Text Mining and Unsupervised Deep Learning for Intrusion Detection in Smart-Grid Communication Networks by Joseph Azar, Mohammed Al Saleh, Raphaël Couturier, Hassan Noura

“…To address the limitations of existing approaches that rely on labeled samples, the proposed model learns the properties and patterns of a large number of normal samples in an unsupervised manner. …”
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Unsupervised Particle Tracking with Neuromorphic Computing by Emanuele Coradin, Fabio Cufino, Muhammad Awais, Tommaso Dorigo, Enrico Lupi, Eleonora Porcu, Jinu Raj, Fredrik Sandin, Mia Tosi

“…We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. …”
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Exploration of Unsupervised Deep Learning-Based Gear Fault Detection for Wind Turbine Gearboxes by Bartłomiej Kiczek, Michał Batsch

“…This article presents a method for detecting surface damage on gear teeth using unsupervised machine learning. …”
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Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines by Enamundram Naga Karthik, Farida Cheriet, Catherine Laporte

“…Uncertainty estimations through approximate Bayesian inference provide interesting insights to deep neural networks' behavior. In unsupervised learning tasks, where expert labels are unavailable, it becomes ever more important to critique the model through uncertainties. …”
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SFDA-MEF: An Unsupervised Spacecraft Feature Deformable Alignment Network for Multi-Exposure Image Fusion by Qianwen Xiong, Xiaoyuan Ren, Huanyu Yin, Libing Jiang, Canyu Wang, Zhuang Wang

“…Therefore, we propose an unsupervised learning framework for the multi-exposure fusion of optical spacecraft image sequences. …”
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Feature extraction and classification of digital rock images via pre-trained convolutional neural network and unsupervised machine learning by Masashige Shiga, Masao Sorai, Tetsuya Morishita, Masaatsu Aichi, Naoki Nishiyama, Takashi Fujii

“…To address this challenge, this study presents a novel approach for the classification and visualization of rock microstructure from micro-computed tomography images, leveraging pre-trained convolutional neural network (CNN) models (AlexNet, GoogLeNet, Inception v3 Net, ResNet, and DenseNet) combined with unsupervised machine learning (USML) techniques principal component analysis, multidimensional scaling, isometric mapping, t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation projection (UMAP)). …”
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Low‐Frequency Reconstruction for Full Waveform Inversion by Unsupervised Learning by Ningcheng Cui, Tao Lei, Wei Zhang

“…In this study, we employed an unsupervised learning method, namely cycle‐consistent adversarial networks (CycleGAN), to reconstruct large‐scale‐feature related low‐frequency information based on the high‐frequency input data. …”
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Unsupervised neural network-based image stitching method for bladder endoscopy. by Zixing Ye, Chenyu Shao, Kelei Zhu

“…The method comprises two modules: an unsupervised alignment network and an unsupervised fusion network. …”
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Noise2Variance: Dual networks with variance constraint for self‐supervised real‐world image denoising by Hanlin Tan, Yu Liu, Maojun Zhang

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Performance Analysis of a Wind Turbine Pitch Neurocontroller with Unsupervised Learning by J. Enrique Sierra-García, Matilde Santos

“…In this work, a neural controller for wind turbine pitch control is presented. The controller is based on a radial basis function (RBF) network with unsupervised learning algorithm. …”
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Unsupervised Multimodal UAV Image Registration via Style Transfer and Cascade Network by Xiaoye Bi, Rongkai Qie, Chengyang Tao, Zhaoxiang Zhang, Yuelei Xu

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Hierarchical partition of urban land-use units by unsupervised graph learning from high-resolution satellite images by Mengmeng Li, Xinyi Gai, Kangkai Lou, Alfred Stein

“…A significant challenge remains the accurate partition of fine-grained land-use units from these images. This paper presents a novel method for deriving these units based on unsupervised graph learning techniques using high-resolution satellite images and open street boundaries. …”
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AE-BoNet: A Deep Learning Method for Pediatric Bone Age Estimation using an Unsupervised Pre-Trained Model by Mojtaba Sirati-Amsheh, Elham Shabaninia, Ali Chaparian

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A Survey of Unsupervised Learning Algorithms for Zero-Day Attacks in Intrusion Detection Systems. by Sunkanmi Oluwadare, Zag ElSayed

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A Comparative Study of Unsupervised Deep Learning Methods for Anomaly Detection in Flight Data by Sameer Kumar Jasra, Gianluca Valentino, Alan Muscat, Robert Camilleri

“…This paper provides a comparative study of unsupervised Deep Learning (DL) methods for anomaly detection in Flight Data Monitoring (FDM). …”
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An explainable unsupervised learning approach for anomaly detection on corneal in vivo confocal microscopy images by Ningning Tang, Qi Chen, Yunyu Meng, Daizai Lei, Li Jiang, Yikun Qin, Xiaojia Huang, Fen Tang, Shanshan Huang, Qianqian Lan, Qi Chen, Lijie Huang, Rushi Lan, Xipeng Pan, Huadeng Wang, Fan Xu, Wenjing He

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Applications of Machine Learning in Food Safety and HACCP Monitoring of Animal-Source Foods by Panagiota-Kyriaki Revelou, Efstathia Tsakali, Anthimia Batrinou, Irini F. Strati

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Unsupervised Anomaly Detection for Volcanic Deformation in InSAR Imagery by Robert Popescu, Nantheera Anantrasirichai, Juliet Biggs

“…Manual inspection could miss these anomalies, and an automatic system modeled with supervised learning requires suitably labeled data sets. To tackle these issues, this paper explores the use of unsupervised deep learning on InSAR images for the purpose of identifying volcanic deformation as anomalies. …”
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Stepwise Corrected Attention Registration Network for Preoperative and Follow-Up Magnetic Resonance Imaging of Glioma Patients by Yuefei Feng, Yao Zheng, Dong Huang, Jie Wei, Tianci Liu, Yinyan Wang, Yang Liu

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Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach by Akella S. Narasimha Raju, K. Venkatesh, Ranjith Kumar Gatla, Eswara Prasad Konakalla, Marwa M. Eid, Nataliia Titova, Sherif S. M. Ghoneim, Ramy N. R. Ghaly

“…This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. …”
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