liu, Tong Jin, YuCheng Wang, Shuo Zheng, QinWen Yang, Guoan Beijing Univ Chem Technol Coll Mech & Elect Engn Beijing Peoples R China Beijing Univ Chem Technol Coll Mech & Elect Engn Beijing 100029 Peoples R China

The weak fault acoustic emission (AE) signals collected in the actual operating conditions of the engine are often submerged in the strong background noise. This paper proposes a denoising method of AE signals based on the combination of autoencoder and wavelet packet decomposition (AE-WPD) to address the above problem. Firstly, the wavelet packet is used to decompose engine background noise signals and noise-containing fault AE signals to enhance the local analysis capability of the autoencoder. Then, the dataset of each frequency band after decomposition is created. Among them, background noise signals are regarded as normal datasets. Noise-containing fault signals are treated as outlier datasets. The difference between each frequency band of background noise signals and noise-containing fault signals is analyzed. The autoencoder model is trained, validated and tested for effectiveness. In addition, a comparison is made with other commonly used denoising methods. Four types of evaluation indexes are introduced to quantitatively assess various methods. Finally, the real engine background noise signals with different signal-to-noise ratio (SNR) are added to the fault AE signals to verify the robustness of the proposed AE-WPD method. The experimental results show that the proposed AE-WPD method outperforms other denoising methods at different SNR. This lays the foundation for engine structural condition monitoring and subsequent fault identification and localization.

关键词： Acoustic emission denoising autoencoder wavelet packet decomposition

来源：评论

学校读者我要写书评

暂无评论

EANet: Multiscale autoencoder based edge attention network for fluid segmentation from SD-OCT images

引用

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2023年第3期33卷 909-927页

作者： Pappu, Geetha Pavani Tankala, Sreekar Talabhaktula, Krishna Biswal, Birendra Sankar Fdn Eye Hosp & Inst Dept CQI & Res Visakhapatnam India Gayatri Vidya Parishad Coll Engn A Ctr Med Imaging Studies Dept ECE Visakhapatnam India Sankar Fdn Eye Hosp & Inst Vitreo Retina Dept Visakhapatnam India

Macular edema is a retinal complication that occurs due to the presence of excess fluid between the retinal layers. This might lead to swelling in the retina and cause severe vision impairment if not detected in its early stages. This paper presents a robust Edge Attention network (EANet) for segmenting the different retinal fluids like Intraretinal Fluid (IRF), Subretinal Fluid (SRF), and Pigment Epithelial Detachment (PED) from the Spectral Domain - Optical Coherence Tomography (SD-OCT) images. The proposed method employs a novel image enhancement technique by filtering OCT images using a BM3D (Block Matching and 3D Filtering) filter followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) and a linear filter based on multivariate Taylor series to acquire the edge maps of the OCT images. A novel autoencoder based multiscale attention mechanism is incorporated with EANet that feeds on both the OCT image and edge-enhanced OCT image at every level of the encoder. The proposed network, EANet, has been trained and tested for the segmentation of all three types of fluids on the RETOUCH challenge dataset, and the segmentation of the IRF on the OPTIMA challenge and DUKE DME datasets. The average dice coefficient of IRF, SRF, and PED for the RETOUCH dataset is 0.683, 0.873, and 0.756, respectively, whereas it is 0.805, 0.77, and 0.756 for Cirrus, Spectralis, and Topcon vendors, respectively. The proposed method outperformed all the teams that participated in the OPTIMA challenge on all types of vendor images in terms of dice coefficient. The average dice coefficients of IRF on the OPTIMA and DUKE DME datasets are 0.84 and 0.72, respectively.

关键词： autoencoder edge enhancement macular edema multiscale attention mechanism optical coherence tomography images retinal fluid segmentation

来源：评论

学校读者我要写书评

暂无评论

Deep manifold matrix factorization autoencoder using global connectivity for link prediction

引用

APPLIED INTELLIGENCE 2023年第21期53卷 25816-25835页

作者： Lin, Xinyi Chen, Xiaoyun Zheng, Zhiwen Fuzhou Univ Coll Math & Stat Fuzhou 350116 Peoples R China

Link prediction aims to predict missing links or eliminate spurious links by employing known complex network information. As an unsupervised linear feature representation method, matrix factorization (MF)-based autoencoder (AE) can project the high-dimensional data matrix into the low-dimensional latent space. However, most of the traditional link prediction methods based on MF or AE adopt shallow models and single adjacency matrices, which cannot adequately learn and represent network features and are susceptible to noise. In addition, because some methods require the input of symmetric data matrix, they can only be used in undirected networks. Therefore, we propose a deep manifold matrix factorization autoencoder model using global connectivity matrix, called DM-MFAE-G. The model utilizes PageRank algorithm to get the global connectivity matrix between nodes for the complex network. DM-MFAE-G performs deep matrix factorization on the local adjacency matrix and global connectivity matrix, respectively, to obtain global and local multi-layer feature representations, which contains the rich structural information. In this paper, the model is solved by alternating iterative optimization method, and the convergence of the algorithm is proved. Comprehensive experiments on different real networks demonstrate that the global connectivity matrix and manifold constraints introduced by DM-MFAE-G significantly improve the link prediction performance on directed and undirected networks.

关键词： Link prediction Graph representation learning Deep matrix factorization Manifold regularization autoencoder

来源：评论

学校读者我要写书评

暂无评论

Ontology construction and mapping of multi-source heterogeneous data based on hybrid neural network and autoencoder

引用

NEURAL COMPUTING & APPLICATIONS 2023年第36期35卷 25131-25141页

作者： Zhao, Wenbin Fu, Zijian Fan, Tongrang Wang, Jiaqi Shijiazhuang Tiedao Univ Sch Informat Sci & Technol Shijiazhuang Hebei Peoples R China

In big data era, multi-source heterogeneous data become the biggest obstacle to data sharing due to its high dimension and inconsistent structure. Using text classification to solve the ontology construction and mapping problem of multi-source heterogeneous data can not only reduce manual operation, but also improve the accuracy and efficiency. This paper proposes an ontology construction and mapping scheme based on hybrid neural network and autoencoder. Firstly, the proposed text classification method uses the multi-core convolutional neural network to capture local features and uses the improved Bidirectional Long Short-Term Memory network to compensate for the shortcomings of the convolutional neural network that cannot obtain context-related information. Secondly, a similarity matching method is used for ontology mapping, which integrate autoencoder to improve anti-interference ability. We have carried out several sets of experiments to test the validity of the proposed ontology construction and mapping scheme.

关键词： Ontology construction Ontology mapping Hybrid neural network autoencoder Multi-source heterogeneous data

来源：评论

学校读者我要写书评

暂无评论

TSMAE: A Novel Anomaly Detection Approach for Internet of Things Time Series Data Using Memory-Augmented autoencoder

引用

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第5期10卷 2978-2990页

作者： Gao, Honghao Qiu, Binyang Barroso, Ramon J. Duran Hussain, Walayat Xu, Yueshen Wang, Xinheng Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Univ Valladolid Fac Telecommun Engn Valladolid 47002 Spain Victoria Univ Business Sch Melbourne Vic 3011 Australia Univ Technol Sydney Ultimo NSW 2007 Australia Xidian Univ Sch Comp Sci & Technol Xian 710126 Peoples R China Xian Jiaotong Liverpool Univ Sch Adv Technol Suzhou 215123 Peoples R China

With the development of communication, the Internet of Things (IoT) has been widely deployed and used in industrial manufacturing, intelligent transportation, and healthcare systems. The time-series feature of the IoT increases the data density and the data dimension, where anomaly detection is important to ensure hardware and software security. However, for the general anomaly detection methods, the anomaly may be well-reconstructed with tiny differences that are hard to discover. Measuring model complexity and the dataset feature space is a long and inefficient process. In this paper, we propose a memory-augmented autoencoder approach for detecting anomalies in IoT data, which is unsupervised, end-to-end, and not easily overgeneralized. First, a memory mechanism is introduced to suppress the generalization ability of the model, and a memory-augmented time-series autoencoder (TSMAE) is designed. Each memory item is encoded and recombined according to the similarity with the latent representation. Then, the new representation is decoded to generate the reconstructed sample, based on which the anomaly score can be obtained. Second, the addressing vector tends to be sparse by adding penalties and rectification functions to the loss. Memory modules are encouraged to extract typical normal patterns, thus inhibiting model generalization ability. Long short-term memory (LSTM) is introduced for decoding and encoding time-series data to obtain the contextual characteristics of time-series data. Finally, through experiments on the ECG and Wafer datasets, the validity of the TSMAE is verified. The rationality of the hyperparameter setting is discussed by visualizing the memory module addressing vector.

关键词： Anomaly detection Data models Task analysis Feature extraction Internet of Things Decoding Complexity theory time-series classification memory augmentation autoencoder

来源：评论

学校读者我要写书评

暂无评论

A missing value filling model based on feature fusion enhanced autoencoder

引用

APPLIED INTELLIGENCE 2023年第21期53卷 24931-24946页

作者： Liu, Xinyao Du, Shengdong Li, Tianrui Teng, Fei Yang, Yan Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China

With the advent of the big data era, the data quality problem is becoming more critical. Among many factors, data with missing values is one primary issue, and thus developing effective imputation models is a key topic in the research community. Recently, a major research direction is to employ neural network models such as self-organizing mappings or automatic encoders for filling missing values. However, these classical methods can hardly discover interrelated features and common features simultaneously among data attributes. Especially, it is a very typical problem for classical autoencoders that they often learn invalid constant mappings, which dramatically hurts the filling performance. To solve the above-mentioned problems, we propose a missing-value-filling model based on a feature-fusion-enhanced autoencoder. We first incorporate into an autoencoder a hidden layer that consists of de-tracking neurons and radial basis function neurons, which can enhance the ability of learning interrelated features and common features. Besides, we develop a missing value filling strategy based on dynamic clustering that is incorporated into an iterative optimization process. This design can enhance the multi-dimensional feature fusion ability and thus improves the dynamic collaborative missing-value-filling performance. The effectiveness of the proposed model is validated by extensive experiments compared to a variety of baseline methods on thirteen data sets.

关键词： Missing value filling Feature fusion autoencoder Radial basis function Deep neural network

来源：评论

学校读者我要写书评

暂无评论

Prognosis prediction of patients with malignant pleural mesothelioma using conditional variational autoencoder on 3D PET images and clinical data

引用

MEDICAL PHYSICS 2023年第12期50卷 7548-7557页

作者： Matsuo, Hidetoshi Kitajima, Kazuhiro Kono, Atsushi K. Kuribayashi, Kozo Kijima, Takashi Hashimoto, Masaki Hasegawa, Seiki Yamakado, Koichiro Murakami, Takamichi Kobe Univ Grad Sch Med Dept Radiol Kobe Japan Hyogo Med Univ Sch Med Dept Radiol Nishinomiya Hyogo Japan Hyogo Med Univ Sch Med Dept Resp Med & Hematol Nishinomiya Hyogo Japan Hyogo Med Univ Sch Med Dept Thorac Surg Nishinomiya Hyogo Japan Kobe Univ Grad Sch Med Dept Radiol 7-5-2 Kusunoki ChoChuo Ku Kobe Japan

BackgroundDeep learning (DL) has been widely used for diagnosis and prognosis prediction of numerous frequently occurring diseases. Generally, DL models require large datasets to perform accurate and reliable prognosis prediction and avoid overlearning. However, prognosis prediction of rare diseases is still limited owing to the small number of cases, resulting in small *** paper proposes a multimodal DL method to predict the prognosis of patients with malignant pleural mesothelioma (MPM) with a small number of 3D positron emission tomography-computed tomography (PET/CT) images and clinical *** 3D convolutional conditional variational autoencoder (3D-CCVAE), which adds a 3D-convolutional layer and conditional VAE to process 3D images, was used for dimensionality reduction of PET images. We developed a two-step model that performs dimensionality reduction using the 3D-CCVAE, which is resistant to overlearning. In the first step, clinical data were input to condition the model and perform dimensionality reduction of PET images, resulting in more efficient dimension reduction. In the second step, a subset of the dimensionally reduced features and clinical data were combined to predict 1-year survival of patients using the random forest classifier. To demonstrate the usefulness of the 3D-CCVAE, we created a model without the conditional mechanism (3D-CVAE), one without the variational mechanism (3D-CCAE), and one without an autoencoder (without AE), and compared their prediction results. We used PET images and clinical data of 520 patients with histologically proven MPM. The data were randomly split in a 2:1 ratio (train : test) and three-fold cross-validation was performed. The models were trained on the training set and evaluated based on the test set results. The area under the receiver operating characteristic curve (AUC) for all models was calculated using their 1-year survival predictions, and the results were *** obtained

关键词： autoencoder conditional variational autoencoder deep learning PET imaging semi-supervised learning

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：