Search Results - Hybrid classification autoencoder

A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery by Wu, Xinya, Zhang, Yan, Cheng, Changming, Peng, Zhike

“…•A novel semi-supervised fault diagnosis method is proposed.•The model can be trained using both labeled and unlabeled data simultaneously.•The performance of…”
Get full text

Out-of-Distribution-Aware Structural Damage Classification via Hybrid Variational Autoencoder and Gradient Boosting by Prayogo, Handy, Yang, I-Tung

“…Structural damage classification is vital for the safety and longevity of critical infrastructure…”
Get full text

Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach by Siva Raja, P.M., rani, Antony Viswasa

“… This paper developed a brain tumor classification using a hybrid deep autoencoder with a Bayesian fuzzy clustering-based segmentation approach…”
Get full text

Evaluation and Optimisation of a Generative-Classification Hybrid Variational Autoencoder in the Search for Resonances at the LHC by Stevenson, Finn, Lieberman, Benjamin, Swain, Abhaya, Mellado, Bruce

“… This research makes use of a Variational Autoencoder (VAE), in the search for new bosons. The functionality of a VAE to be trained as both a generative model and a classification model makes the architecture an attractive option for aiding the search…”
Get full text

Clustering and enhanced classification using a hybrid quantum autoencoder by Srikumar, Maiyuren, Hill, Charles D, Hollenberg, Lloyd C L

Get full text

Spindle Autoencoder-CNN hybrid model for cardiac arrhythmia classification by Akkuş, Merve, Karabatak, Murat, Tekin, Ramazan

“… In this study, we propose a novel deep learning-based classification framework that integrates a Modified Spindle Autoencoder (MSCAE…”
Get full text

CAD-Skin: A Hybrid Convolutional Neural Network–Autoencoder Framework for Precise Detection and Classification of Skin Lesions and Cancer by Khan, Abdullah, Sajid, Muhammad Zaheer, Khan, Nauman Ali, Youssef, Ayman, Abbas, Qaisar

“… neural networks and autoencoders to improve the classification efficiency of skin cancer…”
Get full text

NeuroHybridNet: A Hybrid EEG Classification Model with Autoencoder-Enhanced Fractal-Wavelet Features by Tekin, Hazret

“… and accurate detection of mental fatigue or distraction is critical for safety and performance.This study proposes WaveFrac-AE, a hybrid EEG classification model…”
Get full text

Hybrid attribute conditional adversarial denoising autoencoder for zero-shot classification of mechanical intelligent fault diagnosis by Lv, Haixin, Chen, Jinglong, Pan, Tongyang, Zhou, Zitong

“… Inspired by the zero-shot learning method, hybrid attribute conditional adversarial denoising autoencoder (CADAE…”
Get full text

Enhancing GNSS Localization in Urban Canyons with a Hybrid CNN-Autoencoder Approach to LOS/NLOS Classification by Titouni, S., Messaoudene, I., Himeur, Y., Dawoud, D. W., Belazzoug, M., Hammache, B., Chetouah, F., Atalla, S., Mansoor, W.

“… A hybrid deep learning (DL) framework is proposed to address these constraints, integrating a Convolutional…”
Get full text

Care2Vec: a hybrid autoencoder-based approach for the classification of self-care problems in physically disabled children by Putatunda, Sayan

“…Accurate classification of self-care problems in children who suffer from physical and motor affliction is an important problem in the healthcare industry…”
Get full text

Fault detection and severity classification in HVAC fan coil units using a hybrid autoencoder-based method by Mahmoudan, A, Nojedehi, P, Gunay, B

“… This study presents a hybrid fault detection framework for fan coil units (FCUs) that leverages simulated fault-labelled data for evaluation and unsupervised learning for anomaly detection…”
Get full text

Passive ship detection and classification using hybrid cepstrums and deep compound autoencoders by Kamalipour, Maryam, Agahi, Hamed, Khishe, Mohammad, Mahmoodzadeh, Azar

“… In this approach, the compound autoencoders automatically extract the features without information loss and human intervention, and hybrid cepstral lifters reduce the multi-path distortion and time…”
Get full text

Classification of lemon quality using hybrid model based on Stacked AutoEncoder and convolutional neural network by Yılmaz, Esra Kavalcı, Adem, Kemal, Kılıçarslan, Serhat, Aydın, Hatice Aktaş

“… When the literature is examined, it is seen that artificial intelligence is used for classification processes of many agricultural products…”
Get full text

Hybrid twin attention based convolutional stacked sparse autoencoder for classification of defected weld images by Srikanth, T., Mani, M. Radhika

“… The most crucial steps that are carried out in the proposed welding error detection framework are pre-processing feature extraction and classification…”
Get full text

A hybrid deep learning-based fruit classification using attention model and convolution autoencoder by Xue, Gang, Liu, Shifeng, Ma, Yicao

“… chain, factories, supermarkets, and other fields. In this paper, we develop a hybrid deep learning-based fruit image classification framework, named attention-based densely connected convolutional networks with convolution autoencoder (CAE-ADN…”
Get full text

Advanced EEG signal processing with deep autoencoders and hybrid Mamba classifier for accurate classification of chronic neuropathic pain etiologies by Şentürk, Ümit

“…This study introduces a groundbreaking framework that leverages deep autoencoders and a novel hybrid Mamba classifier to enhance the objective classification of chronic neuropathic pain (CNP…”
Get full text

Hybrid stacked sparse autoencoder for robust feature extraction and classification in sparse data across multiple domains by Abdussamad, Abdulkadir, Said Jadid, Daud, Hanita, Sokkalingam, Rajalingam, Khan, Iliyas Karim

“… increasing the computational complexity. This paper presents a Hybrid-Stacked Sparse Autoencoder (HSSAE…”
Get full text

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

“…: This study presents an autoencoder-augmented stacked ensemble learning (SEL) framework integrating deep feature extraction (DFE…”
Get full text

An automated hybrid attention based deep convolutional capsule with weighted autoencoder approach for skin cancer classification by Desale, R.P., Patil, P.S.

“… It is essential to identify the disease at the initial stage and eliminate it from spreading. Hence, this research introduces an automated hybrid deep learning (DL…”
Get full text