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A model-based collaborate filtering algorithm based on stacked autoencoder

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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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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stacked autoencoder for Arabic handwriting word recognition

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2021年第6期24卷 629-638页

作者： Benbakreti, Samir Benouis, Mohamed Roumane, Ahmed Benbakreti, Soumia Natl Inst Telecommun & ICT Dept Special Oran Algeria Univ Msila Comp Sci Dept Msila Algeria Univ Djillali Liabes Sidi Bel Abbes Lab Math Sidi Bel Abbes Algeria

Arabic handwritten recognition systems face several challenges such as the very diverse scripting styles, the presence of pseudo-words and the position-dependent shape of a character inside a given word, etc. These characteristics complicate the task of features extraction. Our proposed solution to this problem is a stacked autoencoder (SAE) unsupervised learning approach applied to resolve the unconstrained Arabic handwritten word recognition. Our strategy consists in using an unsupervised pre-training stage, i.e., SAE which will extract the features layer by layer, then, through fine-tuning, the global system will be used for classification tasks. By exploiting this, our system gets the advantage of applying a holistic approach, i.e., without word segmentation. In order to train our model, we have enhanced the NOUN v3 hybrid (i.e., offline and online) database that contains 9,600 handwritten Arabic words and 4,800 characters. However, this work is focusing on the offline recognition of Arabic word handwriting using a SAE-based architecture for images classification. Our experiment study shows that after a careful tuning of the main SAE parameters we got good results (98.03%).

关键词： Arabic handwriting offline recognition neural network deep learning unsupervised training stacked autoencoder SAE

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Optimized stacked autoencoder for IoT Enabled Financial Crisis Prediction Model

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Computers, Materials & Continua 2022年第4期71卷 1079-1094页

作者： Mesfer Al Duhayyim Hadeel Alsolai Fahd N.Al-Wesabi Nadhem Nemri Hany Mahgoub Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,Financial Technology(FinTech)has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or *** crisis prediction(FCP)is an essential topic in business sector that finds it useful to identify the financial condition of a financial *** the same time,the development of the internet of things(IoT)has altered the mode of human interaction with the physical *** IoT can be combined with the FCP model to examine the financial data from the users and perform decision making *** paper presents a novel multi-objective squirrel search optimization algorithm with stacked autoencoder(MOSSA-SAE)model for FCP in IoT *** MOSSA-SAE model encompasses different subprocesses namely preprocessing,class imbalance handling,parameter tuning,and ***,the MOSSA-SAE model allows the IoT devices such as smartphones,laptops,etc.,to collect the financial details of the users which are then transmitted to the cloud for further *** addition,SMOTE technique is employed to handle class imbalance *** goal of MOSSA in SMOTE is to determine the oversampling rate and area of nearest neighbors of ***,SAE model is utilized as a classification technique to determine the class label of the financial *** the same time,the MOSSA is applied to appropriately select the‘weights’and‘bias’values of the *** extensive experimental validation process is performed on the benchmark financial dataset and the results are examined under distinct *** experimental values ensured the superior performance of the MOSSA-SAE model on the applied dataset.

关键词： Financial data financial crisis prediction class imbalance problem internet of things 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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User Behavior Analysis Based on stacked autoencoder and Clustering in Complex Power Grid Environment

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第12期23卷 25521-25535页

作者： Deng, Song Cai, Qingyuan Zhang, Zi Wu, Xindong Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210003 Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Hefei 230009 Peoples R China Mininglamp Technol Mininglamp Acad Sci Shanghai 200232 Peoples R China

Analyzing user behavior characteristics in a complex power grid environment is essential for user behavior planning and resource coordination optimization. Traditional user behavior analysis methods based on model-driven and causal analysis have the disadvantages of strong subjectivity and physical models that are difficult to deal with the randomness and uncertainty of user behavior in complex grid environments. In this paper, we use unsupervised learning methods to analyze user behavior in complex power grid environments, and propose user behavior analysis methods based on stacked autoencoder and clustering. We first reduce the complexity of user behavior data by proposing adaptive feature selection algorithm of user behavior based on stacked autoencoder and unsupervised learning (AFS-SAEUL). Finally, we build a user behavior analysis model based on adaptive feature selection and improved clustering (UBA-AFSIC). The model improved the performance of unsupervised classification of user behavior by fusing the adaptive generation strategy of the initial cluster centers. The simulation experiment results on two real electricity datasets and one public electric vehicle charging dataset show that compared with the existing feature selection algorithm and clustering algorithm, the algorithms proposed in this paper have higher feature selection rate and better clustering performance.

关键词： Feature extraction Clustering algorithms Analytical models Power demand Correlation Power grids Load modeling Feature selection stacked autoencoder unsupervised learning power consumption behavior analysis electric vehicle charging behavior analysis

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Hybrid Image Compression-Encryption Scheme Based on Multilayer stacked autoencoder and Logistic Map

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China Communications 2022年第1期19卷 238-252页

作者： Neetu Gupta Ritu Vijay Computer Science&Engineering Department Banasthali VidyapithBanasthaliIndia Department of Electronics Engineering Banasthali VidyapithBanasthaliIndia.

Secure transmission of images over a communication channel, with limited data transfer capacity, possesses compression and encryption schemes. A deep learning based hybrid image compression-encryption scheme is proposed by combining stacked auto-encoder with the logistic map. The proposed structure of stacked autoencoder has seven multiple layers, and back propagation algorithm is intended to extend vector portrayal of information into lower vector space. The randomly generated key is used to set initial conditions and control parameters of logistic map. Subsequently, compressed image is encrypted by substituting and scrambling of pixel sequences using key stream sequences generated from logistic *** proposed algorithms are experimentally tested over five standard grayscale images. Compression and encryption efficiency of proposed algorithms are evaluated and analyzed based on peak signal to noise ratio(PSNR), mean square error(MSE), structural similarity index metrics(SSIM) and statistical,differential, entropy analysis respectively. Simulation results show that proposed algorithms provide high quality reconstructed images with excellent levels of security during transmission..

关键词： compression-encryption stacked autoencoder chaotic system back propagation algorithm logistic map

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Transfer learning based on improved stacked autoencoder for bearing fault diagnosis

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KNOWLEDGE-BASED SYSTEMS 2022年第0期256卷

作者： Luo, Shuyang Huang, Xufeng Wang, Yanzhi Luo, Rongmin Zhou, Qi Huazhong Univ Sci & Technol Sch Aerosp Engn 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China

Deep transfer learning algorithm is regarded as a promising method to address the issue of rolling bearing fault diagnosis with limited labeled data. stacked autoencoder (SAE) has been widely employed in deep transfer learning research since it is a semi-supervised algorithm. However, there are still some limitations for the transfer learning based on SAE, including the vanishing gradient problem caused by the sigmoid activation function in SAE, and low accuracy under the condition of cross-domain or limited labeled training data. In this work, an improved SAE based on convolutional shortcuts and domain fusion strategy (ISAE-CSDF) is proposed for fault diagnosis of rolling bearing. The sparse term Kullback-Leibler (KL) divergence in the original SAE is replaced with the convolutional shortcuts to prevent vanishing gradient problem and improve the feature extraction ability. The domain fusion strategy can transfer commonly shared feature information from various domains. The feasibility of ISAE-CSDF is validated on two publicly available bearing datasets and a custom-built experiment device. Results show that ISAE-CSDF outperforms the state-of-art methods in the context of different working conditions, cross-domain, and limited labeled data. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Transfer learning Fault diagnosis stacked autoencoder Convolutional shortcuts Domain fusion

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Classification of Imbalanced Bioassay Data with Features Learned Using stacked autoencoder 15

Classification of Imbalanced Bioassay Data with Features Lea...

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15th International Conference on Machine Vision (ICMV)

作者： Shah, Jeni Joshi, Manjunath Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar India

ISBN: (纸本)9781510666184;9781510666191

Bioassay data classification is an important task in drug discovery. However, the data used in classification is highly imbalanced, leading to inaccuracies in classification for the minority class. We propose a novel approach for classification in which we train separate models by using different features that are derived by training stacked autoencoders (SAE). Experiments are performed on 7 bioassay datasets, in which each data file consists of feature descriptors for every compound along with class label of compound being active, or inactive. We first perform data cleaning using borderline synthetic minority oversampling technique (SMOTE) followed by removing the Tomek links, and then learn different features hierarchically, based on the cleaned data or feature vectors. We then train separate cost-sensitive feed-forward neural network (FNN) classifiers using the hierarchical features in order to obtain the final classification. To increase the True Positive Rate (TPR), a test sample is labeled as active if at least one classifier predicts it as active. In this paper, we demonstrate that by data cleaning and learning separate classifiers one can improve the TPR and F1 score when compared to other machine learning approaches. To the best of our knowledge, the researchers have not yet attempted the use of SAE and FNN for classifying bioassay data.

关键词： stacked autoencoder SMOTE Imbalanced data

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Hybrid Sea Lion Crow Search Algorithm-Based stacked autoencoder for Drug Sensitivity Prediction From Cancer Cell Lines

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第1期13卷 1-21页

作者： Mahajan, Rupali A. Shaikh, Nilofer Karim Tikhe, Atharva Balkrishna Vyas, Renu Chavan, Smita M. MIT ADT Univ Pune Maharashtra India Govt Coll Engn Pune Maharashtra India

Providing better therapy to cancer patients remains a major task due to drug resistance of tumor cells. This paper proposes a sea lion crow search algorithm (SLCSA) for drug sensitivity prediction. The drug sensitivity from cultured cell lines is predicted using stacked autoencoder, and the proposed SLCSA is derived from a combination of sea lion optimization (SLnO) and crow search algorithm (CSA). The implemented approach has offered superior results. The maximum value of testing accuracy for normal is 0.920, leukemia is 0.920, NSCLC is 0.912, and urogenital is 0.914.

关键词： Cancer Cell Lines Crow Search Algorithm (CSA) Drug Sensitivity Prediction Sea Lion Optimization (SLnO) stacked autoencoder

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