Search Results - Classifier Discrimination Autoencoder

Breast cancer cell nuclei classification in histopathology images using deep neural networks by Feng, Yangqin, Zhang, Lei, Yi, Zhang

“… A supervised classifier is further developed to improve the discrimination of the model…”
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Hyperspectral anomaly detection based on adaptive weighting method combined with autoencoder and convolutional neural network by Hou, Minkai, Wang, Tao, Su, Yanzhao, Cai, Yanping, Cao, Jiping

“… (HSIs) are used to train a CNN as a binary classifier for similarity discrimination in the data preparation stage, where morphological attribute filtering (MAF…”
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Deep Margin Cosine Autoencoder Based Medical Hyperspectral Image Classification for Tumor Diagnosis by Wang, Meiling, Xu, Yongchang, Wang, Zhisheng, Xing, Changda

“… other. Therefore, the extracted features may be incompatible with the used classifier for prediction…”
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Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Combined With Deep Learning for Classification of Idiopathic Recurrent Spontaneous Miscarriage (IRSM) by Sherpa, Dadoma, Rajwade, Dhruva Abhijit, Mitra, Imon, Biswas, Souvik, Sharma, Sunita, Chakraborty, Pratip, Kalapahar, Shovandeb, Chattopadhyay, Ratna, Chaudhury, Koel

“… Furthermore, DL classifiers were implemented to predict IRSM. Hierarchical cluster analysis (HCA…”
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TumorAwareNet: Deep representation learning with attention based sparse convolutional denoising autoencoder for brain tumor recognition by Bodapati, Jyostna Devi, Balaji, Bharadwaj Bagepalli

“…) images suitable for effective tumor recognition. The proposed model employs a Sparse Convolutional Denoising Autoencoder (SCDA…”
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Fault Diagnosis Using Improved Discrimination Locality Preserving Projections Integrated With Sparse Autoencoder by He, Yan-Lin, Li, Kun, Zhang, Ning, Xu, Yuan, Zhu, Qun-Xiong

“…) method named discrimination locality preserving projections integrated with sparse autoencoder (SAEDLPP…”
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Generation and discrimination of autism MRI images based on autoencoder by Shi, Yuxin, Gong, Yongli, Guan, Yurong, Tang, Jiawei

“…This study aims to explore an autoencoder-based method for generating brain MRI images of patients with Autism Spectrum Disorder (ASD…”
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Enhanced Deep Autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System by Vaiyapuri, Thavavel, Binbusayyis, Adel

“… For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder (DAE…”
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Semi-Supervised Gaussian Mixture Variational Autoencoder for Pulse Shape Discrimination by Abdulaziz, Abdullah, Zhou, Jianxin, Di Fulvio, Angela, Altmann, Yoann, McLaughlin, Stephen

“…We address the problem of pulse shape discrimination (PSD) for radiation sources characterization by leveraging a Gaussian mixture variational autoencoder (GMVAE…”
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Discrimination of multiple sclerosis using scanning laser ophthalmoscopy images with autoencoder-based feature extraction by Aghababaei, Ali, Arian, Roya, Soltanipour, Asieh, Ashtari, Fereshteh, Rabbani, Hossein, Kafieh, Raheleh

“…Optical coherence tomography (OCT) investigations have revealed that the thickness of inner retinal layers becomes decreased in multiple sclerosis (MS)…”
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Network traffic discrimination improvement in software defined network (SDN) with deep autoencoder and ensemble method by Shirmarz, Alireza, Ghaffari, Ali

“… The proposed model is composed of the deep autoencoder and the ensemble method classifier to be implemented in the centralized…”
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Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach by Ogundokun, Roseline Oluwaseun, Owolawi, Pius Adewale, Tu, Chunling, van Wyk, Etienne

“…) and ensembles of machine learning classifiers to improve lymphoma subtype identification. Methods: Convolutional autoencoder…”
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Classification of hyperspectral images by deep learning of spectral-spatial features by Ding, Haiyong, Xu, Luming, Wu, Yue, Shi, Wenzhong

“… In this study, a hybrid stacked autoencoder (SAE) architecture and support vector machine (SVM) classifier was constructed to classify the HSI…”
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Aligning Discriminative and Representative Features: An Unsupervised Domain Adaptation Method for Building Damage Assessment by Li, Yundong, Hu, Wei, Li, Hongguang, Dong, Han, Zhang, Baochang, Tian, Qing

“… Although machine learning has made remarkable improvement in building damage classification, it remains challenging because classifiers must be trained using a massive amount of labeled data…”
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Regional Forecast of Heavy Precipitation and Interpretability Based on TD-VAE by Lu, Zhiying, Ding, Xudong, Yan, Qin, Guo, Jianlin

“… Moreover, a variational autoencoder-based transfer discrimination (TD-VAE) algorithm is employed to tackle the difficulty of class imbalance…”
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Discriminating Known from Unknown Objects via Structure-Enhanced Recurrent Variational AutoEncoder by Wu, Aming, Deng, Cheng

“…) data to improve the detector's discrimination ability. In this paper, we first propose a method of Structure-Enhanced Recurrent Variational AutoEncoder (SR-VAE…”
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Improving performance of one-class classifiers applied to anomaly detection in oil wells by Machado, André Paulo Ferreira, Vargas, Ricardo Emanuel Vaz, Ciarelli, Patrick Marques, Munaro, Celso Jose

“… In this paper, a proposal for detecting such anomalies using Long Short-Term Memory (LSTM) autoencoder and one-class Support Vector Machine (SVM) is presented…”
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Infrared Thermography Anomaly Detection Using VAESIMCA Approach by Petersen, Akam, Brabrand, Mikkel, Kucheryavskiy, Sergey

“… Deep learning can solve this problem efficiently, however most of the deep learning-based methods currently developed for thermographic image analysis are based on discrimination models and hence…”
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Integrating in-field Vis-NIR leaf spectroscopy and deep learning feature extraction for growth-stage dependent and independent genotyping of wheat plants by Salehi, Bakhtiyar, Mireei, Seyed Ahmad, Jafari, Mehrnoosh, Hemmat, Abbas, Majidi, Mohammad Mahdi

“… To handle the large amount of spectral data generated, a deep learning technique was first used to extract advanced features from the spectral data using the stacked autoencoder (SAE) approach…”
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Multiclass classification of patients during different stages of Alzheimer's disease using fMRI time-series by Ahmadi, Hessam, Fatemizadeh, Emad, Motie-Nasrabadi, Ali

“…Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI…”
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