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Spectral Variability-Aware Cascaded autoencoder for Hyperspectral Unmixing

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Zhang, Ge Mei, Shaohui Wang, Yufei Han, Huiyang Feng, Yan Du, Qian Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Peoples R China Beijing Inst Tracking & Telecommun Technol Beijing 100094 Peoples R China Qiyuan Lab Beijing 100089 Peoples R China Mississippi State Univ Dept Elect & Comp Engn Starkville MS 39762 USA

Spectral variability inevitably presents in hyperspectral images (HSIs), resulting in significant unmixing errors when using the conventional linear mixture model (LMM). Though several variants of LMM have been proposed to encounter such spectral variability, they cannot well model the complex characteristics of spectral variability, and the performance of these variants strongly depends on the prior knowledge of the scene. In this article, spectral variability within an image is classified into class-dependent variability and class-independent one, which can be tackled by a novel fully linear mixture model (FLMM) introducing a class-dependent multiplicative scaling term, a class-dependent additive perturbation term, and a class-independent variability term into the conventional LMM. Moreover, a spectral variability-aware cascaded autoencoder (SVACA) is designed to realize the automatic learning and representation of unmixing targets and spectral variability in different hyperspectral scenarios, which consists of a class-independent variability autoencoder and a cascaded class-dependent variability autoencoder. Such a network is able to handle different spectral variability autonomously without any scene prior by parallel inference structure. Experimental results over synthetic and real hyperspectral datasets demonstrate that the proposed SVACA network not only outperforms several state-of-the-art unmixing networks but also presents a stronger capability to handle spectral variability within HSIs.

关键词： autoencoders Perturbation methods Mixture models Hyperspectral imaging Atmospheric modeling Additives Training Lighting Electronic mail Geometry autoencoder deep learning hyperspectral images (HSIs) spectral variability

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Improving brain MRI denoising using convolutional autoencoder and sparse representations

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 263卷

作者： Velayudham, A. Kumar, K. Madhan Priya, M. S. Krishna Jansons Inst Technol Autonomous Dept Comp Sci & Engn Coimbatore Tamilnadu India PET Engn Coll Elect & Commun Engn Vallioor Tamilnadu India Jansons Inst Technol Autonomous Dept Artificial Intelligence & Data Sci Coimbatore Tamilnadu India

Magnetic Resonance Imaging (MRI) is an essential tool for diagnosing and monitoring diseases under various conditions. However, noise often degrades image quality, leading to inaccurate diagnoses. To address this issue, a Convolutional autoencoder-based Orthogonal Matching Pursuit (CAE-OMP) model is proposed for brain MRI image denoising. In this model, the encoder block extracts relevant features from the image while reducing overfitting, and the OMP algorithm creates a sparse representation to enhance denoising. To improve performance and computational efficiency, the traditional greedy search process in OMP is replaced with the Crossover Boosted Elephant Herd Optimization (CBEHO) algorithm. Rather than searching for atoms, CBEHO optimizes parameter selection, thereby reducing search times and enhancing convergence in the OMP process. Using this optimized sparse representation, the model iteratively improves the original image's approximation by updating residuals and the support set. The decoder block then reconstructs the denoised image features. The proposed method was tested on multiple datasets, including the RSNA MICCAI PNG dataset, the Brain Tumor Detection MRI (BTD-MRI) dataset, the Brain Tumor Classification MRI (BTC-MRI) Images dataset, and the Brain Tumor Segmentation (BraTS2020) dataset. The results show that the CAE-OMP model achieves Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) values of 0.989 and 47.345 on the BTD-MRI dataset, 0.985 and 46.321 on the RSNA MICCAI PNG dataset, 0.978 and 45.453 on the BTC-MRI dataset, and 0.981 and 46.892 on the BraTS2020 dataset, all evaluated at a 15% noise level. These outcomes indicate that the proposed CAEOMP model outperforms existing methods, demonstrating superior efficiency for denoising brain MRI images.

关键词： Noise autoencoder Neural Network Deep learning MRI Denoising Crossover Boosted Elephant Herd Optimization

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Multiscale Memory autoencoder and Spatial Filtering for Hyperspectral Anomaly Detection

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Ma, Ziyang Zhang, Yongshan Lian, Yuyun Jiang, Xinwei Liu, Xiaobo Cai, Zhihua China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China

The hyperspectral anomaly detection (HAD) aims to identify potential anomalies from complex backgrounds. Most reconstruction-based autoencoders equally treat background pixels and anomalies or ignore potential spatial information. In this letter, we propose an HAD method based on multiscale memory autoencoder and spatial filtering, abbreviated as SFM2AE. Specifically, by introducing memory modules into different hidden layers of the autoencoder, multiscale reconstruction of background and anomaly pixels is achieved in the spectral domain. In addition, morphological filtering in the spatial domain is used to extract spatial structural information from anomalies. Joint spatial-spectral anomaly detection is achieved by combining multiscale memory autoencoder and spatial filtering. Experiments demonstrate superior detection performance of the proposed method over the state-of-the-art methods.

关键词： autoencoders Image reconstruction Anomaly detection Memory modules Filtering Feature extraction Decoding Training Statistical analysis Spectral analysis autoencoder hyperspectral anomaly detection (HAD) memory module spatial filtering

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Design and use of a Denoising Convolutional autoencoder for reconstructing electrocardiogram signals at super resolution

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2025年 160卷 103058-103058页

作者： Lomoio, Ugo Veltri, Pierangelo Guzzi, Pietro Hiram Lio, Pietro Magna Graecia Univ Catanzaro Dept Surg & Med Sci Catanzaro Italy Univ Calabria DIMES Arcavacata Di Rende Italy Univ Cambridge Dept Comp Sci & Technol Cambridge England

Electrocardiogram signals play a pivotal role in cardiovascular diagnostics, providing essential information on electrical hearth activity. However, inherent noise and limited resolution can hinder an accurate interpretation of the recordings. In this paper an advanced Denoising Convolutional autoencoder designed to process electrocardiogram signals, generating super-resolution reconstructions is proposed;this is followed by in-depth analysis of the enhanced signals. The autoencoder receives a signal window (of 5 s) sampled at 50 Hz (low resolution) as input and reconstructs a denoised super-resolution signal at 500 Hz. The proposed autoencoder is applied to publicly available datasets, demonstrating optimal performance in reconstructing high-resolution signals from very low-resolution inputs sampled at 50 Hz. The results were then compared with current stateof-the-art for electrocardiogram super-resolution, demonstrating the effectiveness of the proposed method. The method achieves a signal-to-noise ratio of 12.20 dB, a mean squared error of 0.0044, and a root mean squared error of 4.86%, which significantly outperforms current state-of-the-art alternatives. This framework can effectively enhance hidden information within signals, aiding in the detection of heart-related diseases.

关键词： ECG Super-resolution Signals Denoising autoencoder

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LLM-AE-MP: Web Attack Detection Using a Large Language Model with autoencoder and Multilayer Perceptron

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 274卷

作者： Yang, Jing Wu, Yuangui Yuan, Yuping Xue, Haozhong Bourouis, Sami Abdel-Salam, Mahmoud Prajapat, Sunil Por, Lip Yee Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur 50603 Malaysia Jilin Univ Sch Software Changchun 130012 Jilin Province Peoples R China Radio Film & Televis Design & Res Inst Co Ltd Informat & Network Inst Beijing 100045 Peoples R China NYU Tandon Sch Engn New York NY 11101 USA Taif Univ Coll Comp & Informat Technol Dept Informat Technol POB 11099 Taif 21944 Saudi Arabia Mansoura Univ Fac Comp & Informat Sci Mansoura 35516 Egypt Cent Univ Himachal Pradesh Srinivasa Ramanujan Dept Math Dharamshala 176215 India

Web applications store sensitive data, making them prime targets for cybercriminals and posing national security risks. This study introduces a new approach to distinguishing legitimate and malicious hypertext transfer protocol (HTTP) requests using an autoencoder (AE). The integration of AE allows for efficient feature distillation, enhancing the sensitivity of the model to anomalies in HTTP traffic. The AE framework is combined with a transductive long short-term memory (TLSTM) network, which is trained with an advanced generative adversarial network (GAN). Using GAN promotes an adaptive learning environment, significantly boosting the robustness and generalizability of our method against evolving web attack vectors. TLSTM uses transductive learning to focus on data points near the test set, improving the adaptability of the model to outperform traditional LSTM models. In our GAN, the generator purposely excludes gradients from the most influential batch elements, improving the ability of the model to generate diverse and generalized outputs. After training the AE, its latent representations are passed to a multilayer perceptron (MLP) for detection tasks. To address the imbalanced classification in MLP, we use a reinforcement learning (RL) strategy. The RL approach strategically adjusts incentives, enhancing the performance of the model in identifying less frequent but critical malicious instances, thereby supporting a balanced security assessment. Our evaluations using the CSIC 2010 (Spanish National Research Council 2010), FWAF (web application firewall), and HttpParams datasets show that our method outperforms existing techniques, achieving (Accuracy, F-measure, geometric mean (G-means), and area under the curve (AUC)) reaching (90.937%, 89.755%, 88.446%, 0.838), (89.055, 90.663%, 88.334%, 0.847) and (92.242%, 93.774%, 91.356%, 0.897), respectively. Moreover, our model achieves efficient runtime and memory usage across the datasets, providing a practical soluti

关键词： Web attack Anomalous request autoencoder Long short-term memory Generative adversarial network Reinforcement learning

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Convolution autoencoder based Classification Model for Content Based Image Retrieval

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Procedia Computer Science 2025年 258卷 1865-1876页

作者： Rishi Rai Dilip Singh Sisodia

The Content Based Image Retrieval (CBIR) has gained significant importance due to surge of online images and its diverse applications. The CBIR system gets an image as input and returns the most analogous and pertinent image from the database. The CBIR has a wide variety of applications ranging from crime prevention to medical diagnosis and an efficient CBIR system may further improve these areas. In CBIR systems images are converted into binary codes for search and retrieval. There are many approaches for converting images into binary codes but most of them could not properly represent the non-linear relationship exist within the images. To overcome this issue many deep learning architectures and models have been proposed for learning the non-linear relationship within an image. The models used may be unsupervised learning models such as autoencoders or supervised learning models like Convolutional Neural Networks (CNN) with their respective advantages and limitations. Therefore, this paper proposes a convolutional autoencoder model for exploiting the benefits of autoencoder and CNN models for CBIR system. In this way both the data and the class distribution are considered while producing the binary code for CBIR system. The proposed model is evaluated on CIFAR-10 training dataset considering ten classes and performance is also compared with the other existing models. The convolutional autoencoder model has produced better bit codes with more than 95% accuracy as compared to an autoencoder model and has outperformed state of the state of art autoencoder models used for CBIR.

关键词： autoencoder Binary codes CBIR CNN Deep Learning Image Classification

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Enhanced Colon Cancer Prediction Using Capsule Networks and autoencoder-Based Feature Selection in Histopathological Images

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第2期16卷 817-829页

作者： Ramesh, Janjhyam Venkata Naga Mary, F. Sheeja Balaji, S. Nimma, Divya Muniyandy, Elangovan Kranthi, A. Smitha El-Ebiary, Yousef A. Baker Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram Andhra Pradesh India Graph Era Hill Univ Dept CSE Dehra Dun 248002 India Graph Era Deemed Be Univ Dept CSE Dehra Dun 248002 Uttarakhand India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept CSE Chennai India Panimalar Engn Coll Dept CSE Chennai India Univ Southern Mississippi Computat Sci Hattiesburg MS USA Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai India Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram 522302 AP India UniSZA Univ Fac Informat & Comp Kuala Terengganu Malaysia

The malignant development of cells in the colon or rectum is known as colon cancer, and because of its high incidence and possibility for death, it is a serious health problem. Because the disease frequently advances without symptoms in its early stages, early identification is essential. Improved survival rates and more successful therapy depend on an early and accurate diagnosis. The reliability of early detection can be impacted by problems with traditional diagnostic procedures, such as high false-positive rates, insufficient sensitivity, and inconsistent outcomes. This unique approach to colon cancer diagnosis uses autoencoder-based feature selection, capsule networks (CapsNets), and histopathology images to overcome these problems. CapsNets capture spatial hierarchies in visual input, improving pattern identification and classification accuracy. When employed for feature extraction, autoencoders reduce dimensionality, highlight important features, and eliminate noise, all of which enhance model performance. The suggested approach produced remarkable outcomes, with a 99.2% accuracy rate. The model's strong capacity to detect cancerous lesions with few mistakes is demonstrated by its high accuracy in differentiating between malignant and non-malignant tissues. This study represents a substantial development in cancer detection technology by merging autoencoders with Capsule Networks, so overcoming the shortcomings of existing approaches and offering a more dependable tool for early diagnosis. This method may improve patient outcomes, provide more individualized treatment regimens, and boost diagnostic accuracy.

关键词： Colon cancer prediction capsule network autoencoder histopathological images early cancer detection

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An Unsupervised Hybrid Approach for Detection of Damage with autoencoder and One-Class Support Vector Machine

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APPLIED SCIENCES-BASEL 2025年第8期15卷 4098-4098页

作者： Gunes, Burcu Gunes, Oguz Istanbul Tech Univ Dept Civil Engn TR-34467 Istanbul Turkiye MiTek Mitigat Technol AS TR-34467 Istanbul Turkiye

Progressive deterioration and accumulated damage due to overloading, extreme events, and fatigue necessitate the continuous monitoring of civil infrastructure to ensure serviceability and safety. With advances in sensor technology, data-driven structural health monitoring (SHM) strategies, particularly artificial neural networks (ANNs), have gained prominence for analyzing large datasets and identifying complex patterns. Among these, autoencoders (AEs), a specialized class of ANNs, are well-suited for unsupervised learning tasks, enabling dimensionality reduction and feature extraction. This study employs transmissibility functions (TFs) as training samples for the AE. TFs are directly derived from response measurements without the need to measure input and exhibit local sensitivity to changes in dynamic properties, making them an efficient feature for structural assessment. The reconstruction errors in TFs, quantifying the deviation between the original and AE-reconstructed data, are leveraged as damage-sensitive features for classification using a one-class support vector machine (OC-SVM). The proposed methodology is validated through numerical simulations with noise-contaminated data representing various damage scenarios in a shear-building model, as well as experimental tests on a masonry arch bridge model subjected to progressive damage. Numerical investigations demonstrate improved detection accuracy and robustness of the procedure through the incorporation of nonlinear encoding into the dimensionality reduction process, compared to the classical principal component analysis method.. Experimental results confirm the framework's effectiveness in detecting and localizing damage using unlabeled field data.

关键词： unsupervised learning autoencoder structural damage assessment one-class support vector machine transmissibility function principal component analysis

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An Online Learner Clustering Algorithm Incorporating autoencoder And Canopy-Kmeans 24

An Online Learner Clustering Algorithm Incorporating Autoenc...

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Proceedings of the 2024 2nd International Conference on Information Education and Artificial Intelligence

作者： Di Fan Leihua Fan Fuyan Zhao Huimin Sun Xiaolin Min School of Electronic Information Engineering Shandong University of Science and Technology Qingdao Shandong China

ISBN: (纸本)9798400711732

As online courses are more and more widely used in college courses, the analysis of online learning data has become an important means to improve teaching quality and learning effect. In this paper, an online learner clustering algorithm that integrates autoencoder and Canopy-Kmeans is proposed. The autoencoder is used to perform feature degradation and feature extraction for high-dimensional data which effectively solves the problems of data sparsity and dimensionality disaster. The Canopy algorithm is introduced to precluster the data which reduces the randomness of the K-means algorithm during initialization and improves the stability of clustering. And the improved K-means algorithm is used to perform the clustering analysis of text data. The experimental results show that the algorithm proposed behaved better in both clustering accuracy and noise resistance compared with traditional clustering algorithm, which is helpful to a better understanding of the behavioral characteristics of online learners and a better support for personalized teaching is provided based on this.

关键词： autoencoder

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A convolutional autoencoder framework for ECG signal analysis

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Heliyon 2025年第2期11卷 e41517页

作者： Lomoio, Ugo Vizza, Patrizia Giancotti, Raffaele Petrolo, Salvatore Flesca, Sergio Boccuto, Fabiola Guzzi, Pietro Hiram Veltri, Pierangelo Tradigo, Giuseppe Department of Surgical and Medical Sciences University of Catanzaro Catanzaro Italy DIMES University of Calabria Rende Italy SMARTEST Laboratory E-Campus University Novedrate Italy Division of Cardiology Department of Surgical and Medical Sciences University of Catanzaro Catanzaro Italy

Electrocardiographic (ECG) signals are used to evaluate heart activity and to identify disease-related anomalies. Reliable support systems are useful for analyzing ECG signals, for instance, in long-term data acquisition and evaluation (e.g., 24-hour holter recording) or to support physicians in reading ECGs. Analysis of time varying signals may be done by using autoencoders (AEs) deep neural networks. AE specialized for signal data, named Convolutional autoencoder (CAE), showed the best performances in the analysis of ECG signals. This paper presents a CAE-based framework for ECG signal analysis and anomaly identification. The trained phase is performed on synthetic data signals. The trained neural network obtained is used for the detection of anomalies in ECG signals. The trained framework has been tested on 12 lead ECG signals on a benchmark dataset and applied in scenarios where anomalies are related to cardiological risks and pathologies. The results show interesting results in automatic anomaly detection to support physicians in the decision process. The results show that the CAE-based framework is able to identify anomalies in ECG signals with a ROC AUC of 97.82% on simulated test set and a ROC AUC of 99.75% using a real test set. Finally, the proposed method has been enriched by means of reconstruction error based explainability modules and time-windows based preprocessing modules. Explainability results have been validated using abnormalities annotated by a cardiologist as ground truth and compared with explainations results. System with both code and data, is available at https://***/UgoLomoio/ECG_DSS_CAE. © 2024 The Author(s)

关键词： Anomaly detection autoencoder Decision support systems ECG Signal annotation

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