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

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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

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

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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

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An Innovative Study for Tool Wear Prediction Based on Stacked Sparse autoencoder and Ensemble Learning Strategy

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SENSORS 2025年第8期25卷 2391-2391页

作者： He, Zhaopeng Shi, Tielin Chen, Xu Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Peoples R China

Highlights What are the main findings? The SSAE-BPNN models can be trained as multiple deep learning models with different predictive performance by using different activation function configurations of the hidden layer. The SSAE model can learn the deep fusion features that reflect the change of tool wear from the combination of multi-sensor sensitive features. What is the implication of the main finding? The integrated learning model based on the stacking learning strategy used the SSE-BPNN models with different prediction performance as the primary learners and the Bayesian optimized GBD model as the secondary learner to construct the intergrated deep learning model for tool wear prediction. The proposed model further improved the predictive performance of tool wear prediction model based on deep learning *** What are the main findings? The SSAE-BPNN models can be trained as multiple deep learning models with different predictive performance by using different activation function configurations of the hidden layer. The SSAE model can learn the deep fusion features that reflect the change of tool wear from the combination of multi-sensor sensitive features. What is the implication of the main finding? The integrated learning model based on the stacking learning strategy used the SSE-BPNN models with different prediction performance as the primary learners and the Bayesian optimized GBD model as the secondary learner to construct the intergrated deep learning model for tool wear prediction. The proposed model further improved the predictive performance of tool wear prediction model based on deep learning *** Accurately predicting tool wear in real time is crucial to enhance the tool prognostics and health monitoring system in computerized numerical control (CNC) machining. This paper proposed a novel integrated deep learning model for predicting the wear of milling tools by fusing multi-sensor features. The raw signals of vibration and cut

关键词： tool wear prediction ensemble learning deep learning autoencoder multi-sensor fusion milling

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

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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

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Graph embedded patch-sense autoencoder with prior knowledge for multi-component system anomaly detection

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 256卷

作者： Liu, Shen Chen, Jinglong Liu, Zijun Wang, Jun Wang, Z. Jane Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Xian Aerosp Prop Inst Sci & Technol Liquid Rocket Engine Lab Xian 710100 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

For large-scale integrated systems, efficient anomaly detection is crucial in monitoring complex equipment. Interdependency among multiple components hinders the establishment of trust in system decision-making within multivariate time series. In this work, we propose a prior knowledge graph embedded patch-sense autoencoder (GEPAE), aiming to enable unsupervised anomaly detection in large-scale systems and provide a reference for anomaly localization. The proposed method learns from multi-source normal condition data to attain efficient and reliable anomaly detection. Diverging from pointwise reconstruction and evaluation, the proposed method employs unit-level patch embedding in the encoding module and patch correction in the decoding module, aiming to preserve data structure information by learning the global relationships among patches. System decision-making and component anomaly localization are subsequently conducted by Gaussian mixture density estimation of the patch embedding results. Meanwhile, prior knowledge of the physical entity structure and multi-sensor deployment is generalized and utilized for the model to obtain convincing decisionmaking. The proposed method is tested on two real-world data sets of liquid rocket engine systems and two fault simulation data sets of subway train transmission systems, and promising results demonstrate its validity and generality.

关键词： Fault diagnosis Anomaly detection Liquid rocket engine autoencoder Prior knowledge

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Spectral Variability-Aware Cascaded autoencoder for Hyperspectral Unmixing

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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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Generative autoencoder to prevent overregularization of variational autoencoder

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ETRI JOURNAL 2025年第1期47卷 80-89页

作者： Ko, YoungMin Ko, SunWoo Kim, YoungSoo Jeonju Univ Dept Artificial Intelligence Jeonju South Korea Jeonbuk Univ Artificial Intelligence Res Lab Jeonju South Korea

In machine learning, data scarcity is a common problem, and generative models have the potential to solve it. The variational autoencoder is a generative model that performs variational inference to estimate a low-dimensional posterior distribution given high-dimensional data. Specifically, it optimizes the evidence lower bound from regularization and reconstruction terms, but the two terms are imbalanced in general. If the reconstruction error is not sufficiently small to belong to the population, the generative model performance cannot be guaranteed. We propose a generative autoencoder (GAE) that uses an autoencoder to first minimize the reconstruction error and then estimate the distribution using latent vectors mapped onto a lower dimension through the encoder. We compare the Frechet inception distances scores of the proposed GAE and nine other variational autoencoders on the MNIST, Fashion MNIST, CIFAR10, and SVHN datasets. The proposed GAE consistently outperforms the other methods on the MNIST (44.30), Fashion MNIST (196.34), and SVHN (77.53) datasets.

关键词： autoencoder data augmentation dimensionality reduction generative model variational autoencoder

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

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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

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An autoencoder for compressing angle-resolved photoemission spectroscopy data

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2025年第1期6卷

作者： Agustsson, Steinn ymir Haque, Mohammad Ahsanul Truong, Thi Tam Bianchi, Marco Klyuchnikov, Nikita Mottin, Davide Karras, Panagiotis Hofmann, Philip Aarhus Univ Dept Phys & Astron DK-8000 Aarhus Denmark Aarhus Univ Dept Comp Sci DK-8000 Aarhus C Denmark

Angle-resolved photoemission spectroscopy (ARPES) is a powerful experimental technique to determine the electronic structure of solids. Advances in light sources for ARPES experiments are currently leading to a vast increase of data acquisition rates and data quantity. On the other hand, access time to the most advanced ARPES instruments remains strictly limited, calling for fast, effective, and on-the-fly data analysis tools to exploit this time. In response to this need, we introduce ARPESNet, a versatile autoencoder network that efficiently summmarises and compresses ARPES datasets. We train ARPESNet on a large and varied dataset of 2-dimensional ARPES data extracted by cutting standard 3-dimensional ARPES datasets along random directions in k. To test the data representation capacity of ARPESNet, we compare k-means clustering quality between data compressed by ARPESNet, data compressed by discrete cosine transform, and raw data, at different noise levels. ARPESNet data excels in clustering quality despite its high compression ratio.

关键词： autoencoder angle-resolved photoemission spectroscopy data compression

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