Ultrasonic Guided Waves-Based Monitoring of Rail Head: Laboratory and Field Tests by Piervincenzo Rizzo, Marcello Cammarata, Ivan Bartoli, Francesco Lanza di Scalea, Salvatore Salamone, Stefano Coccia, Robert Phillips

“…These data are combined into a damage index vector fed to an unsupervised learning algorithm based on outlier analysis that determines the anomalous conditions of the rail. …”
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Improving of the Generation Accuracy Forecasting of Photovoltaic Plants Based on <i>k</i>-Means and <i>k</i>-Nearest Neighbors Algorithms by P. V. Matrenin, A. I. Khalyasmaa, V. V. Gamaley, S. A. Eroshenko, N. A. Papkova, D. A. Sekatski, Y. V. Potachits

“…In this case, unsupervised learning is first performed using the k-means method to form clusters. …”
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Dimensionality cutback and deep learning algorithms efficacy as to the breast cancer diagnostic dataset by Gennady Chuiko, Denys Honcharov

“…The Deep Learning algorithms in WEKA deliver excellent performance for both supervised and unsupervised learning, regardless of whether dealing with full or reduced datasets.…”
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Multimodal machine learning for analysing multifactorial causes of disease—The case of childhood overweight and obesity in Mexico by Rosario Silva Sepulveda, Magnus Boman, Magnus Boman

“…The top five most important features for classifying child or adolescent health were measures of an adult in the household, selected at random: BMI, obesity diagnosis, being single, seeking care at private healthcare, and having paid TV in the home. Unsupervised learning approaches varied in the optimal number of clusters but agreed on the importance of home environment features when analysing inter-cluster patterns. …”
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Numerical Weather Data-Driven Sensor Data Generation for PV Digital Twins: A Hybrid Model Approach by Jooseung Lee, Jimyung Kang, Sangwoo Son, Hui-Myoung Oh

“…The proposed model utilizes a hybrid data-driven model structure combining supervised learning-based long short-term memory (LSTM) and unsupervised learning-based generative adversarial network (GAN) to enhance both average and variance accuracy. …”
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The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review by Ricardo Smits Serena, Florian Hinterwimmer, Rainer Burgkart, Rudiger von Eisenhart-Rothe, Daniel Rueckert

“…Impressively, 93% of the studies used supervised learning, revealing an underuse of unsupervised learning, and indicating an important area for future exploration on discovering hidden patterns and insights without predefined labels or outcomes. …”
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Computational Simulation of Virtual Patients Reduces Dataset Bias and Improves Machine Learning-Based Detection of ARDS from Noisy Heterogeneous ICU Datasets by Konstantin Sharafutdinov, Sebastian Johannes Fritsch, Mina Iravani, Pejman Farhadi Ghalati, Sina Saffaran, Declan G. Bates, Jonathan G. Hardman, Richard Polzin, Hannah Mayer, Gernot Marx, Johannes Bickenbach, Andreas Schuppert

“…We compare the results of an unsupervised learning method (clustering) in two cases: where the learning is based on original patient data and on data derived in the matching procedure of the VP model to real patient data. …”
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The interaction network and potential clinical effectiveness of dimensional psychopathology phenotyping based on EMR: a Bayesian network approach by Jianqing Qiu, Ting Zhu, Ke Qin, Wei Zhang

“…Therefore, we employed unsupervised learning to translate five domains of eRDoC scores derived from electronic medical records (EMR) of patients diagnosed with Major Depressive Disorder (MDD), Schizophrenia (SCZ), and Bipolar Disorder (BD) at West China Hospital between 2008 and 2021. …”
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Enhancing Semi-Supervised Learning With Concept Drift Detection and Self-Training: A Study on Classifier Diversity and Performance by Jose L. M. Perez, Roberto S. M. Barros, Silas G. T. C. Santos

“…Concept drift occurs in supervised, semi-supervised and unsupervised learning environments, and is addressed through different approaches such as statistics, machine learning, among others. …”
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Unsupervised machine learning model for phenogroup-based stratification in acute type A aortic dissection to identify postoperative acute gastrointestinal injury by Yuhu Ma, Xiaofang Yang, Chenxiang Weng, Xiaoqing Wang, Baoping Zhang, Ying Liu, Rui Wang, Zhenxing Bao, Peining Yang, Hong Zhang, Yatao Liu

“…The areas under the curve (AUCs) for diagnosing postoperative AGI of phenogroup A, B, and C were 0.943 (0.854–0.992), 0.990 (0.966–1.000), and 0.964 (0.899–0.997) using the RF model, respectively.ConclusionPhenogroup stratification based on unsupervised learning can accurately identify high-risk populations for postoperative AGI in ATAAD, providing a new approach for implementing individualized preventive and therapeutic measures in clinical practice.…”
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Data-driven exploration of weak coordination microenvironment in solid-state electrolyte for safe and energy-dense batteries by Zhoujie Lao, Kehao Tao, Xiao Xiao, Haotian Qu, Xinru Wu, Zhiyuan Han, Runhua Gao, Jian Wang, Xian Wu, An Chen, Lei Shi, Chengshuai Chang, Yanze Song, Xiangyu Wang, Jinjin Li, Yanfei Zhu, Guangmin Zhou

“…Assisting with unsupervised learning, we use Climbing Image-Nudged Elastic Band simulations to screen lithium-ion conductors and screen out five potential candidates that elucidate the impact of lithium coordination environment on conduction behavior. …”
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A 3D Clinical Face Phenotype Space of Genetic Syndromes Using a Triplet-Based Singular Geometric Autoencoder by Soha S. Mahdi, Eduarda Caldeira, Harold Matthews, Michiel Vanneste, Nele Nauwelaers, Meng Yuan, Giorgos Bouritsas, Gareth S. Baynam, Peter Hammond, Richard Spritz, Ophir D. Klein, Michael Bronstein, Benedikt Hallgrimsson, Hilde Peeters, Peter Claes

“…In this paper, a triplet loss-based autoencoder developed by geometric deep learning (GDL) is trained using multi-task learning, which combines supervised and unsupervised learning approaches. Experiments are designed to illustrate the following properties of CFPSs that can aid clinicians in narrowing down their search space: a CFPS can 1) classify syndromes accurately, 2) generalize to novel syndromes, and 3) preserve the relatedness of genetic diseases, meaning that clusters of phenotypically similar disorders reflect functional relationships between genes. …”
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The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance by Hazrat Bilal, Muhammad Nadeem Khan, Sabir Khan, Muhammad Shafiq, Wenjie Fang, Rahat Ullah Khan, Mujeeb Ur Rahman, Xiaohui Li, Qiao-Li Lv, Bin Xu

“…Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. …”
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Selected Abstracts of the 20th International Workshop on Neonatology and Pediatrics; Cagliari (Italy); October 23-26, 2024 by --- Various Authors

“…Masnata (Cagliari, Italy) ABS 24. AN UNSUPERVISED LEARNING TOOL FOR STEM CELL BURDEN ASSESSMENT IN THE PRETERM KIDNEY • M. …”
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Academic Activities Transaction Extraction Based on Deep Belief Network by Xiangqian Wang, Fang Huang, Wencong Wan, Chengyuan Zhang

“…However, because Deep Belief Network (DBN) model has the ability to automatically unsupervise learning of the advanced features from shallow text features, the model is employed to extract the academic activities transaction. …”
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