Convolutional autoencoder network lithology recognition based on scratch tests by Suling Wang, Zhihui Ren, Kangxing Dong, Yanchun Li, Jinbo Li, Pengyun Wen, Ruyi Qu, Tingting Li, Zhennan Wen

“…The results demonstrate that when the identification scale is set at 20 × 9, the test dataset achieves an accuracy of 89.58%, with recall rates exceeding 84% across all lithology recognitions, outperforming other identification scales. The convolutional autoencoder network (CAE) exhibits superior accuracy and recall rates in lithology identification compared to other neural networks, enabling a more precise representation of the actual lithological characteristics. …”
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Enhanced multiple sclerosis diagnosis by MRI image retrieval using convolutional autoencoders by R.M. Haggag, Eman M. Ali, M.E. Khalifa, Mohamed Taha

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Computer-Aided Diagnosis of Acute Lymphoblastic Leukemiaby Using a Novel CAE-CNN Framework by Mohammed Mansoor Alhammadi

“…In this paper, ALL-diagnosing methods based on the convolutional autoencoder (CAE) was proposed to reduce the amount of data, and then convolutional neural network (CNN) was applied to identify ALL. …”
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River Channel Microgeomorphic Feature Extraction and Potential Sandstorm Source Identification Method Based on a Convolutional Autoencoder Model by Kecong Wu, Lirong Chen, Yalige Bai, Xinhang Wang, Danzeng Pingcuo, Zhongpeng Han, Chengshan Wang

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A convolutional autoencoder framework for ECG signal analysis by Ugo Lomoio, Patrizia Vizza, Raffaele Giancotti, Salvatore Petrolo, Sergio Flesca, Fabiola Boccuto, Pietro Hiram Guzzi, Pierangelo Veltri, Giuseppe Tradigo

“…Analysis of time varying signals may be done by using autoencoders (AEs) deep neural networks. AE specialized for signal data, named Convolutional Autoencoder (CAE), showed the best performances in the analysis of ECG signals.This paper presents a CAE-based framework for ECG signal analysis and anomaly identification. …”
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Multi-perspective Learning Based on Transformer for Stock Price Trend by Xiliang Li, Shuoru Chen, Xiaoyan Qiao, Mingli Zhang, Caiming Zhang, Feng Zhao

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Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder by Zahra Rastin, Gholamreza Ghodrati Amiri, Ehsan Darvishan

“…The CAE is chosen to take advantage of high feature extraction capability of convolution layers and at the same time use the advantages of an autoencoder as an unsupervised algorithm that does not need data from damaged states in the training phase. …”
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A study on hybrid-architecture deep learning model for predicting pressure distribution in 2D airfoils by Jaehyun Yoon, Jaehyeok Doh

Subjects: “…Convolutional autoencoder (CAE)…”
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Integrasi Convolutional Autoencoder dengan Support Vector Machine untuk Klasifikasi Varietas Almond by Rizal Fadlullah, Sri Winarno, Muhammad Naufal

“…Penelitian ini bertujuan mengoptimalkan klasifikasi varietas almond dengan mengintegrasikan Convolutional Autoencoder (CAE) sebagai metode ekstraksi fitur dan Support Vector Machine (SVM) untuk klasifikasi. …”
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Abnormal sound detection method for coal mine belt conveyors based on convolutional autoencoder by SHEN Long, SHAN Haoran, PEI Wenliang, YANG Guixiang, WANG Yongli

“…To address the issue of insufficient abnormal sound samples for coal mine belt conveyors, which makes it difficult for training models to recognize anomalies, an abnormal sound detection method for coal mine belt conveyors based on Convolutional Autoencoder (CAE) is proposed. First, sound signals from the normal operation of the belt conveyor's idlers, reducer, and motor were collected. …”
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Convolutional Autoencoder With Sequential and Channel Attention for Robust ECG Signal Denoising With Edge Device Implementation by Alif Wicaksana Ramadhan, Syifa Kushirayati, Salsabila Aurellia, Mgs M. Luthfi Ramadhan, Muhammad Hannan Hunafa, Muhammad Febrian Rachmadi, Aprinaldi Jasa Mantau, Siti Nurmaini, Satria Mandala, Wisnu Jatmiko

“…Denoising ECG signals is particularly challenging because noise usually overlaps with the frequency range of the signal of interest. We proposed a convolutional autoencoder with sequential and channel attention (CAE-SCA) to address this issue. …”
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A Novel Multi-Task and Ensembled Optimized Parallel Convolutional Autoencoder and Transformer for Speech Emotion Recognition by Zahra Sharifzadeh Jafari, Sanaz Seyedin

“…In this paper, we present a novel model for speech emotion recognition (SER) based on new multi-task parallel convolutional autoencoder (PCAE) and transformer networks. …”
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Domain-Adaptive Direction of Arrival (DOA) Estimation in Complex Indoor Environments Based on Convolutional Autoencoder and Transfer Learning by Lingyu Shen, Jianfeng Li, Jingjing Pan, Junpeng Shi, Rui Xu, Hao Wang, Weiming Deng

“…The approach begins with deep feature extraction using a Convolutional Autoencoder (CAE) and employs a Domain-Adversarial Neural Network (DANN) for domain adaptation. …”
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Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system by Philipp Teutsch, Philipp Pfeffer, Mohammad Sharifi Ghazijahani, Christian Cierpka, Jörg Schumacher, Patrick Mäder

“…Within the context of reduced-order models, convolutional autoencoders (CAEs) pose a universally applicable alternative to conventional approaches. …”
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Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models by Renato Melo, Rafaelle Finotti, António Guedes, Vítor Gonçalves, Andreia Meixedo, Diogo Ribeiro, Flávio Barbosa, Alexandre Cury

“…This study presents a comparative analysis of three AutoEncoder (AE) models—Variational AutoEncoder (VAE), Sparse AutoEncoder (SAE), and Convolutional AutoEncoder (CAE)—to detect and quantify structural anomalies in railway vehicle wheels, such as polygonization. …”
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A CAE model-based secure deduplication method by Chunbo Wang, Guoying Zhang, Hui Qi, Bin Chen

“…Building on this, this paper further introduces a secure deduplication method based on a Convolutional Autoencoder (CAE) model, which utilizes convolution and pooling operations to reduce the number of parameters in the Convolutional Autoencoder model, thereby decreasing computational and storage overhead. …”
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Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder by Shubham Chaudhry, Azzedine Abdedou, Azzeddine Soulaïmani

“…Abstract This study proposes and compares two data-driven, non-intrusive reduced-order models (ROMs) for additive manufacturing (AM) processes: a combined proper orthogonal decomposition-artificial neural network (POD-ANN) and a convolutional autoencoder-multilayer perceptron (CAE-MLP). …”
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Machine learning approach to reconstruct density matrices from quantum marginals by Daniel Uzcategui-Contreras, Antonio Guerra, Sebastian Niklitschek, Aldo Delgado

“…Our method integrates a quantum marginal imposition technique with convolutional denoising autoencoders. The loss function is carefully designed to enforce essential physical constraints, including Hermiticity, positivity, and normalization. …”
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Vibration-Based Anomaly Detection in Industrial Machines: A Comparison of Autoencoders and Latent Spaces by Luca Radicioni, Francesco Morgan Bono, Simone Cinquemani

“…This study explores the application of unsupervised learning methods, particularly Convolutional Autoencoders (CAEs) and variational Autoencoders (VAEs), for anomaly detection (AD) in vibration signals. …”
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Multi-Modal Deep Embedded Clustering (MM-DEC): A Novel Framework for Mineral Detection Using Hyperspectral Imagery by Priyanka Nair, Devesh Kumar Srivastava, Roheet Bhatnagar

“…To this end, we propose Multi-Modal Deep Embedded Clustering (MM-DEC) approach, an innovative unsupervised learning framework that integrates Convolutional Autoencoders(CAEs), Variational Autoencoders (VAEs), and Gray Level Co-occurrence Matrix (GLCM) based texture extraction that is able to exploit the spatial, spectral, and texture features of mineral in consideration We demonstrate the MM-DEC potential to identify hematite prospects in the mineralized Kiriburu area of Jharkhand, India using EO-1 Hyperion hyperspectral data. …”
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