Clustering Input Signals Based Identification Algorithms for Two-Input Single-Output Models with Autoregressive Moving Average Noises by Khalid Abd El Mageed Hag ElAmin

“…This study focused on the identification problems of two-input single-output system with moving average noises based on unsupervised learning methods applied to the input signals. …”
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Health Assessment of Rolling Bearing based on Self-organizing Map and Restricted Boltzmann Machine by Liu Hao, Xiong Xin, Wang Xiaojing, Guo Jiayu, Shen Jiexi

“…With the consideration of degradation induced changes for response features,unsupervised learning scheme based on SOM,with the help of multi-domain feature sets consist of features in time domain,frequency domain and time-frequency domain,the optimal feature domain is constructed by sorting the diverse features adopting the sequential forward selection（ SFS） regulation,the mapping relationship between the selected feature vectors and bearing health status is obtained. …”
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An Unsupervised Deep Learning Framework for Retrospective Gating of Catheter-Based Cardiac Imaging by Zheng Sun, Yue Yao, Ru Wang

“…In this paper, we report on an end-to-end unsupervised learning framework for retrospective image-based gating (IBG) of catheter-based intracoronary images, named IBG Network. …”
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Application of improved self-training model in the identification of users with poor service quality by Li YU, Zhe LI, Fei GAO, Xiangyang YUAN, Yong YANG

“…Poor quality user identification is an important method to reduce the complaint rate and increase satisfaction.It is difficult to effectively label a large amount of structured and unstructured data related to business perception in current telecommunications network systems, poor quality user labels are not complete, and the existing supervised learning model training samples are unbalanced, resulting in a low quality recognition rate.An improved self-training semi-supervised learning model was adopted, a small number of low-satisfaction and complaint users as poor quality user labels was used to label network data, and label migration was used to train a large amount of unlabeled data to identify poor quality users.Experiments show that compared to fully supervised learning with high recognition model accuracy but high training cost and unsupervised learning with low recognition model accuracy, semi-supervised learning can make full use of unlabeled sample data for effective training, ensuring lower training costs and the recognition accuracy of poor-quality users is significantly improved.…”
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Enhancing Rehabilitation Assessment with Artificial Intelligence: A Comprehensive Investigation of Posture Quality Prediction Using Machine Learning by Zhang Wenxi

“…Various supervised and unsupervised learning methods are analyzed and their effectiveness in classifying rehabilitation movements and providing real-time feedback to improve rehabilitation outcomes is demonstrated. …”
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Freeway Traffic Speed Prediction under the Intelligent Driving Environment: A Deep Learning Approach by Chengying Hua, Wei (David) Fan

“…The results indicate that both supervised learning and unsupervised learning are superior to the simulation-based model on the freeway, and the two deep learning networks are almost identical to one another. …”
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Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network by Mingzhu Li, Zhiqian Wang, Jun Luo, Yusheng Liu, Sheng Cai

“…According to the characteristics of VPVS, a novel thresholding function is constructed, and then its optimized threshold is selected through unsupervised learning of TNN. The original VPVS mixed in trend and random noise is constructed as VPVS model. …”
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Clustering of Parameter Sensitivities: Examples from a Helicopter Airframe Model Updating Exercise by H. Shahverdi, C. Mares, W. Wang, J.E. Mottershead

“…A new methodology is developed using an unsupervised learning technique based on similarity clustering of the columns of the sensitivity matrix. …”
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Monitoring Changes in Clustering Solutions: A Review of Models and Applications by Muhammad Atif, Muhammad Shafiq, Muhammad Farooq, Gohar Ayub, Friedrich Leisch, Muhammad Ilyas

“…The clustering technique is an unsupervised learning problem that involves the identification of natural subgroups in a large dataset. …”
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Color Image Segmentation Using Fuzzy C-Regression Model by Min Chen, Simone A. Ludwig

“…Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. …”
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Unsupervised machine and deep learning methods for structural damage detection: A comparative study by Zilong Wang, Young‐Jin Cha

“…Abstract While many structural damage detection methods have been developed in recent decades, few data‐driven methods in unsupervised learning mode have been developed to solve the practical difficulties in data acquisition for civil infrastructures in different scenarios. …”
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Spontaneous thought separates into clusters of negative, positive, and flexible thinking by Marta Migó, Jessica A. Cooper, Philip A. Kragel, Michael T. Treadway

“…After the data was embedded into a 3-dimensional semantic space and fit by our dynamic model, unsupervised learning consistently grouped data into four clusters across all independent samples. …”
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Effective First-Break Picking of Seismic Data Using Geometric Learning Methods by Zhongyang Wen, Jinwen Ma

“…Specifically, in the case of unsupervised learning, we design an effective curve evolving algorithm according to the active contour(AC) image segmentation model, in which the length of the target curve and the fitting region energy are minimized together. …”
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Harnessing artificial intelligence for predictive modelling in oral oncology: Opportunities, challenges, and clinical Perspectives by Vishnu Priya Veeraraghavan, Shikhar Daniel, Arun Kumar Dasari, Kaladhar Reddy Aileni, Chaitra patil, Santosh R. Patil

“…Various AI algorithms, including supervised learning, unsupervised learning, and deep learning approaches, have been discussed in the context of oral cancer prediction. …”
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Role of Artificial Intelligence in Oral Cancer by Vidhya Rekha Umapathy, Prabhu Manickam Natarajan, Bhuminathan Swamikannu, Sabarinathan Jaganathan, Suba Rajinikanth, Vijayalakshmi Periyasamy

“…Machine learning paradigms, encompassing supervised and unsupervised learning, afford meticulous classification and pattern identification from multifarious clinical and histopathological datasets. …”
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An efficient interpretable framework for unsupervised low, very low and extreme birth weight detection. by Ali Nawaz, Amir Ahmad, Shehroz S Khan, Mohammad Mehedy Masud, Nadirah Ghenimi, Luai A Ahmed

“…While traditional approaches to managing class imbalance require labeled data, our study explores the use of unsupervised learning to detect anomalies indicative of low birth weight scenarios. …”
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Low-Resource Active Learning of Morphological Segmentation by Stig-Arne Grönroos, Katri Hiovain, Peter Smit, Ilona Rauhala, Kristiina Jokinen, Mikko Kurimo, Sami Virpioja

“…With 300 words annotated with our active learning setup, we see a relative improvement in morph boundary F1-score of 19% compared to unsupervised learning and 7.8% compared to random selection. …”
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Interpretable and integrative deep learning for discovering brain-behaviour associations by Corentin Ambroise, Antoine Grigis, Josselin Houenou, Vincent Frouin

“…These developments have led to more comprehensive approaches to studying psychiatric conditions that incorporate diverse data sources such as imaging, genetics, and symptom reports. Multi-view unsupervised learning frameworks, particularly deep learning models, present promising solutions for integrating and analysing complex datasets. …”
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Ensemble graph auto-encoders for clustering and link prediction by Chengxin Xie, Jingui Huang, Yongjiang Shi, Hui Pang, Liting Gao, Xiumei Wen

“…Graph auto-encoders are a crucial research area within graph neural networks, commonly employed for generating graph embeddings while minimizing errors in unsupervised learning. Traditional graph auto-encoders focus on reconstructing minimal graph data loss to encode neighborhood information for each node, yielding node embedding representations. …”
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A Review of Foreign Object Debris Detection on Airport Runways: Sensors and Algorithms by Jingfeng Shan, Lapo Miccinesi, Alessandra Beni, Lorenzo Pagnini, Andrea Cioncolini, Massimiliano Pieraccini

“…AI approaches, employing supervised and unsupervised learning, demonstrate potential for automating detection and improving precision, although challenges such as limited datasets and high computational demands persist. …”
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