Search Results - Cascaded stacked autoencoder

SAINT-IIOT: Elk herd optimized deep learning model for efficient anomaly detection in the IIoT by Mahalakshmi, K., Jaison, B.

“… To address these issues, a novel cascaded Stacked Autoencoder INtegrated aTtention CNN-BiGRU for IIoT (SAINT-IIoT…”
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Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network by Aslam, Muhammad Aqeel, Xue, Cuili, Chen, Yunsheng, Zhang, Amin, Liu, Manhua, Wang, Kan, Cui, Daxiang

“… In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples…”
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A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery by Xiong, Qiongbing, Wu, Xuecheng, Yu, Cizhen, Hosseinzadeh, Hasan

“… The methodology involved utilizing the VGG-19 model for feature extraction, dimensionality reduction, and a stacked autoencoder optimized with a boosted version of the Big Bang Crunch Theory…”
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Stacked-autoencoder-based model for COVID-19 diagnosis on CT images by Li, Daqiu, Fu, Zhangjie, Xu, Jun

“… In this paper, a stacked autoencoder detector model is proposed to greatly improve the performance…”
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Optimized feature fusion-based modified cascaded kernel extreme learning machine for heart disease prediction in E-healthcare by Kumar, Sumit, Gola, Kamal Kumar, Jee, Narayan, Singh, Brij Mohan

“… To conquer these drawbacks, the proposed research article presents an innovative cascaded extreme learning machine for effective heart disease (HD) prediction…”
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Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder by Kang, Miao, Ji, Kefeng, Leng, Xiangguang, Xing, Xiangwei, Zou, Huanxin

“… In order to obtain distinctive features, this paper proposes a feature fusion algorithm for SAR target recognition based on a stacked autoencoder (SAE…”
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Local receptive field constrained stacked sparse autoencoder for classification of hyperspectral images by Wan, Xiaoqing, Zhao, Chunhui

“…As a competitive machine learning algorithm, the stacked sparse autoencoder (SSA) has achieved outstanding popularity in exploiting high-level features for classification of hyperspectral images…”
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ROPRNet: Deep learning-assisted recurrence prediction for retinopathy of prematurity by Huang, Peijie, Xie, Yiying, Wu, Rong, Lin, Qiuxia, Cai, Nian, Chen, Haitao, Feng, Songfu

“…•ROPRNet: a dual-modal model for recurrence prediction of retinopathy of prematurity.•Corrected gestational age at treatment is involved as a key factor for…”
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Rough-Wavelet Feature Space, Deep Autoencoder, and Hyperspectral Image Classification by Meher, Saroj K.

“…) and stacked deep autoencoder (SDAE). Stationary WT extracts the features by exploiting the spectral-spatial information and stacked DAE extracts through representative learning of input information…”
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A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited–memory BFGS optimization algorithms by Badem, Hasan, Basturk, Alper, Caliskan, Abdullah, Yuksel, Mehmet Emin

“…) to tune the parameters of a DNN structure, which includes one or more autoencoder layers cascaded to a softmax classification layer…”
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Intrusion Detection Model for Industrial Internet of Things Based on Improved Autoencoder by Zhang, Wumei, Zhang, Yongzhen

“…) have attracted more and more attention. Aiming to improve the security of industrial IoT, a detection method using stacked sparse autoencoder network model is proposed…”
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Robust face alignment with cascaded coarse-to-fine auto-encoder network by Peng, Cheng, Ge, Yongxin, Hong, Mingjian, Huang, Sheng, Yang, Dan

“… The second level autoencoder networks are constructed by cascading several sub stacked auto-encoder networks (SSAN…”
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Privacy-Preserving Baseline Load Reconstruction for Residential Demand Response Considering Distributed Energy Resources by Chen, Yang, Chen, Chunyu, Zhang, Xiao, Cui, Mingjian, Li, Fangxing, Wang, Xinan, Yin, Shengfei

“… Targeting the CBL reconstruction of residential prosumers, a regression-based estimation scheme is proposed using stacked autoencoders (SAEs…”
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Constructing a Deep Regression Model Utilizing Cascaded Sparse Autoencoders and Stochastic Gradient Descent by Moussavi-Khalkhali, Arezou, Jamshidi, Mo

“… The focus of this research is on exploiting and learning from the determining features of continuous data via stacked autoencoders, thus increasing the prediction accuracy of regression method…”
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Feature fusion models via stacked autoencoders: Applications to vehicular traffic flow prediction and Alzheimer's disease stage detection by Moussavi-Khalkhali, Arezou

“…The aim of this dissertation is to explore the deep architectures via stacked autoencoders (AE…”
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Chapter 12 - Enhancing crop productivity through autoencoder-based disease detection and context-aware remedy recommendation system by Abinaya, S., Devi, M.K. Kavitha

“…According to experts, recent developments in agricultural sectors are inherently connected to the production and all other facets of agriculture to increase…”
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Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework by Ullah, Hayat, Munir, Arslan

“…Vision-based human activity recognition (HAR) has emerged as one of the essential research areas in video analytics. Over the last decade, numerous advanced…”
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Deep Learning-Based Approaches for Brain Tumour Segmentation and Classification by Baiju, Vidya, J, Akshitha, Naveen S, Namasiyava

“…Brain tumours are caused by the abnormal growth of cells in the brain. This occurs mainly due to genetic changes or exposure to X-ray radiation. When the…”
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Spectral Partitioning Residual Network With Spatial Attention Mechanism for Hyperspectral Image Classification by Zhang, Xiangrong, Shang, Shouwang, Tang, Xu, Feng, Jie, Jiao, Licheng

“… The SPRN splits the input spectral bands into several nonoverlapping continuous subbands and uses cascaded parallel improved residual blocks to extract spectral-spatial features from these subbands, respectively…”
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Class-wise deep dictionaries for EEG classification by Khurana, Prerna, Majumdar, Angshul, Ward, Rabab

“…), Stacked Autoencoder, Convolutional Neural Net (CNN) and Label Consistent KSVD (dictionary learning). We find that our proposed…”
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