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A deep autoencoder with sparse and graph Laplacian regularization for characterizing dynamic functional connectivity during brain development

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NEUROCOMPUTING 2021年 456卷 97-108页

作者： Qiao, Chen Hu, Xin-Yu Xiao, Li Calhoun, Vince D. Wang, Yu-Ping Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Tulane Univ Dept Biomed Engn New Orleans LA 70118 USA Tulane Univ Ctr Genom & Bioinformat New Orleans LA 70112 USA Georgia State Univ Georgia Inst Technol Triinst Ctr Translat Res Neuroimaging & Data Sci Atlanta GA 30303 USA Emory Univ Atlanta GA 30303 USA

deep-layer autoencoder (DAE) provides a powerful way for medical image analysis, while it remains a daunting challenge due to the limited samples but high dimension. In this paper, a DAE with sparse and graph Laplacian regularization, termed as GSDAE, is presented to identify significant differences of dynamic functional connectivity (dFC) between child and young adult groups. The proposed model incor-porates prior knowledge into sparse learning, i.e., the intrinsic structural information defined by manifold in the data. In this way, the reconstruction ability of unsupervised DAE can be improved, which facilitates the extraction of most discriminative features of dFC changing with age. Results on the fMRI data from the Philadelphia Neurodevelopmental Cohort project reveal essential differences lying in the reoccur-rence patterns of dFC and in the connectivity of resting state networks with increasing age, e.g., there exist different trajectories of connectivity patterns in brain functions: those associated with complex cog-nitive functions generally decreased, while those associated with basic visual or motor control functions usually enhanced. In addition, the brain circuitry moves from segregation to integration during brain development. (c) 2021 Elsevier B.V. All rights reserved.

关键词： deep autoencoder Graph regularization Sparsity Dynamic functional connectivity Brain development

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CDDA: color-dominant deep autoencoder for faster and efficient bilateral image filtering

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SIGNAL IMAGE AND VIDEO PROCESSING 2021年第6期15卷 1189-1195页

作者： Das, Apurba Shylaja, S. S. PES Univ Dept Comp Sci & Engn Bangalore Karnataka India

Nonlinear processing of high-dimensional data is quite common in image filtering algorithms. Bilateral, joint bilateral, and non-local means filters are the examples of the same. Real-time implementation of high-dimensional filters has always been a research challenge due to its computational complexity. In this paper, we have proposed a solution utilizing both color sparseness and color dominance in an image which ensures a faster algorithm for generic high-dimensional filtering. The solution speeds up the filtering algorithm further by psycho-visual saliency-based deep encoded dominant color gamut, learned for different subject classes of images. The proposed bilateral filter has been proved to be efficient both in terms of psycho-visual quality and performance for edge-preserving smoothing and denoising of color images. The results demonstrate competitiveness of our proposed solution with the existing fast bilateral algorithms in terms of the CTQ (critical to quality) parameters.

关键词： Range-domain filtering deep autoencoder weighted histogram fast algorithm color sparseness

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Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs

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BRIEFINGS IN BIOINFORMATICS 2024年第1期25卷 bbad483页

作者： Zhou, Zhecheng Zhuo, Linlin Fu, Xiangzheng Zou, Quan Wenzhou Univ Technol Coll Data Sci & Artificial Intelligence Wenzhou Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu Peoples R China

Exploring microbial stress responses to drugs is crucial for the advancement of new therapeutic methods. While current artificial intelligence methodologies have expedited our understanding of potential microbial responses to drugs, the models are constrained by the imprecise representation of microbes and drugs. To this end, we combine deep autoencoder and subgraph augmentation technology for the first time to propose a model called JDASA-MRD, which can identify the potential indistinguishable responses of microbes to drugs. In the JDASA-MRD model, we begin by feeding the established similarity matrices of microbe and drug into the deep autoencoder, enabling to extract robust initial features of both microbes and drugs. Subsequently, we employ the MinHash and HyperLogLog algorithms to account intersections and cardinality data between microbe and drug subgraphs, thus deeply extracting the multi-hop neighborhood information of nodes. Finally, by integrating the initial node features with subgraph topological information, we leverage graph neural network technology to predict the microbes' responses to drugs, offering a more effective solution to the 'over-smoothing' challenge. Comparative analyses on multiple public datasets confirm that the JDASA-MRD model's performance surpasses that of current state-of-the-art models. This research aims to offer a more profound insight into the adaptability of microbes to drugs and to furnish pivotal guidance for drug treatment strategies. Our data and code are publicly available at: https://***/ZZCrazy00/JDASA-MRD.

关键词： microbial responses to drugs deep autoencoder microbe and drug subgraphs multi-hop neighborhood information

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A drug repositioning algorithm based on a deep autoencoder and adaptive fusion

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BMC BIOINFORMATICS 2021年第1期22卷 1-18页

作者： Chen, Peng Bao, Tianjiazhi Yu, Xiaosheng Liu, Zhongtu China Three Gorges Univ Coll Comp & Informat Technol Yichang Hubei Peoples R China

Background Drug repositioning has caught the attention of scholars at home and abroad due to its effective reduction of the development cost and time of new drugs. However, existing drug repositioning methods that are based on computational analysis are limited by sparse data and classic fusion methods;thus, we use autoencoders and adaptive fusion methods to calculate drug repositioning. Results In this study, a drug repositioning algorithm based on a deep autoencoder and adaptive fusion was proposed to mitigate the problems of decreased precision and low-efficiency multisource data fusion caused by data sparseness. Specifically, a drug is repositioned by fusing drug-disease associations, drug target proteins, drug chemical structures and drug side effects. First, drug feature data integrated by drug target proteins and chemical structures were processed with dimension reduction via a deep autoencoder to characterize feature representations more densely and abstractly. Then, disease similarity was computed using drug-disease association data, while drug similarity was calculated with drug feature and drug-side effect data. Predictions of drug-disease associations were also calculated using a top-k neighbor method that is commonly used in predictive drug repositioning studies. Finally, a predicted matrix for drug-disease associations was acquired after fusing a wide variety of data via adaptive fusion. Based on experimental results, the proposed algorithm achieves a higher precision and recall rate than the DRCFFS, SLAMS and BADR algorithms with the same dataset. Conclusion The proposed algorithm contributes to investigating the novel uses of drugs, as shown in a case study of Alzheimer's disease. Therefore, the proposed algorithm can provide an auxiliary effect for clinical trials of drug repositioning.

关键词： Drug repositioning Adaptive fusion deep autoencoder

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A Combination of deep autoencoder and Multi-Scale Residual Network for Landslide Susceptibility Evaluation

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REMOTE SENSING 2023年第3期15卷 653-653页

作者： Wang, Zhuolu Xu, Shenghua Liu, Jiping Wang, Yong Ma, Xinrui Jiang, Tao He, Xuan Han, Zeya Liaoning Tech Univ Sch Mapping & Geog Sci Fuxin 123000 Peoples R China Chinese Acad Surveying & Mapping Beijing 100830 Peoples R China Wuhan Univ Sch Resources & Environm Sci Wuhan 430072 Peoples R China

Landslide susceptibility evaluation can accurately predict the spatial distribution of potential landslides, which offers great usefulness for disaster prevention, disaster reduction, and land resource management. Aiming at the problems of insufficient samples for landslide compilation, difficulty in expanding landslide samples, and insufficient expression of nonlinear relationships among evaluation factors, this paper proposes a new evaluation method of landslide susceptibility combining deep autoencoder and multi-scale residual network (DAE-MRCNN). In the first step, a deep autoencoder network was used to learn the feature expression of the original landslide data in order to acquire effective features in the data. Next, a multi-scale residual network was constructed;specifically, the model was improved into a deep residual network model by adding skip connections in the convolutional layer. In addition, the multi-scale idea was utilized to fully extract the scale characteristics of the evaluation factors. Finally, the model was used for feature training, and the results were input into the Softmax classifier to complete the prediction of landslide susceptibility. For this purpose, a machine learning method and two state-of-the-art deep learning methods, namely SVM, CPCNN-ML, and 2D-CNN, were utilized to model landslide susceptibility in Hanzhong City, Shaanxi Province. The proposed method produced the highest model performance of 0.891, followed by 0.842, 0.869, and 0.873. The experimental results show that the DAE-MRCNN method can fully express the complex nonlinear relationships among the evaluation factors, alleviate the problem of insufficient samples in convolutional neural networks (CNN) training, and significantly improve the accuracy of susceptibility prediction.

关键词： landslide susceptibility deep autoencoder convolutional neural networks residual learning multiscale

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deep autoencoder for Mass Spectrometry Feature Learning and Cancer Detection

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IEEE ACCESS 2020年 8卷 45156-45166页

作者： Zhou, Qingguo Yong, Binbin Lv, Qingquan Shen, Jun Wang, Xin Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730030 Peoples R China Lanzhou Univ Sch Phys Sci & Technol Lanzhou 730030 Peoples R China State Grid Gansu Elect Power Res Inst Lanzhou 730030 Peoples R China Univ Wollongong Sch Comp & Informat Technol Wollongong NSW 2522 Australia

Cancer is still one of the most life threatening disease and by far it is still difficult to prevent, prone to recurrence and metastasis and high in mortality. Lots of studies indicate that early cancer diagnosis can effectively increase the survival rate of patients. But early stage cancer is difficult to be detected because of its inconspicuous features. Hence, convenient and effective cancer detection methods are urgently needed. In this paper, we propose to utilize deep autoencoder to learn latent representation of high-dimensional mass spectrometry data. Meanwhile, as a contrast, traditional particle swarm optimization (PSO) optimization algorithm are also used to select optimized features from mass spectrometry data. The learned features are further evaluated on three cancer datasets. The experimental results demonstrate that the cancer detection accuracy by learned features is as high as 100%. As our main contribution, the deep autoencoder method used in this study is a feasible and powerful instrument for mass spectrometry feature learning and also cancer diagnosis.

关键词： Cancer Mass spectroscopy Cancer detection Feature extraction Tumors Proteomics Machine learning Early cancer diagnosis deep autoencoder particle swarm optimization mass spectrometry feature learning

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Fast deep autoencoder for fe derate d learning

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PATTERN RECOGNITION 2023年第1期143卷

作者： Novoa-Paradela, David Fontenla-Romero, Oscar Guijarro-Berdinas, Bertha Univ A Coruna CITIC Campus Elvina s-n La Coruna 15008 Spain

This paper presents a novel, fast and privacy preserving implementation of deep autoencoders. DAEF (deep autoencoder for Federated learning), unlike traditional neural networks, trains a deep autoencoder network in a non-iterative way, which drastically reduces training time. Training can be performed incrementally, in parallel and distributed and, thanks to its mathematical formulation, the information to be exchanged does not endanger the privacy of the training data. The method has been evaluated and compared with other state-of-the-art autoencoders, showing interesting results in terms of accuracy, speed and use of available resources. This makes DAEF a valid method for edge computing and federated learning, in addition to other classic machine learning scenarios.& COPY;2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： deep autoencoder Anomaly detection Federated learning Edge computing Machine learning

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deep-ReID: deep features and autoencoder assisted image patching strategy for person re-identification in smart cities surveillance

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第5期83卷 15079-15100页

作者： Khan, Samee Ullah Hussain, Tanveer Ullah, Amin Baik, Sung Wook Sejong Univ Seoul 143747 South Korea

Person Re-identification (P-ReID) task searches for true matches of a given query from a large repository of non-overlapping camera's images/videos. In smart cities surveillance, P-ReID is challenging due to variation in human's appearance, illumination affects, and difference in viewpoints. The mainstream approaches achieve P-ReID by implementing supervised learning strategies, requiring exhaustive manual annotation, which is probably erroneous due to human involvement. In addition, the employed methods use high-dimensional feature maps to identify a person, which is not a realistic approach in terms of storage resources and computational complexity. To tackle these issues, we incorporate learned features and deep autoencoder under the umbrella of a unified framework for P-ReID. First, we apply a unique image patching strategy by dividing the input image into two parts (upper and lower) and acquire learned features from fully connected layer of a pretrained Convolutional Neural Network (CNN) model for both patches. To achieve efficient and high performance, the proposed framework utilizes a self-tuned autoencoder to acquire low-dimensional representative features. The obtained features are matched with the patterns of database via cosine similarity measurement to re-identify a person's appearance. The proposed framework provides a trade-off between time complexity and accuracy, where a lightweight model can be incorporated with reduced number of autoencoder layers to obtain fast and comparatively flexible results. The major novelty of the proposed framework includes implementation of a hybrid network mechanism for P-ReID, which shows convincing real-time results and best fits for smart cities surveillance. The proposed framework is tested over several P-ReID datasets to prove its influence over the existing works with reduced computational complexity.

关键词： Smart surveillance Person re-identification deep autoencoder deep learning Learned features extraction Hybrid CNN

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Vibration Recognition Based on Feature Extraction by deep autoencoder 23

Vibration Recognition Based on Feature Extraction by Deep Au...

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Proceedings of the 2023 International Conference on Frontiers of Artificial Intelligence and Machine Learning

作者： Qingsong Xiong Cheng Yuan Keyan Ji Chang He Hai Bei Xiong State Key Laboratory of Disaster Reduction in Civil Engineering Tongji University Shanghai China China and Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Disaster Reduction in Civil Engineering Tongji University Shanghai China China

ISBN: (纸本)9798400707544

Vibrational response identification of high-rise building structures under excitation of varying ambient conditions is of great significance for structural vibration control and health monitoring. Traditional strategies allocating time-frequency domain transformation or graph neural network for feature extraction cast great loss on computational efficacy and engineering applicability. Herein, this study proposes a novel vibration recognition method based on feature extraction by deep autoencoder. In this method, a deep autoencoder (AUE) is established for vibration signal reconstruction in frequency domain, on the basis of which the vibration sensitive parameters are defined and quantified based on the discrepancy between original signals and the reconstructed ones. The extracted indicators are then performed as input feature engineering of downstream linear classifiers in a supervised pattern, thereby achieving rapid identification of vibration response. To validate the capacity of the proposed method, a task of vibration response identification of a super high-rise building under typical ambient excitations was conducted. The performance results achieved a satisfactory accuracy exceeding 0.95, suggesting a powerful capability and potential applicability.

关键词： Vibration recognition deep autoencoder feature extraction signal reconstruction

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deep autoencoder with localized stochastic sensitivity for short-term load forecasting

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 130卷 106954-106954页

作者： Wang, Ting Lai, Chun Sing Ng, Wing W. Y. Pan, Keda Zhang, Mingyang Vaccaro, Alfredo Lai, Loi Lei South China Univ Technol Guangdong Prov Key Lab Computat Intelligence & Cy Sch Comp Sci & Engn Guangzhou 510630 Peoples R China Brunel Univ London Brunel Interdisciplinary Power Syst Res Ctr London UB8 3PH England Guangdong Univ Technol Dept Elect Engn Sch Automat Guangzhou 510006 Peoples R China Univ Sannio Engn Dept I-82100 Benevento Italy

This paper presents a short-term electric load forecasting model based on deep autoencoder with localized stochastic sensitivity (D-LiSSA). D-LiSSA can learn informative hidden representations from unseen samples by minimizing the perturbed error (including the training error and stochastic sensitivity) from historical load data. Specifically, this general deep autoencoder network as a deep learning model improves prediction accuracy and reliability. Moreover, a nonlinear fully connected feedforward neural network as a regression layer is applied to forecast the short-term load, with the generalization capability of the proposed model using hidden representations learned by D-LiSSA. The performance of D-LiSSA is evaluated using real-world public electric load markets of France (FR), Germany (GR), Romania (RO), and Spain (ES) from ENTSO-E. Extensive experimental results and comparisons with the classical and state-of-the-art models show that D-LiSSA yields accurate load forecasting results and achieves desired reliable capability. For instance, with the French case, D-LiSSA yields the lowest mean absolute error, mean absolute percentage error, root mean squared error;providing up to 61.89%, 63.20%, and 56.40% forecasting accuracy improvements as compared to the benchmark model for forecasting hourly horizon, respectively.

关键词： Short-term load forecasting deep autoencoder deep learning Stochastic sensitivity

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