Search Results - "deep sparse autoencoder"

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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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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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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Predicting potential microbe–disease associations based on multi-source features and deep learning by Wang, Liugen, Wang, Yan, Xuan, Chenxu, Zhang, Bai, Wu, Hanwen, Gao, Jie

“…Abstract Studies have confirmed that the occurrence of many complex diseases in the human body is closely related to the microbial community, and microbes can…”
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Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition by Li, Qi, Liu, Yunqing, Shang, Yujie, Zhang, Qiong, Yan, Fei

“…Recently, emotional electroencephalography (EEG) has been of great importance in brain–computer interfaces, and it is more urgent to realize automatic emotion…”
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Deep sparse autoencoder integrated with three‐stage framework for glaucoma diagnosis by Wang, Wenle, Zhou, Wei, Ji, Jianhang, Yang, Jikun, Guo, Wei, Gong, Zhaoxuan, Yi, Yugen, Wang, Jianzhong

“…Recently, end‐to‐end deep neural networks‐based glaucoma diagnosis approaches have been gaining much attention. However, the feature extractor and classier in…”
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Self-Adaptive Imbalanced Domain Adaptation With Deep Sparse Autoencoder by Zhu, Yi, Wu, Xindong, Li, Yun, Qiang, Jipeng, Yuan, Yunhao

“…Domain adaptation aims to transfer knowledge between different domains to develop an effective hypothesis in the target domain with scarce labeled data, which…”
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Detection and Classification of Surface Cracks Using Deep Learning Based Autoencoders in Eddy Current Testing by Fatima, Barrarat, Bachir, Helifa, Samir, Bensaid, Karim, Rayane, IbnKhaldoun, Lefkaier

“…Industrial equipment subjected to rigorous conditions of high speed and pressure leads to the development of cracks on metal surfaces. These cracks reduce the…”
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Drug Repositioning Based on Deep Sparse Autoencoder and Drug–Disease Similarity by Lei, Song, Lei, Xiujuan, Chen, Ming, Pan, Yi

“…Drug repositioning is critical to drug development. Previous drug repositioning methods mainly constructed drug–disease heterogeneous networks to extract…”
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Detection of Pitting in Gears Using a Deep Sparse Autoencoder by Qu, Yongzhi, He, Miao, Deutsch, Jason, He, David

“…In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method…”
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Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics by Yousefi, Bardia, Akbari, Hamed, Maldague, Xavier P.V.

“…Breast cancer is the most common cancer in women. Early diagnosis improves outcome and survival, which is the cornerstone of breast cancer treatment…”
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Integration of Deep Sparse Autoencoder and Particle Swarm Optimization to Develop a Recommender System by Ahmadian, Milad, Ahmadi, Mahmood, Ahmadian, Sajad, Jafar Jalali, Seyed Mohammad, Khosravi, Abbas, Nahavandi, Saeid

“…Recommender systems are known as intelligent systems which have many applications in enormous domains such as social networks, e-commerce services, and online…”
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A new deep sparse autoencoder for community detection in complex networks by Fei, Rong, Sha, Jingyuan, Xu, Qingzheng, Hu, Bo, Wang, Kan, Li, Shasha

“…Feature dimension reduction in the community detection is an important research topic in complex networks and has attracted many research efforts in recent…”
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Community Detection Based on Graph Representation Learning in Evolutionary Networks by Chen, Dongming, Nie, Mingshuo, Wang, Jie, Kong, Yun, Wang, Dongqi, Huang, Xinyu

“…Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The…”
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Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal by Saufi, Syahril Ramadhan, Isham, Muhammad Firdaus, Ahmad, Zair Asrar, Hasan, M. Danial Abu

“…Intelligent fault diagnosis (IFD) is an effective system to ensure the safe operation of mechanical components such as bearings, gears, and blades. The main…”
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Artificial Intelligence Applications and Innovation

“…The idea of an intelligent machine has fascinated humans for centuries. Intelligence can be defined as the capacity for learning, reasoning, understanding, or…”
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Biosensors: 10th Anniversary Feature Papers

“…Biosensors are analytical devices used for the detection of a chemical substance, or analyte, which combines a biological component with a physicochemical…”
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Remaining Useful Life Prediction of Gear Pump Based on Deep Sparse Autoencoders and Multilayer Bidirectional Long–Short–Term Memory Network by Zhang, Peiyao, Jiang, Wanlu, Shi, Xiaodong, Zhang, Shuqing

“…Prediction of remaining useful life is crucial for mechanical equipment operation and maintenance. It ensures safe equipment operation, reduces maintenance…”
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Bearing Fault Diagnosis Using Deep Sparse Autoencoder by Saufi, S R, Ahmad, Z A B, Leong, M S, Hee, L M

“…Rolling element bearing is an important component in various machinery. Faulty on bearing cause severe equipment damage that lead to high maintenance cost. The…”
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Fault Prediction Method of Gear Based on DSAE and GRU Network by Jiang, Liying, Qu, Liqiang, Cui, Xiao, Wang, Jinglin, Yu, Mingyue, Tang, Xiaochu

“…Aiming at the problems of excessive reliance on manual experience and processing of complex signals in traditional gear fault prediction methods, a gear fault…”
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