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Groundwater Pollution Source and Aquifer Parameter Estimation Based on a stacked autoencoder Substitute

WATER 2024年第18期16卷

作者： Wang, Han Zhang, Jinping Li, Hang Li, Guanghua Guo, Jiayuan Lu, Wenxi Minist Water Resources Song Liao River Water Resources Commiss Changchun 130000 Peoples R China Song Liao River Water Resources Commiss River Basin Planning & Policy Res Ctr Changchun 130000 Peoples R China Jilin Univ Coll New Energy & Environm Changchun 130000 Peoples R China

A concurrent heuristic search iterative process (CHSIP) is used for estimating groundwater pollution sources and aquifer parameters in this work. Frequent calls to carry out a numerical simulation of groundwater pollution have generated a huge calculated load during the CHSIP. Therefore, a valid means to mitigate this is building a substitute to emulate the numerical simulation at a low calculated load. However, there is a complicated nonlinear correlativity between the import and export of the numerical simulation on account of the large quantity of variables. This leads to a poor approach accuracy of the substitute compared to the simulation when using shallow learning methods. Therefore, we first built a stacked autoencoder substitute, using the deep learning method, to boost the approach accuracy of the substitute compared to the numerical simulation. In total, 400 training samples and 100 testing samples for the substitute were collected by employing the Latin hypercube sampling method and running the numerical simulator. The CHSIP was then employed for estimating the groundwater pollution sources and aquifer parameters, and the estimated outcome was obtained when the CHSIP was terminated. The data analysis, including interval estimation and point estimation, was implemented on the MATLAB platform. A relevant hypothetical case is set to verify our approaches, which shows that the CHSIP is helpful for estimating the groundwater pollution source and aquifer parameters and that the stacked autoencoder method can effectively boost the approach precision of the substitute for the simulator.

关键词： groundwater pollution source estimation concurrent heuristic search iterative process deep learning method substitute stacked autoencoder

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A Novel Symmetric stacked autoencoder for Adversarial Domain Adaptation Under Variable Speed

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IEEE ACCESS 2022年 10卷 24678-24689页

作者： Li, Ranran Li, Shunming Xu, Kun Li, Xianglian Lu, Jiantao Zeng, Mengjie Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Peoples R China

At present, most of the fault diagnosis methods with extensive research and good diagnostic effect are based on the premise that the sample distribution is consistent. However, in reality, the sample distribution of rotating machinery is inconsistent due to variable working conditions, and most of the fault diagnosis algorithms have poor diagnostic effects or even invalid. To dispose the above problems, a novel symmetric stacked autoencoder (NSSAE) for adversarial domain adaptation is proposed. Firstly, the symmetric stacked autoencoder network with shared weights is used as the feature extractor to extract features which can better express the original signal. Secondly, adding domain discriminator that constituting adversarial with feature extractor to enhance the ability of feature extractor to extract domain invariant features, thus confusing the domain discriminator and making it unable to correctly distinguish the features of the two domains. Finally, to assist the adversarial training, the maximum mean discrepancy (MMD) is added to the last layer of the feature extractor to align the features of the two domains in the high-dimensional space. The experimental results show that, under the condition of variable speed, the NSSAE model can extract domain invariant features to achieve the transfer between domains, and the transfer diagnosis accuracy is high and the stability is strong.

关键词： Feature extraction Fault diagnosis Adaptation models Generators Data models Data mining Rolling bearings Artificial intelligence fault diagnosis stacked autoencoder domain adaptation

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A model-based collaborate filtering algorithm based on stacked autoencoder

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NEURAL COMPUTING & APPLICATIONS 2022年第4期34卷 2503-2511页

作者： Yu, Miao Quan, Tianqi Peng, Qinglong Yu, Xu Liu, Lei Qingdao Univ Coll Text & Clothing Qingdao 266071 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China Qingdao Univ Coll Mat Sci & Engn Qingdao 266071 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

Recently, recommender systems are widely used on various platforms in real world to provide personalized recommendations. However, sparsity is a tough problem in a Collaborate Filtering (CF) recommender system as it always leads to the over-fitting problem. This paper proposes a Model-based Collaborate Filtering Algorithm Based on stacked autoencoder (MCFSAE) to overcome the sparsity problem. In the MCFSAE model, we first convert the rating matrix into a high-dimensional classification dataset with a size equal to the number of ratings. As the number of ratings is usually large scale, the classification performance can be guaranteed. Since the obtained classification dataset is high dimensional, we then utilize stacked autoencoder, which is a good nonlinear feature reduction model, to obtain a high-level low-dimensional feature presentation. Finally, a softmax classification model is used to predict the unknown ratings based on the high-level features. Extensive experiments on EachMovie and MovieLens datasets are conducted to compare the proposed MCFSAE model with other SOTA CF models. Experimental results show that MCFSAE performs better than other CF models, especially when the rating matrix is sparse.

关键词： Recommender systems Collaborate Filtering The sparsity problem Classification problem stacked autoencoder Softmax classification

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Deep Spatio-Temporal Representation for Detection of Road Accidents Using stacked autoencoder

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第3期20卷 879-887页

作者： Singh, Dinesh Mohan, Chalavadi Krishna IIT Hyderabad Dept Comp Sci & Engn Visual Learning & Intelligence Grp Hyderabad 502205 India

Vision-based detection of road accidents using traffic surveillance video is a highly desirable but challenging task. In this paper, we propose a novel framework for automatic detection of road accidents in surveillance videos. The proposed framework automatically learns feature representation from the spatiotemporal volumes of raw pixel intensity instead of traditional hand-crafted features. We consider the accident of the vehicles as an unusual incident. The proposed framework extracts deep representation using denoising autoencoders trained over the normal traffic videos. The possibility of an accident is determined based on the reconstruction error and the likelihood of the deep representation. For the likelihood of the deep representation, an unsupervised model is trained using one class support vector machine. Also, the intersection points of the vehicle's trajectories are used to reduce the false alarm rate and increase the reliability of the overall system. We evaluated out proposed approach on real accident videos collected from the CCTV surveillance network of Hyderabad City in India. The experiments on these real accident videos demonstrate the efficacy of the proposed approach.

关键词： Accident detection anomaly detection deep learning stacked autoencoder

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Parallel stacked autoencoder with particle swarm optimization for community detection in complex networks

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APPLIED INTELLIGENCE 2022年第3期52卷 3366-3386页

作者： Al-Andoli, Mohammed Tan, Shing Chiang Cheah, Wooi Ping Multimedia Univ Fac Informat Sci & Technol Jalan Ayer Keroh Lama Bukit Bewang Melaka Malaysia Univ Nottingham Ningbo China Fac Sci & Engn Sch Comp Sci Taikang East Rd Ningbo 315100 Peoples R China

Community detection is one of the long standing and challenging tasks in the field of Complex Networks (CNs). Recently, deep learning is one of the promising community detection methods, which can learn effectively low-dimensional representation of CNs. However, the existing methods have major drawbacks in terms of local minima and slow convergence, since they use Gradient Descent Backpropagation algorithm (GDBP). This reduces the performance of community detection in terms of effectiveness and efficiency. To overcome these drawbacks, this paper introduces a new parallel deep learning model based on Metaheuristic (MH) algorithm instead of the GDBP algorithm. To be specific, a new parallel stacked autoencoder (SAE) based on particle swarm optimization (PSO) is developed for feature learning and community detection in CNs. The PSO algorithm uses a multi-objective fitness function that includes the standard loss function (i.e., MSE) of the autoencoder and the modularity function to guide SAE optimization and improve community detection performance. In addition, an efficient distributed parallel implementation is proposed to improve the efficiency and scalability of the SAE-based PSO method. The parameter settings of PSO such as features-dimension and number of particles, are tuned and studied to observe their implications on community detection performance. We conducted an experiment comprising datasets of 10 real-world networks to evaluate the proposed method in different parameter settings. The results demonstrated that the SAE-based PSO method is promising and provides a competitive performance against state-of-art methods in community detection. Furthermore, the results showed that the parallel implementation of the proposed method could improve efficiency with three or greater orders of speed.

关键词： Complex networks Community detection stacked autoencoder Particle swarm optimization Parallel computation

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Enhancing Top-N Recommendation Using stacked autoencoder in Context-Aware Recommender System

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NEURAL PROCESSING LETTERS 2021年第3期53卷 1865-1888页

作者： Abinaya, S. Devi, M. K. Kavitha Thiagarajar Coll Engn Dept Comp Sci & Engn Madurai Tamil Nadu India

Context-aware recommender systems (CARS) are a vital module of many corporate, especially within the online commerce domain, where consumers are provided with recommendations about products potentially relevant for them. A traditional CARS, which utilizes deep learning models considers that user's preferences can be predicted by ratings, reviews, demographics, etc. However, the feedback given by the users is often conflicting when comparing the rating score and the sentiment behind the reviews. Therefore, a model that utilizes either ratings or reviews for predicting items for top-N recommendation may generate unsatisfactory recommendations in many cases. In order to address this problem, this paper proposes an effective context-specific sentiment based stacked autoencoder (CSSAE) to learn the concrete preference of the user by merging the rating and reviews for a context-specific item into a stacked autoencoder. Hence, the user's preferences are consistently predicted to enhance the Top-N recommendation quality, by adapting the recommended list to the exact context where an active user is operating. Experiments on four Amazon (5-core) datasets demonstrated that the proposed CSSAE model persistently outperforms state-of-the-art top-N recommendation methods on various effectiveness metrics.

关键词： Context-aware recommender systems Item splitting Sentiment analysis stacked autoencoder

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A Data-Driven Health Monitoring Method Using Multiobjective Optimization and stacked autoencoder Based Health Indicator

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第9期17卷 6379-6389页

作者： Chen, Zhiwen Guo, Rongjie Lin, Zhi Peng, Tao Peng, Xia Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen State Key Lab High Performance Complex Mfg Changsha 410083 Peoples R China Hong Kong Univ Sci & Technol Sch Engn Hong Kong Peoples R China

This article proposes a new data-driven health monitoring method, which uses multiobjective optimization and stacked autoencoder based health indicator. Specifically, the proposed method proposes an improved nondominated sorting genetic algorithm-II (NSGA-II) to perform multiobjective optimization on a large number of candidate features extracted from the sensor measurements. Then, a stacked autoencoder model is used to construct health indicators from the selected features. In the improved NSGA-II algorithm, the optimization goals of feature selection are defined as the minimum gap of health indicators between different states and the number of features. Comparisons between the proposed method and the state-of-the-art methods on simulation experiments show that the proposed method can accurately identify the status of the equipment and effectively limit the complexity of the diagnostic model.

关键词： Optimization Feature extraction Indexes Monitoring Sorting Sociology Statistics Data driven health monitoring multiobjective optimization stacked autoencoder

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Diagnosis of breast cancer with stacked autoencoder and Subspace kNN

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 551卷

作者： Adem, Kemal Aksaray Univ Dept Management Informat Syst TR-68100 Aksaray Turkey

Breast cancer is one of the most common and deadliest cancer types in women worldwide. Research on this disease has become very important because early diagnosis stages, clinical applications and the speed of response to treatment are facilitated in diseases such as cancer. In this study, an approach is proposed in which a Subspace kNN algorithm is used together with stacked autoencoder (SAE) for diagnosis of disease on the breast cancer microarray dataset for the first time. Such hybrid approaches can provide better results when classifying data sets with high-dimensional and uncertainty. The data set used in the study was taken from Kent Ridge-2 database. It consists of 97 samples (51 benign, 46 malicious) and 24482 attributes. The performance of the proposed method was evaluated and the results were compared with other well-known methods of dimension reduction and machine learning. As a result of the comparison, the data set was reduced to 100 attributes by using SAE and Subspace kNN and 91.24% accuracy was achieved. The result obtained provides important classification accuracy, especially in high-dimensional data sets. The importance of this study is that the models that were created by using various classifiers to increase the success rate of the stacked autoencoder-softmax classifier model in the breast cancer microarray data set were applied for the first time. In this regard, it is considered that automation-based studies will provide diagnostic decision support system a solution using the proposed method in future works. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Breast cancer stacked autoencoder Subspace kNN

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Classification of data on stacked autoencoder using modified sigmoid activation function

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期44卷 1-18页

作者： Kumar, Arvind Sodhi, Sartaj Singh Comp Sci & Engn USICT GGSIPU Delhi India

A Neural Network is one of the techniques by which we classify data. In this paper, we have proposed an effectively stacked autoencoder with the help of a modified sigmoid activation function. We have made a two-layer stacked autoencoder with a modified sigmoid activation function. We have compared our autoencoder to the existing autoencoder technique. In the existing autoencoder technique, we generally use the logsigmoid activation function. But in multiple cases using this technique, we cannot achieve better results. In that case, we may use our technique for achieving better results. Our proposed autoencoder may achieve better results compared to this existing autoencoder technique. The reason behind this is that our modified sigmoid activation function gives more variations for different input values. We have tested our proposed autoencoder on the iris, glass, wine, ovarian, and digit image datasets for comparison propose. The existing autoencoder technique has achieved 96% accuracy on the iris, 91% accuracy on wine, 95.4% accuracy on ovarian, 96.3% accuracy on glass, and 98.7% accuracy on digit (image) dataset. Our proposed autoencoder has achieved 100% accuracy on the iris, wine, ovarian, and glass, and 99.4% accuracy on digit (image) datasets. For more verification of the effeteness of our proposed autoencoder, we have taken three more datasets. They are abalone, thyroid, and chemical datasets. Our proposed autoencoder has achieved 100% accuracy on the abalone and chemical, and 96% accuracy on thyroid datasets.

关键词： autoencoder sigmoid activation function logsigmoid neural network classification stacked autoencoder

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Distributed-ensemble stacked autoencoder model for nonlinear process monitoring

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INFORMATION SCIENCES 2021年 542卷 302-316页

作者： Li, Zhichao Tian, Li Jiang, Qingchao Yan, Xuefeng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Determining whether a fault occurs locally or globally is highly important for large-scale industrial processes involving multiple operating units. Moreover, the complex nonlinearity among process variables is a prominent feature of modern industries. This paper proposes a distributed-ensemble stacked autoencoder (DE-SAE) model based on deep learning technology for monitoring non-linear, large-scale, multi-unit processes. First, the deep features of the variables involved in each operating unit are extracted with the stacked autoencoder (SAE) to represent the essential structure of the unit. Two statistics are separately constructed using the deep features and the reconstruction error for detecting the faults in local units. Subsequently, the deep representations of the variables from each operating unit are modeled with the SAE to extract the global information for global monitoring. The proposed DE-SAE model uses deep learning techniques to solve the complex non-linear relationships in industrial processes, while considering their local and global information. Therefore, the method can explain the monitoring results better. Experimental results obtained from the numerical simulation and Tennessee-Eastman process confirm the feasibility and superiority of this method. (C) 2020 Elsevier Inc. All rights reserved.

关键词： stacked autoencoder Process monitoring Distributed ensemble Joint feature

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