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21st International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Gabryel, Marcin Lada, Dawid Kocic, Milan Spark Digitup Plac Woln 13 Lok 10 PL-31060 Krakow Poland

ISBN: (纸本)9783031234798;9783031234804

In this paper, a neural network model is presented to identify fraudulent visits to a website, which are significantly different from visits of human users. Such unusual visits are most often made by automated software, i.e. bots. Bots are used to perform advertising scams or to do scraping, i.e., automatic scanning of website content frequently not in line with the intentions of website authors. The model proposed in this paper works on data extracted directly from a web browser when a user or a bot visits a website. This data is acquired by way of using JavaScript. When bots appear on the website, collected parameter values are significantly different from the values collected during usual visits made by human website users. However, just knowing what values these parameters have is simply not enough to identify bots as they are being constantly modified and new values that have not yet been accounted for appear. Thus, it is not possible to know all the data generated by bots. Therefore, this paper proposes a neural network with an autoencoder structure that makes it possible to detect deviations in parameter values that depart from the learned data from usual users. This enables detection of anomalies, i.e., data generated by bots. The effectiveness of the presented model is demonstrated on authentic data extracted from several online stores.

关键词： autoencoder Neural networks Deep learning Web traffic Ad fraud

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Revealing spatio-temporal characteristics of ecological resistance surfaces: A regional study case based on coupled algorithms autoencoder-XGBoost

ECOLOGICAL FRONTIERS

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ECOLOGICAL FRONTIERS 2025年第2期45卷 422-432页

作者： Lao, Yeming He, Xiaorong Chen, Jiale Liang, Youjia Wuhan Univ Technol Sch Resources & Environm Engn Wuhan 430070 Peoples R China Minist Nat Resources Observat Res Stn Land Ecol & Land Use Yangtze Rive Nanjing 210017 Peoples R China

As regional economy develops rapidly, the ecological environment exposes a series of problems, with the characteristics of ecological resistance surfaces being obviously differentiated. In the context of the increasing advance in computer science, this research introduced a deep learning algorithm, autoencoder, to analyze the ecological resistance surfaces over a 20-year period in 23 counties and districts in the Three Gorges Reservoir Area. The main results are as follows: (1) the overall resistance surfaces were at a low level, with high values concentrated in the core urban areas and along the Yangtze River. (2) The spatial distributions of the autoencoder results were smoother and more focused compared to the results of analytic hierarchy process. (3) The XGBoost detection results indicated land cover types as the highest contributing factor to the resistance. (4) Habitat quality analysis revealed a high degree of spatial heterogeneity, with the northeastern regions exhibiting more favorable conditions and the densely populated urban centers in the southwest displaying degraded quality. (5) The regionalization analysis outlined key conservation area, key restoration area and moderate development area, proposing a strategic framework of "One Axis, Two Zones and Four Protection Screens" for guiding the long-term ecological development in the research region.

关键词： Resistance surfaces Habitat quality autoencoder XGBoost Eological regionalization Ecological development planning

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Convolutional autoencoder based on latent subspace projection for anomaly detection

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METHODS 2023年第1期214卷 48-59页

作者： Yu, Qien Li, Chen Zhu, Ye Kurita, Takio Chongqing Jiaotong Univ Sch Informat Sci & Engn Chongqing 400074 Peoples R China Nagoya Univ Grad Sch Sci Nagoya 4648602 Japan Deakin Univ Sch Informat Technol Geelong Vic 3125 Australia Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima Hiroshima 7398521 Japan

Image anomaly detection (AD) is widely researched on many occasions in computer vision tasks. High -dimensional data, such as image data, with noise and complex background is still challenging to detect anomalies under the situation that imbalanced or incomplete data are available. Some deep learning methods can be trained in an unsupervised way and map the original input into low-dimensional manifolds to predict larger differences in anomalies according to normal ones by dimension reduction. However, training a single low-dimension latent space is limited to present the low-dimensional features due to the fact that the noise and irreverent features are mapped into this space, resulting in that the manifolds are not discriminative for detecting anomalies. To address this problem, a new autoencoder framework is proposed in this study with two trainable mutually orthogonal complementary subspaces in the latent space, by latent subspace projection (LSP) mechanism, which is named as LSP-CAE. Specifically, latent subspace projection is used to train the latent image subspace (LIS) and the latent kernel subspace (LKS) in the latent space of the autoencoder-like model respectively, which can enhance learning power of different features from the input instance. The features of normal data are projected into the latent image subspace, while the latent kernel subspace is trained to extract the irrelevant information according to normal features by end-to-end training. To verify the generality and effectiveness of the proposed method, we replace the convolutional network with the fully-connected network contucted in the real-world medical datasets. The anomaly score based on projection norms in two subspace is used to evaluate the anomalies in the testing. Consequently, our proposed method can achieve the best performance according to four public datasets in comparison of the state-of-the-art methods.

关键词： autoencoder Anomaly detection Latent subspace projection Subspace detection

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Approximating the Hotelling observer with autoencoder-learned efficient channels for binary signal detection tasks

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JOURNAL OF MEDICAL IMAGING 2023年第5期10卷 055501页

作者： Granstedt, Jason L. Zhou, Weimin Anastasio, Mark A. Univ Illinois Dept Comp Sci Champaign IL 61820 USA Shanghai Jiao Tong Univ Global Inst Future Technol Shanghai Peoples R China Univ Illinois Dept Bioengn Champaign IL 61820 USA

Purpose: The objective assessment of image quality (IQ) has been advocated for the analysis and optimization of medical imaging systems. One method of computing such IQ metrics is through a numerical observer. The Hotelling observer (HO) is the optimal linear observer, but conventional methods for obtaining the HO can become intractable due to large image sizes or insufficient data. Channelized methods are sometimes employed in such circumstances to approximate the HO. The performance of such channelized methods varies, with different methods obtaining superior performance to others depending on the imaging conditions and detection task. A channelized HO method using an AE is presented and implemented across several tasks to characterize its performance. Approach: The process for training an AE is demonstrated to be equivalent to developing a set of channels for approximating the HO. The efficiency of the learned AE-channels is increased by modifying the conventional AE loss function to incorporate task-relevant information. Multiple binary detection tasks involving lumpy and breast phantom backgrounds across varying dataset sizes are considered to evaluate the performance of the proposed method and compare to current state-of-the-art channelized methods. Additionally, the ability of the channelized methods to generalize to images outside of the training dataset is investigated. Results: AE-learned channels are demonstrated to have comparable performance with other state-of-the-art channel methods in the detection studies and superior performance in the generalization studies. Incorporating a cleaner estimate of the signal for the detection task is also demonstrated to significantly improve the performance of the proposed method, particularly in datasets with fewer images. Conclusions: AEs are demonstrated to be capable of learning efficient channels for the HO. The resulting significant increase in detection performance for small dataset sizes when incorporating a

关键词： objective image quality assessment numerical observers channelized Hotelling observer autoencoder neural networks

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A Novel Approach to Detection of COVID-19 and Other Respiratory Diseases Using autoencoder and LSTM

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SN Computer Science 2025年第1期6卷 1-11页

作者： Malviya, Anjali Dixit, Rahul Shukla, Anupam Kushwaha, Nagendra Indian Institute of Information Technology Pune India Sardar Vallabhbhai National Institute of Technology Surat India

Innumerable approaches of deep learning-based COVID-19 detection systems have been suggested by researchers in the recent past, due to their ability to process high-dimensional, complex data, leading to more accurate prediction of the COVID-19 infected patients. There is a visible dominance of Convolutional Neural Network (CNN) based models analysing chest images like X-rays and Computed Tomography (CT) scans for prediction, while the utilization of audio data for the same is less prevalent. Considering the respiratory system is one of the primary means by which the SARS-CoV-2 virus spreads, respiratory sounds are a potential biomarker for determining the presence of COVID-19. In this paper, we propose a novel approach for the detection of COVID-19 from amidst a dataset comprising of respiratory sound samples of healthy, COVID-19, and other lung diseases which are often misinterpreted as COVID-19. The approach employs an autoencoder for anomaly detection and a Long Short-Term Memory (LSTM) network for the detection of COVID-19 from amongst other lung diseases. The first stage of the model comprises an encoder-decoder-based autoencoder model with baseline reconstruction error, trained in an unsupervised environment, to reconstruct “healthy” audio signals. An LSTM based multi-class classifier is proposed for the second stage to classify the infected samples into the five classes: COVID-19, Bronchiolitis, COPD, Pneumonia and URTI. The experimental results demonstrate the efficacy of our proposed approach in detecting COVID-19 from a 5-class test set of audio samples of patients suffering from respiratory disease, with an accuracy of 98.7%, and an AUC of 1. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： autoencoder COVID-19 Deep learning Long short-term memory (LSTM) Respiratory-diseases classification

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autoencoder and Teaching-learning-based Optimizer for Mobile Edge Computing System Optimization Problems

Autoencoder and Teaching-learning-based Optimizer for Mobile...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Xu, Dian Zhou, Mengchu Yuan, Haitao Institute of Systems Engineering Macau University of Science and Technology Macao China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark United States School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9798350337020

By using an autoencoder as a dimension reduction tool, an autoencoder-embedded Teaching-Learning Based Optimization (ATLBO) has been proved to be effective in solving high-dimensional computationally expensive problems through several widely used function problems. However, the following two crucial issues have not been resolved, 1) ATLBO should be verified by solving real-life optimization problems;and 2) how autoencoder parameters and structures impact AEO's performance. In this work, ATLBO is verified by an energy consumption minimization problem (ECM) in mobile edge computing systems. To design an effective autoencoder for ATLBO, this work proposes a parameter tuning optimization strategy for autoencoders. By using the proposed autoencoder Parameter Tuning (APT) strategy, ATLBO can enjoy higher robustness than those without it. The experimental results show that it is three to six times better than state-of-the-art methods in solving ECM. We consider the strategy-induced overhead and take the execution time as the primary criterion to evaluate them. In addition, the experimental results show that, against the conventional wisdom that higher-accuracy auto encoders bring higher system performance, lower-accuracy ones can actually assist ATLBO in locating the best solutions. This work promotes a novel application of autoencoders in optimization theory and practice. © 2023 IEEE.

关键词： autoencoder autoencoder-embedded teaching-learning based optimization (ATLBO) High-dimensional expensive problems (HEPs) Mobile edge computing systems

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Defense against Adversarial Attacks on Image Recognition Systems Using an autoencoder

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2023年第8期57卷 989-995页

作者： Platonov, V. V. Grigorjeva, N. M. Peter Great St Petersburg Polytech Univ St Petersburg 195251 Russia Ulyanov Lenin St Petersburg State Electrotech Univ Ulyanov Lenin St St Petersburg 197022 Russia

Adversarial attacks on artificial neural network systems for image recognition are considered. To improve the security of image recognition systems against adversarial attacks (evasion attacks), the use of autoencoders is proposed. Various attacks are considered and software prototypes of autoencoders of full-link and convolutional architectures are developed as means of defense against evasion attacks. The possibility of using developed prototypes as a basis for designing autoencoders more complex architectures is substantiated.

关键词： image recognition system adversarial attack evasion attack autoencoder

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Bubble: a fast single-cell RNA-seq imputation using an autoencoder constrained by bulk RNA-seq data

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BRIEFINGS IN BIOINFORMATICS 2023年第1期24卷 bbac580-bbac580页

作者： Chen, Siqi Yan, Xuhua Zheng, Ruiqing Li, Min Cent South Univ Comp Sci Changsha Peoples R China Cent South Univ Scl Comp Sci & Engn Changsha Peoples R China

Single -cell RNA -sequencing technology (scRNA-seq) brings research to single -cell resolution. However, a major drawback of scRNA-seq is large sparsity, i.e. expressed genes with no reads due to technical noise or limited sequence depth during the scRNA-seq protocol. This phenomenon is also called 'dropout' events, which likely affect downstream analyses such as differential expression analysis, the clustering and visualization of cell subpopulations, cellular trajectory inference, etc. Therefore, there is a need to develop a method to identify and impute these dropout events. We propose Bubble, which first identifies dropout events from all zeros based on expression rate and coefficient of variation of genes within cell subpopulation, and then leverages an autoencoder constrained by bulk RNA-seq data to only impute those values. Unlike other deep learning -based imputation methods, Bubble fuses the matched bulk RNA-seq data as a constraint to reduce the introduction of false positive signals. Using simulated and several real scRNA-seq datasets, we demonstrate that Bubble enhances the recovery of missing values, gene -to -gene and cell -to -cell correlations, and reduces the introduction of false positive signals. Regarding some crucial downstream analyses of scRNA-seq data, Bubble facilitates the identification of differentially expressed genes, improves the performance of clustering and visualization, and aids the construction of cellular trajectory. More importantly, Bubble provides fast and scalable imputation with minimal memory usage.

关键词： single -cell RNA-seq bulk RNA-seq imputation dropouts autoencoder

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A Channel Attention-Based Denoising autoencoder U-Net and Classifier (UNA-DAEC) for Signal Identification Under Challenging SNR Conditions

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IEEE ACCESS 2025年 13卷 77317-77333页

作者： Ebo, Ife Olalekan Ajayi, Idowu Mroueh, Lina Ziade, Youmni Azarian, Sylvain ISEP Lab Informat Signal & Image Elect & Telecommun LIS F-92130 Issy Les Moulineaux France Agence Natl Frequences F-94704 Maisons Alfort France SDR Technol F-78120 Rambouillet France

The use of Low-Power Wide Area Network (LPWAN) technologies, such as Long Range (LoRa), Sigfox, and IEEE 802.15.4g (ZigBee), has grown significantly, addressing a wide range of applications including smart metering, agriculture, smart homes, and healthcare. These technologies are valued for their simplicity, flexible connectivity, low power consumption, efficient modulation techniques, and moderate data rates. As a result, they can coexist within the same environment, serving either similar or distinct applications. However, the increasing deployment of devices and technologies has amplified the likelihood of interference between them, leading to performance degradation, particularly in real-world scenarios under challenging conditions where noise power surpasses signal power. The rapid proliferation of these technologies, especially within unlicensed Industrial, Scientific, and Medical (ISM) frequency bands, underscores the need for effective techniques to ensure seamless coexistence without disrupting communication. To address this challenge, we investigate the role of data representation and propose a Channel Attention-based Denoising autoencoder U-Net and Classifier (UNA-DAEC). This model is designed to denoise multi-label LPWAN signals affected by white Gaussian noise and accurately classify overlapping transmissions, specifically IEEE 802.15.4g, Sigfox, and LoRa signals, within the same environment. The primary objective of UNA-DAEC is to achieve reliable signal classification in low Signal-to-Noise Ratio (SNR) conditions. This is achieved by first denoising the noisy signals to obtain optimal representations, enabling high classification accuracy with a single forward and backward propagation. Our results further demonstrate that data representation plays a critical role in identifying and classifying LPWAN signals, particularly in challenging low-SNR environments, with a significant performance of 44%, 7%, and 26% over CNN-based IQ, CNN-based FFT and DAE+Cla

关键词： Low-power wide area networks Wireless sensor networks Signal to noise ratio LoRa Interference Deep learning Internet of Things Noise reduction Wireless networks Sensors autoencoder channel attention convolutional neural network deep learning denoising auto-encoder low-power wireless area network technology classification time-frequency analysis

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Purifying Adversarial Examples Using an autoencoder 27th

Purifying Adversarial Examples Using an Autoencoder

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27th International Conference on Discovery Science

作者： van Weezel, Thijs van Ree, Famke Bos, Tychon Bastiaanssen, Patrick Hess, Sibylle Eindhoven Univ Technol Math & Comp Sci Dept Eindhoven Netherlands

ISBN: (纸本)9783031789793;9783031789809

One of the most prominent security challenges to neural networks are adversarial examples - inputs with often barely perceptible perturbations causing misclassification. In this study, we propose a defense mechanism that uses an autoencoder to restore adversarial examples before classification. That is, the autoencoder purifies input data points from potential adversarial perturbations. The method is titled autoencoder-based Adversarial Purification (AAP). We demonstrate the effectiveness of AAP on multiple datasets, attack methods, and perturbation levels. While certain limitations exist, this research offers valuable insights and a promising direction for robust defense mechanisms in adversarial deep learning.

关键词： adversarial attack autoencoder purification

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