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A unified efficient deep image compression framework and its application on human-centric Task

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第29期83卷 73407页

作者： Chen, Xueyuan Hu, Zhihao Lu, Guo Liu, Jiaheng Beihang Univ Beijing Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

Image compression is a widely used technique to reduce the spatial redundancy in images. Recently, learning based image compression has achieved significant progress by using the powerful representation ability from neural networks. However, the current learning based image compression methods suffer from the huge computational cost, which limits their capacity for practical applications. In this paper, we propose a unified framework called Efficient Deep Image Compression (EDIC) based on three new technologies, including a channel attention module, a Gaussian mixture model and a decoder-side enhancement module. Specifically, we design an auto-encoder style network for learning based image compression. To improve the coding efficiency, we exploit the channel relationship between latent representations by using the channel attention module. Besides, the Gaussian mixture model is introduced for the entropy model and improves the accuracy for bitrate estimation. Furthermore, we introduce the decoder-side enhancement module to further improve image compression performance. Our EDIC method can also be readily incorporated with the Deep Video Compression (DVC) framework (Lu et al. 2019) to further improve the video compression performance. Simultaneously, our EDIC method boosts the coding performance significantly while bringing slightly increased computational cost. More importantly, experimental results demonstrate that the proposed approach outperforms the current image compression methods and is up to more than 150 times faster in terms of decoding speed when compared with Minnen's method (Minnen et al. 2018). Moreover, we also evaluate the performance of the human-centric task (i.e., face recognition) by using different coding strategies.

关键词： Image compression Neural network auto-encoder Gaussian mixture model

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Deep Learning for automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

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IEEE ACCESS 2023年 11卷 43370-43423页

作者： Prunella, Michela Scardigno, Roberto Maria Buongiorno, Domenico Brunetti, Antonio Longo, Nicola Carli, Raffaele Dotoli, Mariagrazia Bevilacqua, Vitoantonio Polytech Univ Bari Dept Elect & Informat Engn DEI I-70126 Bari Italy Apulian Bioengn Srl I-70026 Modugno Bari Italy Comau SpA I-10095 Turin Italy

automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted feature extraction, selection, and validation, counted on a combined approach of parameterized image processing algorithms and explicated human knowledge. The outstanding performance of deep learning (DL) for vision systems, in automatically discovering a feature representation suitable for the corresponding task, has exponentially increased the number of scientific articles and commercial products aiming at industrial quality assessment. In such a context, this article reviews more than 220 relevant articles from the related literature published until February 2023, covering the recent consolidation and advances in the field of fully-automatic DL-based surface defects inspection systems, deployed in various industrial applications. The analyzed papers have been classified according to a bi-dimensional taxonomy, that considers both the specific defect recognition task and the employed learning paradigm. The dependency on large and high-quality labeled datasets and the different neural architectures employed to achieve an overall perception of both well-visible and subtle defects, through the supervision of fine or/and coarse data annotations have been assessed. The results of our analysis highlight a growing research interest in defect representation power enrichment, especially by transferring pre-trained layers to an optimized network and by explaining the network decisions to suggest trustworthy retention or rejection of the products being evaluated.

关键词： Feature extraction Transfer learning Deep learning Artificial intelligence Inspection Manuals Image recognition Computer vision autonomous systems Generative adversarial networks Artificial vision auto-encoder automatic recognition feature attention mechanism convolutional neural network deep learning explainable artificial intelligence generative-adversarial network industrial surface defects transfer learning

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An effective deep learning scheme for android malware detection leveraging performance metrics and computational resources

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第1期18卷 33-55页

作者： Wajahat, Ahsan He, Jingsha Zhu, Nafei Mahmood, Tariq Nazir, Ahsan Ullah, Faheem Qureshi, Sirajuddin Osman, Musa Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Lasbela Univ Agr Water & Marine Sci Dept Comp Sci Lasebla Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh Saudi Arabia Univ Educ Fac Informat Sci Vehari Pakistan

With the rise in the use of Android smartphones, there has been a proportional surge in the proliferation of malicious applications (apps). As mobile phone users are at a heightened risk of data theft, detecting malware on Android devices has emerged as a pressing concern within the realm of cybersecurity. Conventional techniques, such as signature-based routines, are no longer sufficient to safeguard users from the continually evolving sophistication and swift behavioral modifications of novel varieties of Android malware. Hence, there has been a significant drive in recent times towards leveraging machine learning (ML) models and methodologies to identify and generalize malicious behavioral patterns of mobile apps for detecting malware. This paper proposes Deep learning (DL) based on new and highly reliable classifier, deep neural decision forest (DNDF) for detecting Android malware. Two datasets were used: Drebin and 2014 for comparison with previous studies, and TUANDROMD collected in 2021 for detecting the latest threats with advanced obfuscation and morphing techniques. We have also calculated the time-consuming and computational resources taken by our classifier. After conducting a thorough performance evaluation, our proposed approach attained impressive results on two datasets. The empirical findings reveal that the proposed DBN and DNDF models demonstrated exceptional performance, achieving an accuracy of 99%, a sensitivity of 1, and an AUC value of 0.98%. The metrics we obtained are comparable to those of state-of-the-art ML-based Android malware detection techniques and several commercial antivirus engines.

关键词： Android malware detection deep learning auto-encoder deep belief neural network deep neural decision forest

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Feature Selection and Classification Technique for Predicting Lymph Node Metastasis of Papillary Thyroid Carcinoma

JOURNAL OF COMPUTATIONAL BIOPHYSICS AND CHEMISTRY

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JOURNAL OF COMPUTATIONAL BIOPHYSICS AND CHEMISTRY 2024年第6期23卷 801-814页

作者： Wu, Dan Zhuang, Yan Liao, Guoliang Han, Lin Chen, Ke Li, Cheng Hua, Zhan Lin, Jiangli Sichuan Univ Coll Biomed Engn Chengdu Sichuan Peoples R China China Japan Friendship Hosp Beijing Peoples R China

Papillary thyroid carcinoma (PTC) is typically an indolent cancer, yet a minority of cases develop lymph node metastasis. Due to the unclear mechanisms of lymph node metastasis, a considerable number of patients undergo unnecessary surgeries. Currently, the identification of key genetic biomarkers in high-dimensional data presents a significant challenge, thereby limiting research progress in this area. Here, we proposed a hybrid filter-wrapper feature selection strategy for core factor detection and developed MethyAE, a metastasis prediction model based on DNA methylation, utilizing an end-to-end learning auto-encoder. 46 methylated CpG sites were successfully identified as crucial biomarkers for lymph node metastasis. Leveraging 447 PTC samples from the Cancer Genome Atlas (221 with metastasis, 226 without), the MethyAE model achieves 88.9% accuracy and a recall rate of 88.6% in predicting lymph node metastasis, outperforming commonly used machine learning methods like logistic regression and random forest. Furthermore, the MethyAE model exhibits favorable performance in DNA methylation data from colon cancer, bladder cancer, and breast cancer. To the best of our knowledge, this is the first attempt to predict PTC lymph node metastasis through DNA methylation, offering pivotal decision-making criteria for avoiding unnecessary surgeries and selecting appropriate treatment plans for a substantial cohort of PTC patients.

关键词： DNA methylation genetic markers cancer diagnosis auto-encoder feature selection

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Ensembles of deep one-class classifiers for multi-class image classification

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MACHINE LEARNING WITH APPLICATIONS 2025年 19卷

作者： Novotny, Alexander Bebis, George Nicolescu, Mircea Tavakkoli, Alireza Virginia Tech Dept Comp Sci Blacksburg VA USA Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA

Traditional methods for multi-class classification (MCC) involve using a monolithic feature extractor and classifier trained on data from all the classes simultaneously. These methods are dependent on the number and types of classes and are therefore rigid against changes to the class structure. For instance, if the number of classes needs to be modified or new training data becomes available, retraining would be required for optimum classification performance. Moreover, these classifiers can become biased toward classes with a large data imbalance. An alternative, more attractive framework is to consider an ensemble of one-class classifiers (EOCC) where each one-class classifier (OCC) is trained with data from a single class only, without using any information from the other classes. Although this framework has not yet systematically matched or surpassed the performance of traditional MCC approaches, it deserves further investigation for several reasons. First, it provides amore flexible framework for handling changes in class structure compared to the traditional MCC approach. Second, it is less biased toward classes with large data imbalances compared to the multi-class classification approach. Finally, each OCC can be separately optimized depending on the characteristics of the class it represents. In this paper, we have performed extensive experiments to evaluate EOCC for MCC using traditional OCCs based on Principal Component Analysis (PCA) and auto-encoders (AE) as well as newly proposed OCCs based on Generative Adversarial Networks (GANs). Moreover, we have compared the performance of EOCC with traditional multi-class DL classifiers including VGG-19, Resnet and EfficientNet. Two different datasets were used in our experiments: (i) a subset from the Plant Village dataset plant disease dataset with high variance in the number of classes and amount of data in each class, and (ii) an Alzheimer's disease dataset with low amounts of data and a large imbalance in d

关键词： Ensembles of classifiers One-class classification Multi-class classification Generative adversarial networks auto-encoder Principal component analysis

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RGSE: Robust Graph Structure Embedding for Anomalous Link Detection

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IEEE TRANSACTIONS ON BIG DATA 2023年第5期9卷 1420-1429页

作者： Liu, Zhen Zuo, Wenbo Zhang, Dongning Feng, Xiaodong Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China 54th Res Inst CETC Sch Comp Sci & Engn Shijiazhuang 050081 Peoples R China Sun Yat Sen Univ Sch Informat Management Guangzhou 510275 Peoples R China

Anomalous links such as noisy links or adversarial edges widely exist in real-world networks, which may undermine the credibility of the network study, e.g., community detection in social networks. Therefore, anomalous links need to be removed from the polluted network by a detector. Due to the co-existence of normal links and anomalous links, how to identify anomalous links in a polluted network is a challenging issue. By designing a robust graph structure embedding framework, also called RGSE, the link-level feature representations that are generated from both global embedding view and local stable view can be used for anomalous link detection on contaminated graphs. Comparison experiments on a variety of datasets demonstrate that the new model and its variants achieve up to an average 5.2% improvement with respect to the accuracy of anomalous link detection against the traditional graph representation models. Further analyses also provide interpretable evidence to support the model's superiority.

关键词： Big Data Convolutional neural networks Computational modeling Task analysis Social networking (online) Noise measurement Natural language processing Anomalous link detection auto-encoder dual-view-based framework robust graph structure embedding

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Unsupervised structural damage detection based on an improved generative adversarial network and cloud model

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2023年第3期42卷 1501-1518页

作者： Luo, Yongpeng Guo, Xu Wang, Lin-kun Zheng, Jin-ling Liu, Jing-liang Liao, Fei-yu Fujian Agr & Forestry Univ Sch Transportat & Civil Engn Fuzhou Peoples R China Digital Fujian Lab Internet Things Intelligent Tra Fuzhou Peoples R China Huaqiao Univ Key Lab Struct Engn & Disaster Prevent Fujian Prov Xiamen Peoples R China Fujian Agr & Forestry Univ Sch Transportat & Civil Engn 63 Xiyuangong Rd Fuzhou 350002 Peoples R China

The measurements derived from damaged conditions are difficult to acquire in actual structures, which limits the applicability of supervised damage detection methods. In this study, an unsupervised damage detection method that leverages an improved generative adversarial network (IGAN) and cloud model (CM) is proposed. This method only needs the data in the healthy state of the structure for model training, which can solve the above problems. Firstly, an IGAN model is established, which uses the encoder-decoder-encoder generative network to encode, reconstruct and re-encode the structural response in healthy state. The essential features hidden in complex data are learned by minimizing the distance between the real response and the reconstructed response, and the distance between the latent vectors obtained after two different encodings. During the test phase, when unknown state data is input into the model, the differences of the latent features of the two different codes can be used to preliminarily judge whether the structure is damaged. In addition, CM theory is used to further quantify structural damage and solve the problem of damage misjudgment caused by measurement noise, model parameters, feature selection, and other uncertain factors. The effectiveness of the proposed damage detection method is verified by Phase I IASC-ASCE benchmark structure and a bridge health monitoring benchmark model. The results prove that the proposed method can accurately detect the damage that has occurred.

关键词： Structural health monitoring damage detection unsupervised learning generative adversarial network auto-encoder cloud model

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Adversarial Human Trajectory Learning for Trip Recommendation

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第4期34卷 1764-1776页

作者： Gao, Qiang Zhou, Fan Zhang, Kunpeng Zhang, Fengli Trajcevski, Goce Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Peoples R China Univ Maryland Dept Decis Operat & Informat Technol College Pk MD 20742 USA Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

The problem of trip recommendation has been extensively studied in recent years, by both researchers and practitioners. However, one of its key aspects--understanding human mobility--remains under-explored. Many of the proposed methods for trip modeling rely on empirical analysis of attributes associated with historical points-of-interest (POIs) and routes generated by tourists while attempting to also intertwine personal preferences--such as contextual topics, geospatial, and temporal aspects. However, the implicit transitional preferences and semantic sequential relationships among various POIs, along with the constraints implied by the starting point and destination of a particular trip, have not been fully exploited. Inspired by the recent advances in generative neural networks, in this work we propose DeepTrip--an end-to-end method for better understanding of the underlying human mobility and improved modeling of the POIs' transitional distribution in human moving patterns. DeepTrip consists of: a trip encoder (TE) to embed the contextual route into a latent variable with a recurrent neural network (RNN);and a trip decoder to reconstruct this route conditioned on an optimized latent space. Simultaneously, we define an Adversarial Net composed of a generator and critic, which generates a representation for a given query and uses a critic to distinguish the trip representation generated from TE and query representation obtained from Adversarial Net. DeepTrip enables regularizing the latent space and generalizing users' complex check-in preferences. We demonstrate, both theoretically and empirically, the effectiveness and efficiency of the proposed model, and the experimental evaluations show that DeepTrip outperforms the state-of-the-art baselines on various evaluation metrics.

关键词： Trajectory Generative adversarial networks Semantics Recurrent neural networks Computational modeling Training Multimedia Web sites auto-encoder deep learning generative adversarial net spatial-temporal data trip recommendation

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PSCS-Net: Perception Optimized Image Reconstruction Network for autonomous Driving Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第2期24卷 1564-1579页

作者： Bairi, Zakaria Ben-Ahmed, Olfa Amamra, Abdenour Bradai, Abbas Bey, Kadda Beghdad Ecole Mil Polytech Algiers 16046 Algeria Univ Poitiers XLIM Res Inst UMR CNRS 7252 Poitiers France

The progress achieved in transportation systems and artificial intelligence has amplified the use of intelligent transportation systems and autonomous Vehicles (AVs). Indeed, AV systems have attracted much research in recent years, which enabled multiple autonomous driving tasks, including scene understanding, visual prediction, decision-making, and communication. The latter may create a bottleneck in low-resource autonomous driving systems that send the collected images to remote edge servers for processing and decision-making. Such an issue can be addressed by compressing the images in the AV and ensuring a good-quality reconstruction at the edge. In this paper, we propose a deep neural network for Compressed Sensing (CS) based image reconstruction that integrates image semantic perception to improve the reconstruction process for visual prediction tasks. The reconstruction process is optimized using a perception-inspired loss in an end-to-end model learning process. The trained model is evaluated on autonomous driving car datasets. Obtained experimental results outperform state-of-the-art approaches in terms of both image reconstruction quality and processing time. Finally, we perform semantic urban scene segmentation on the reconstructed image to evaluate reconstruction quality for visual task prediction. Obtained results on three semantic urban scene datasets demonstrate the efficiency of the proposed approach.

关键词： Image reconstruction Image coding autonomous vehicles Semantics Visualization Sensors Task analysis Image autonomous vehicles perception compressed sensing auto-encoder deep learning

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A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach

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NEURAL COMPUTING & APPLICATIONS 2023年第4期35卷 2991-3009页

作者： Dangut, Maren David Jennions, Ian K. King, Steve Skaf, Zakwan Cranfield Univ Bedfordshire Integrated Vehicle Hlth Management Ctr IVHM Cranfield MK43 0AL Beds England Higher Coll Technol Mech Engn Dept Abu Dhabi U Arab Emirates

The use of aircraft operation logs to develop a data-driven model to predict probable failures that could cause interruption poses many challenges and has yet to be fully explored. Given that aircraft is high-integrity assets, failures are exceedingly rare. Hence, the distribution of relevant log data containing prior signs will be heavily skewed towards the typical (healthy) scenario. Thus, this study presents a novel deep learning technique based on the auto-encoder and bidirectional gated recurrent unit networks to handle extremely rare failure predictions in aircraft predictive maintenance modelling. The auto-encoder is modified and trained to detect rare failures, and the result from the auto-encoder is fed into the convolutional bidirectional gated recurrent unit network to predict the next occurrence of failure. The proposed network architecture with the rescaled focal loss addresses the imbalance problem during model training. The effectiveness of the proposed method is evaluated using real-world test cases of log-based warning and failure messages obtained from the fleet database of aircraft central maintenance system records. The proposed model is compared to other similar deep learning approaches. The results indicated an 18% increase in precision, a 5% increase in recall, and a 10% increase in G-mean values. It also demonstrates reliability in anticipating rare failures within a predetermined, meaningful time frame.

关键词： Predictive maintenance Deep learning Extremely rare failure auto-encoder GRU network Aircraft

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