A systematic review of multimodal fake news detection on social media using deep learning models by Maged Nasser, Noreen Izza Arshad, Abdulalem Ali, Hitham Alhussian, Faisal Saeed, Aminu Da'u, Ibtehal Nafea

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Bearing fault diagnosis based on a multiple-constraint modal-invariant graph convolutional fusion network by Zhongmei Wang, Pengxuan Nie, Jianhua Liu, Jing He, Haibo Wu, Pengfei Guo

“…Firstly, a Convolutional Autoencoder (CAE) and Squeeze-and-Excitation Block (SE block) are used to extract features of raw current and vibration signals. …”
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A Convolution Auto-Encoders Network for Aero-Engine Hot Jet FT-IR Spectrum Feature Extraction and Classification by Shuhan Du, Wei Han, Zhenping Kang, Yurong Liao, Zhaoming Li

“…Aiming at classification and recognition of aero-engines, two telemetry Fourier transform infrared (FT-IR) spectrometers are utilized to measure the infrared spectrum of the areo-engine hot jet, meanwhile a spectrum dataset of six types of areo-engines is established. In this paper, a convolutional autoencoder (CAE) is designed for spectral feature extraction and classification, which is composed of coding network, decoding network, and classification network. …”
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MATLAB Application for User-Friendly Design of Fully Convolutional Data Description Models for Defect Detection of Industrial Products and Its Concurrent Visualization by Fusaomi Nagata, Shingo Sakata, Keigo Watanabe, Maki K. Habib, Ahmad Shahrizan Abdul Ghani

“…Models supported by the application include the following original designs: convolutional neural network (CNN), transfer learning-based CNN, NN-based support vector machine (SVM), convolutional autoencoder (CAE), variational autoencoder (VAE), fully convolution network (FCN) (such as U-Net), and YOLO. …”
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Cloud based real-time multivariate multi-step prediction of systolic blood pressure and heart rate using temporal convolutional network and Apache Spark by Hager Saleh, Nora El-Rashidy, Sherif Mostafa, Abdulaziz AlMohimeed, Shaker El-Sappagh, Zainab H. Ali

“…Multi-task comprises forecasting HR and SBP in diverse multi-step heads as puerperal employing TCN, sequence-to-sequence (seq2seq), and Autoencoder models using LSTM and GRU. Extensive results are accomplished by the Medical Information Mart for Intensive Care III (MIMIC III) to assess the performance of the proposed multi-task DL model. …”
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CNN-based remote dental diagnosis model for caries detection with grad-CAM by Donghyeok Kim, Jangkyum Kim, Seong Gon Choi

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Non-contact ECG reconstruction algorithm based on millimeter wave radar by LUO Jingxue, ZHANG Yuanhui, DAI Xiao, FU Duo, LIU Kang

“…The signal processing flow of millimeter-wave radar was introduced in detail, the fine-grained mapping relationship between radar signals and ECG signals was explored, and the nonlinear transformation from radar signals to electrocardiograms was achieved through the introduction of the CAE-BiLSTM deep learning network, which was a hybrid of a convolutional autoencoder (CAE) and bi-directional long short-term memory (BiLSTM), incorporating the convolutional block attention module (CBAM).The results show that the median morphological accuracy of the proposed method is 0.92, and the feature peak prediction error is less than 50 ms. …”
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Deep Learning-Based Anomaly Detection in Occupational Accident Data Using Fractional Dimensions by Ömer Akgüller, Larissa M. Batrancea, Mehmet Ali Balcı, Gökhan Tuna, Anca Nichita

“…This study examines the effectiveness of Convolutional Autoencoder (CAE) and Variational Autoencoder (VAE) models in detecting anomalies within occupational accident data from the Mining of Coal and Lignite (NACE05), Manufacture of Other Transport Equipment (NACE30), and Manufacture of Basic Metals (NACE24) sectors. …”
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Deep Learning Model for Feature Extraction and Anomaly Recognition in High-Dimensional Energy Metering Data by Huakun Que, Zetao Jiang, Zhifeng Zhou, Yongsheng He, Xin Liu

“…Methods: High-dimensional metering data from a city energy provider is processed using a Convolutional Autoencoder (CAE) to extract deep features and reduce dimensionality. …”
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Unsupervised anomaly detection of permanent-magnet offshore wind generators through electrical and electromagnetic measurements by A. Dibaj, M. Valavi, A. R. Nejad

“…The study utilizes a high-speed PMSG model on the National Renewable Energy Laboratory (NREL) 5 <span class="inline-formula">MW</span> reference offshore wind turbine at the rated wind speed to simulate healthy and faulty conditions. An unsupervised convolutional autoencoder (CAE) model, trained on simulated signals from the generator in its healthy state, serves for anomaly detection. …”
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Privacy-Preserving Detection of Tampered Radio-Frequency Transmissions Utilizing Federated Learning in LoRa Networks by Nurettin Selcuk Senol, Mohamed Baza, Amar Rasheed, Maazen Alsabaan

“…We evaluated the performance of multiple FL-enabled anomaly-detection algorithms, including Convolutional Autoencoder Federated Learning (CAE-FL), Isolation Forest Federated Learning (IF-FL), One-Class Support Vector Machine Federated Learning (OCSVM-FL), Local Outlier Factor Federated Learning (LOF-FL), and K-Means Federated Learning (K-Means-FL). …”
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Synergizing vision transformer with ensemble of deep learning model for accurate kidney stone detection using CT imaging by Arwa Alzughaibi, Adwan A. Alanazi, Mohammed Alshahrani, Ines Hilali Jaghdam, Abaker A. Hassaballa

“…Furthermore, the majority voting ensemble of three DL approaches, such as the graph convolutional network (GCN), temporal convolutional network (TCN), and three-dimensional convolutional autoencoder (3D-CAE) approaches, are employed to increase the precision and reliability of the kidney stone recognition. …”
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Internet of things driven object detection framework for consumer product monitoring using deep transfer learning and hippopotamus optimization by Amnah Alshahrani, Mukhtar Ghaleb, Hany Mahgoub, Achraf Ben Miled, Nojood O. Aljehane, Mohammed Yahya Alzahrani, Hasan Beyari, Sultan Alanazi

“…The SqueezeNet model extracts and isolates relevant features from raw data. Moreover, the convolutional autoencoder (CAE) model is implemented for classification. …”
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Secure Biometric Identification Using Orca Predators Algorithm With Deep Learning: Retinal Iris Image Analysis by Louai A. Maghrabi, Mohammed Altwijri, Sami Saeed Binyamin, Fouad Shoie Alallah, Diaa Hamed, Mahmoud Ragab

“…Furthermore, the biometric identification process can be performed by the use of a convolutional autoencoder (CAE). To validate the enhanced biometric detection results of the SBRIC-OPADL technique is tested using the biometric iris dataset. …”
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Deep learning for video-based assessment of endotracheal intubation skills by Jean-Paul Ainam, Erim Yanik, Rahul Rahul, Taylor Kunkes, Lora Cavuoto, Brian Clemency, Kaori Tanaka, Matthew Hackett, Jack Norfleet, Suvranu De

“…The system employs advanced video processing techniques, including a 2D convolutional autoencoder (AE) based on a self-supervision model, capable of recognizing complex patterns in videos. …”
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Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities by Munya A. Arasi, Hussah Nasser AlEisa, Amani A. Alneil, Radwa Marzouk

“…For the detection of indoor activities, the proposed MOEM-SMIADP model utilizes an ensemble of three classifiers, namely the graph convolutional network model, long short-term memory sequence-to-sequence (LSTM-seq2seq) method, and convolutional autoencoder. …”
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Leveraging machine learning techniques for image classification and revealing social media insights into human engagement with urban wild spaces by Haider Khalid, Marcus J. Collier

“…The study follows a two-step methodology: first, scraping image data from Instagram, Facebook, and Flickr using hashtag-based techniques focused on urban wild spaces; second, developing an experimental pipeline using Convolutional Neural Networks (CNN), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Convolutional Autoencoders (CAE) to classify and evaluate the scrapped social media data. …”
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Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web by Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong

“…The upgraded UML method primarily consists of the following three aspects. (1) We employ a convolutional autoencoder to denoise galaxy images and adaptive polar coordinate transformation to enhance the model’s rotational invariance. (2) A pretrained dual-encoder convolutional neural network based on ConvNeXt and a vision transformer is used to encode the image data, while contrastive learning is then applied to reduce the dimension of the features. (3) We adopt a bagging-based clustering model to cluster galaxies with similar features into distinct groups. …”
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Advancements in Herpes Zoster Diagnosis, Treatment, and Management: Systematic Review of Artificial Intelligence Applications by Dasheng Wu, Na Liu, Rui Ma, Peilong Wu

“…AI applications were analyzed across three domains: (1) diagnosis, where mobile deep neural networks, convolutional neural network ensemble models, and mixed-scale attention-based models have improved diagnostic accuracy and efficiency; (2) treatment, where machine learning models, such as deep autoencoders combined with functional magnetic resonance imaging, electroencephalography, and clinical data, have enhanced treatment outcome predictions; and (3) management, where AI has facilitated case identification, epidemiological research, health care burden assessment, and risk factor exploration for postherpetic neuralgia and other complications. …”
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Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach by Ajan Subramanian, Rui Cao, Emad Kasaeyan Naeini, Seyed Amir Hossein Aqajari, Thomas D Hughes, Michael-David Calderon, Kai Zheng, Nikil Dutt, Pasi Liljeberg, Sanna Salanterä, Ariana M Nelson, Amir M Rahmani

“…Data preprocessing involved noise filtering, feature extraction, and combining handcrafted and automatic features through convolutional and long-short-term memory autoencoders. …”
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