Suchergebnisse - stacked sparse denoising autoencoder (SSDAE)

A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings von Sun, Meidi, Wang, Hui, Liu, Ping, Huang, Shoudao, Fan, Peng

“… •Fault diagnosis by stacked autoencoder requires less professional knowledge.•Domain adaptation by optimized transfer learning reduces the algorithm’s complexity …”
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Enhanced CNN for Fruit Disease Detection and Grading Classification Using SSDAE-SVM for Postharvest Fruits von Patel, Himanshu B., Patil, Nitin J.

“… In the realm of agriculture, leveraging image processing has become pivotal for robust image analysis, especially in detecting fruit diseases. However, …”
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Toward Robust Fault Identification of Complex Industrial Processes Using Stacked Sparse-Denoising Autoencoder With Softmax Classifier von Liu, Jinping, Xu, Longcheng, Xie, Yongfang, Ma, Tianyu, Wang, Jie, Tang, Zhaohui, Gui, Weihua, Yin, Huazhan, Jahanshahi, Hadi

“… This article proposes a robust end-to-end deep learning-induced fault recognition scheme by stacking multiple sparse-denoising autoencoders with a Softmax classifier, called stacked spare-denoising autoencoder (SSDAE …”
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Learning social representations with deep autoencoder for recommender system von Pan, Yiteng, He, Fazhi, Yu, Haiping

“… Based on the intuition that users are influenced by their social friends, these methods are capable of addressing the data sparse problem and improving the performance of recommender systems …”
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Building feature space of extreme learning machine with sparse denoising stacked-autoencoder von Cao, Le-le, Huang, Wen-bing, Sun, Fu-chun

“… Deep learning algorithms such as stacked autoencoder (SAE) and deep belief network (DBN) are built on learning several levels of representation of the input …”
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Software defect prediction based on stacked sparse denoising autoencoders and enhanced extreme learning machine von Zhang, Nana, Ying, Shi, Zhu, Kun, Zhu, Dandan

“… To address these two issues, a novel defect prediction model called SSEPG based on Stacked Sparse Denoising AutoEncoders (SSDAE …”
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Deep learning-based feature selection for detection of autism spectrum disorder von Nafisah, Ibrahim, Mahmoud, Nermine, Ewees, Ahmed A., Khattap, Mohamed G., Dahou, Abdelghani, Alghamdi, Safar M., Fares, Ibrahim A., Azmi Al-Betar, Mohammed, Abd Elaziz, Mohamed

“… A hybrid model combining Stacked Sparse Denoising Autoencoder (SSDAE) and Multi-Layer Perceptron (MLP …”
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A novel optimization-driven deep learning framework for the detection of DDoS attacks von Batchu, Raj Kumar, Bikku, Thulasi, Thota, Srinivasarao, Seetha, Hari, Ayoade, Abayomi Ayotunde

“… Distributed denial of service (DDoS) attack is one of the most hazardous assaults in cloud computing or networking. By depleting resources, this attack renders …”
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Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder von Dai, Juying, Tang, Jian, Shao, Faming, Huang, Shuzhan, Wang, Yangyang

“… To realize representative features mining and automatic recognition of bearing health condition, a diagnostic model of stacked sparse denoising autoencoder (SSDAE …”
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A Novel Anti-Risk Method for Portfolio Trading Using Deep Reinforcement Learning von Yue, Han, Liu, Jiapeng, Tian, Dongmei, Zhang, Qin

“… ). It consists of a stacked sparse denoising autoencoder (SSDAE) network and an actor–critic based reinforcement learning (RL) agent …”
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A deep neural network with hybrid spotted hyena optimizer and grasshopper optimization algorithm for copy move forgery detection von Gupta, Ruchi, Singh, Pushpa, Alam, Tanweer, Agarwal, Shivani

“… Proposed a novel deep learning technique stacked sparse denoising autoencoder (SSDAE) to classify the images as legitimate …”
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Applications of Markov Decision Process Model and Deep Learning in Quantitative Portfolio Management during the COVID-19 Pandemic von Yue, Han, Liu, Jiapeng, Zhang, Qin

“… Whether for institutional investors or individual investors, there is an urgent need to explore autonomous models that can adapt to the non-stationary, …”
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Modified metaheuristics with stacked sparse denoising autoencoder model for cervical cancer classification von Vaiyapuri, Thavavel, Alaskar, Haya, Syed, Liyakathunisa, Aljohani, Eman, Alkhayyat, Ahmed, Shankar, K., Kumar, Sachin

“… •Present an optimal SSDAE to classify cervical cancer on pap smear images.•Employ Kapur's entropy segmentation and efficientnet feature extraction …”
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Flatness pattern recognition based on stacked sparse denoising autoencoder and improved Osprey optimisation algorithm kernel-extreme learning machine von Zhou, Yaluo, Zhang, Shaochuan, Liu, Wenguang, Zhang, Ruicheng

“… proposes a flatness recognition method based on stack sparse denoising autoencoder (SSDAE) with improved Osprey optimisation algorithm kernel-extreme learning machine …”
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A novel stacked sparse denoising autoencoder for mammography restoration to visual interpretation of breast lesion von Ghosh, Swarup Kr, Biswas, Biswajit, Ghosh, Anupam

“… This paper proposes a deep unsupervised learning based denoising autoencoder model for the restoration of degraded mammogram with visual interpretation of breast lumps or lesion in mammography images (called SSDAE …”
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An Anomaly Detection Method for Nonlinear Industrial Process Using Sparse Stacked Denoising Autoencoder von Yang, Mingwei, Liu, YanHua, Chen, Hong, Lin, Jiefei, Lin, Haoqiang

“… Therefore, a sparse stacked denoise autoencoder(SSDAE) based anomaly detection model is proposed in the paper, which uses the autoencoder model to capture the nonlinear …”
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Coupled Analysis Dictionary Learning to inductively learn inversion: Application to real-time reconstruction of Biomedical signals von Gupta, Kavya, Bhowmick, Brojeshwar, Majumdar, Angshul

“… is that the inversion is time-consuming and hence not suitable for real-time applications. With the recent advent of deep learning, Stacked Sparse Denoising Autoencoder (SSDAE …”
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Coupled Analysis Dictionary Learning to inductively learn inversion: Application to real-time reconstruction of Biomedical signals von Gupta, Kavya, Bhowmick, Brojeshwar, Majumdar, Angshul

“… With the recent advent of deep learning, Stacked Sparse Denoising Autoencoder (SSDAE) has been used for learning inversion in an inductive setup …”
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