MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern by Q. He, Y. J. Zheng, C.L. Zhang, H. Y. Wang

“…In the prediction part, multiscale convolution and graph attention network are mainly used to capture information in temporal pattern with feature pattern. The threshold selection part uses the root mean square error between the predicted value and the actual value to perform extreme value analysis to obtain the threshold. …”
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Feature extraction and spatial imaging of synchrotron radiation X-ray diffraction patterns using unsupervised machine learning by Kentaro Kutsukake, Takefumi Kamioka, Kota Matsui, Ichiro Takeuchi, Takashi Segi, Takuo Sasaki, Seiji Fujikawa, Masamitu Takahasi

“…We analyzed a number of complicated X-ray diffraction patterns using feature patterns obtained through unsupervised machine learning. …”
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Enhancement of Chest X-Ray Images to Improve Screening Accuracy Rate Using Iterated Function System and Multilayer Fractional-Order Machine Learning Classifier by Chia-Hung Lin, Jian-Xing Wu, Chien-Ming Li, Pi-Yun Chen, Neng-Sheng Pai, Ying-Che Kuo

“…The IFS with nonlinear interpolation functions is then used to reconstruct the 2D feature patterns. These reconstructed patterns are self-affine in the same class and thus help distinguish normal subjects from those with lung diseases. …”
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Identifying AC Control Patterns From a Massive Longitudinal Log Dataset Using Deep Clustering by Seunghwan Bae, Donghee Kim, Seokhwan Seong, Jaeyeon Jang, Young-Kyoon Suh, Honghyun Kim, Dongwoo Han, Inuk Jung

“…Regardless of control activity, all patterns are highly correlated with the progressing weather conditions during the summer. The feature patterns of the actively controlled group significantly differed from those of the inactively controlled group. …”
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Combining Endpoint Detection and One-Dimensional CNN-Based Classifier for Non-Technical Loss Screening in Smart Grids by Ping-Tzan Huang, Feng-Chang Gu, Chia-Hung Lin, Chao-Lin Kuo, Neng-Sheng Pai, Yung-Chang Luo, Wen-Cheng Pu

“…Subsequently, the STFT is applied to analyze the frequency contains in the drastically changing time-domain data and then generates the visualization color feature patterns. With theses feature patterns, the 1D-CNN based classifier is used to identify the data into normal (Nor), suspected incidents (SI), fraud incidents (FI), and fault or power outage (OUT) events. …”
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Multiple Chaos Synchronization System for Power Quality Classification in a Power System by Cong-Hui Huang, Chia-Hung Lin

“…Multiple detectors are used to monitor the dynamic errors between the master system and the slave system and are used to construct the feature patterns from time-domain signals. The maximum likelihood method (MLM), as a classifier, performs a comparison of the patterns of the features in the database. …”
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Machine Learning Optimization and Challenges in Used Car Price Prediction by Zheng Yufan

“…To begin with, models like XGBoost and Random Forest excel at processing large-scale data and identifying complex feature patterns, thanks to their ability to use an ensemble of decision trees to reduce bias and variance. …”
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Star Recognition Based on Path Optimization in Star Sensor with Multiple Fields of View by Di Jiang, Ke Zhang, Olivier Debeir

“…By the proposed algorithm, starting point for path optimization has no influence on the extracted feature pattern. Thus the star recognition rate is improved due to the higher stability of the extracted pattern. …”
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Structure and usage do not explain each other: an analysis of German word-initial clusters by Wiese Richard, Orzechowska Paula

“…First, out of eighteen correlations between (raw and logarithmic) type and token frequencies, and preferred feature patterns, only one significant correlation was found. …”
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MPMFFT based DCA-DBT integrated probabilistic model for face expression classification by Kapil Juneja

“…At First, DCA (Dendric Cell algorithm) is ready to generate feature patterns to identify safe and danger qualified features. …”
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Diagnosis of depression based on facial multimodal data by Nani Jin, Renjia Ye, Peng Li

“…Through the multi-modal feature fusion, the model can effectively capture different feature patterns related to depression.ResultsWe conduct extensive experiments on the publicly available clinical dataset, the Extended Distress Analysis Interview Corpus (E-DAIC). …”
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Multi-Step Parking Demand Prediction Model Based on Multi-Graph Convolutional Transformer by Yixiong Zhou, Xiaofei Ye, Xingchen Yan, Tao Wang, Jun Chen

“…This paper proposes a deep learning model based on multi-graph convolutional Transformer, which captures geographic spatial features through a Multi-Graph Convolutional Network (MGCN) module and mines temporal feature patterns using a Transformer module to accurately predict future multi-step parking demand. …”
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A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification by Jiankang Ke, Faxing Lu, Yifei Liu, Bing Fu

“…The model aims to address the limitations of traditional methods in extracting feature patterns jointly represented by non-adjacent local regions in ship trajectories, optimized through the introduction of a self-attention mechanism. …”
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An Improved Fault Diagnosis Method and Its Application in Compound Fault Diagnosis for Paper Delivery Structure Coupling by Fu Liu, Haopeng Chen, Yan Wang

“…Furthermore, the dimensionality reduction results of each network layer are visualized using the T-stochastic neighbor embedding (T-SNE) method, which reveals clear feature patterns and confirms the model’s reliability and effectiveness.…”
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Sea Surface Current Retrieval From Sequential SAR and Ocean Color Images for Eddy Kinematics Analysis: A Case Study in the Northern Tyrrhenian Sea by Xue Yang, Jinsong Chong, Yawei Zhao

“…However, the absence of traceable feature patterns in sequential remote sensing images constrains the capabilities for retrieving SSCs. …”
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Optimized Identity Authentication via Channel State Information for Two-Factor User Verification in Information Systems by Chuangeng Tian, Fanjia Li, Xiaomeng Liu, Juanjuan Li

“…The SVM classifies the CSI feature patterns obtained from user-specific keystroke dynamics, which are processed by Hampel filtering, Butterworth low-pass filtering, wavelet transform threshold denoising, a dual-threshold moving window detection algorithm, a subcarrier selection method, and PCA, to achieve optimal performance. …”
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Unsupervised Binary Classifier-Based Object Detection Algorithm with Integrated Background Subtraction Suitable for Use with Aerial Imagery by Gabija Veličkaitė, Ignas Daugėla, Ivan Suzdalev

“…Unlike conventional supervised models, SARGAS is trained in a partially unsupervised manner, learning to recognize feature patterns without requiring labeled data. The algorithm achieved a recall of 93%, demonstrating strong detection capability even under challenging conditions. …”
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Hard forest edges act as conduits, not filters, for bats by Matina C. Kalcounis‐Rueppell, Kim M. Briones, Jessica A. Homyack, Radmila Petric, Matthew M. Marshall, Darren A. Miller

“…ABSTRACT High levels of bat activity have been reported at forest edges worldwide, but few studies have examined the ecological function of edges as a linear landscape feature. Patterns of association of bats at edges between old and young forest stands (hard edges) could be a result of edges acting as either a semi‐permeable barrier or a filter to movement into the forest between different‐aged forest stands for bats (or their insect prey), causing an accumulation of bat activity along the edge. …”
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Measured intrapatient radiomic variability as a predictor of treatment response in multi-metastatic soft tissue sarcoma patients by Caryn Geady, James J. Bannon, Shagheyegh Reza, Laura Madanat-Harjuoja, Denise Reinke, Scott Schuetze, Brian Crompton, Andrew Hope, Benjamin Haibe-Kains

“…Euclidean distance captures absolute differences in radiomic features, while cosine dissimilarity assesses variation in feature patterns independent of magnitude. Higher MIRV correlated with greater variability in tumor-specific response classification and volumetric response, independent of baseline tumor volume. …”
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PFVnet, a feature enhancement network for low recognition coal and rock images by Cai Han, Zhenwen Liu, Shenglei Zhao, Yubo Li, Yanwei Duan, Xinzhou Yang, Chuanbo Hao

“…We characterized the grayscale and texture feature patterns of coal-rock media under varying degrees of interference and established a comprehensive multi-element image training sample library. …”
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