Search Results - "Sparse Autoencoder"

Autoencoder Artificial Neural Network Model for Air Pollution Index Prediction by Basir, Nor Irwin, Tan, Kathlyn Kaiyun, Djarum, Danny Hartanto, Ahmad, Zainal, Vo, Dai-Viet N., Jie, Zhang

“…Air pollution, a significant global challenge driven by industrialization, urbanization, and population growth, is caused by the emission of harmful gases,…”
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EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder by Liu, Junxiu, Wu, Guopei, Luo, Yuling, Qiu, Senhui, Yang, Su, Li, Wei, Bi, Yifei

“…Emotion classification based on brain-computer interface (BCI) systems is an appealing research topic. Recently, deep learning has been employed for the…”
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A deep learning based trust- and tag-aware recommender system by Ahmadian, Sajad, Ahmadian, Milad, Jalili, Mahdi

“…Recommender systems are popular tools used in many applications, such as e-commerce, e-learning, and social networks to help users select their desired items…”
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Application of deep canonically correlated sparse autoencoder for the classification of schizophrenia by Li, Gang, Han, Depeng, Wang, Chao, Hu, Wenxing, Calhoun, Vince D., Wang, Yu-Ping

“…•A deep learning model, the deep canonically correlated sparse autoencoder model, is proposed for schizophrenia classification.•The deep learning model, with…”
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Research on the remaining useful life prediction method for lithium-ion batteries by fusion of feature engineering and deep learning by Zhao, Bo, Zhang, Weige, Zhang, Yanru, Zhang, Caiping, Zhang, Chi, Zhang, Junwei

“…Lithium-ion batteries age continuously during usage due to their characteristics and the influence of various external factors, but as degradation deepens, it…”
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Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction by Chen, Luefeng, Zhou, Mengtian, Su, Wanjuan, Wu, Min, She, Jinhua, Hirota, Kaoru

“…Deep neural network (DNN) has been used as a learning model for modeling the hierarchical architecture of human brain. However, DNN suffers from problems of…”
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A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines by Jia, Feng, Lei, Yaguo, Guo, Liang, Lin, Jing, Xing, Saibo

“…In traditional intelligent fault diagnosis methods of machines, plenty of actual effort is taken for the manual design of fault features, which makes these…”
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A Deep Learning Approach for Network Intrusion Detection System by Javaid, Ahmad, Quamar Niyaz, Sun, Weiqing, Alam, Mansoor

“…A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in their organizations. However, many challenges…”
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An industrial process monitoring method based on fusion of multi-scale sparse autoencoder and dual-branch slow feature architecture by Ling, Guobi, Wang, Zhiwen, Wang, Jieying, Shi, Yaoke, Li, Long, Zeng, Jingxiao

“…•MSAE-DSFA merges multi-scale sparse AEs and dual slow feature for monitoring.•Tackles info redundancy, nonlinearity and slow faults in industrial…”
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A hybrid Intrusion Detection System based on Sparse autoencoder and Deep Neural Network by Narayana Rao, K., Venkata Rao, K., P.V.G.D., Prasad Reddy

“…A large number of attacks are launched daily in the era of the internet and with a large number of users. Nowadays, effective detection of numerous attacks…”
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Nondestructive evaluation of Zn content in rape leaves using MSSAE and hyperspectral imaging by Fu, Lvhui, Sun, Jun, Wang, Simin, Xu, Min, Yao, Kunshan, Zhou, Xin

“…[Display omitted] •Hyperspectral image applied for non-destructive detection of Zn content in plants.•An MSSAE was proposed for extracting deep features from…”
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Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing by Sun, Chuang, Ma, Meng, Zhao, Zhibin, Tian, Shaohua, Yan, Ruqiang, Chen, Xuefeng

“…Deep learning with ability to feature learning and nonlinear function approximation has shown its effectiveness for machine fault prediction. While, how to…”
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Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network by Chen, Zhuyun, Li, Weihua

“…To assess health conditions of rotating machinery efficiently, multiple accelerometers are mounted on different locations to acquire a variety of possible…”
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EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing by Ozkan, Savas, Kaya, Berk, Akar, Gozde Bozdagi

“…Data acquired from multichannel sensors are a highly valuable asset to interpret the environment for a variety of remote sensing applications. However, low…”
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Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample by Saufi, Syahril Ramadhan, Ahmad, Zair Asrar Bin, Leong, Mohd Salman, Lim, Meng Hee

“…Massive volumes of data are needed for deep learning (DL) models to provide accurate diagnosis results. Numerous studies of fault diagnosis systems have…”
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Emotion recognition using secure edge and cloud computing by Hossain, M. Shamim, Muhammad, Ghulam

“…•An automatic audio-visual emotion recognition system based on CNN was proposed.•The audio-visual features were fused using a cascaded deep sparse…”
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Deep learning for polyp recognition in wireless capsule endoscopy images by Yuan, Yixuan, Meng, Max Q.‐H.

“…Purpose Wireless capsule endoscopy (WCE) enables physicians to examine the digestive tract without any surgical operations, at the cost of a large volume of…”
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A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of U.S by Qiao, Weibiao, Liu, Wei, Liu, Enbin

“…The prediction model's performance in view of the wavelet transform (WT) is affected because the wavelet basis function (WBF) and its orders and layers are…”
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Detection and Classification of Transmission Line Faults Based on Unsupervised Feature Learning and Convolutional Sparse Autoencoder by Chen, Kunjin, Hu, Jun, He, Jinliang

“…We present in this paper a novel method for fault detection and classification in power transmission lines based on convolutional sparse autoencoder. Contrary…”
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Intelligent simultaneous fault diagnosis for solid oxide fuel cell system based on deep learning by Zhang, Zehan, Li, Shuanghong, Xiao, Yawen, Yang, Yupu

“…•An intelligent simultaneous fault diagnosis method is proposed for SOFC systems.•Stacked Sparse Autoencoder is used to solve simultaneous fault diagnosis…”
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