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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Science and technology indicators by eyes of a librarian by V. S. Lazarev

“…The main directions of the International Conference «STI 2017: Open indicators: innovation, participation and actor-based STI indicators» (Paris, 6-8 September 2017) were the following according to the author's opinion: survey of geographical features, patterns of the science development and a choice of adequate indicators to evaluate these features and patterns; as well as development of new methods for data processing, sharing, analysis and use to assess science and technology. …”
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Illuminations of Headings in the Quran with No 259 and 275 Fixtures in Kastamonu Sheikh Sa’ban-i Veli Foundation Museum by Pelin Güleda Karadeniz

“…The double full-page heading illuminations of two Mushafs, which are thought to belong to the 1847 and 1861, which constitute the subject of the study, examined period features, stylistic features, patterns, and motifs.…”
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Uncovering memorization effect in the presence of spurious correlations by Chenyu You, Haocheng Dai, Yifei Min, Jasjeet S. Sekhon, Sarang Joshi, James S. Duncan

“…Abstract Machine learning models often rely on simple spurious features – patterns in training data that correlate with targets but are not causally related to them, like image backgrounds in foreground classification. …”
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Assessment of economic well-being in South Africa based on remote sensing transfer learning by L. Wang, L. Wang, T. Long, T. Long, E. Adam

“…CNNS are trained to predict nighttime light intensity, act as proxies for economic activity, while learning to recognize environmental features. Patterns indicating economic activity and environmental conditions can be identified from daytime images alone. …”
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“Chatbot Communication” as an Object of Linguistic Research in the System of Digital Communications by S. V. Kiseleva, A. A. Smirnova, N. A. Trofimova

“…The topic is of particular interest to linguists, since the active development of the digital environment leads to the emergence of new means and forms of speech production and speech perception, with their own distinctive features, patterns and rules of construction. Speech acts in the Internet discourse system within the framework of this work are of key importance: the object of study is located at the intersection of several disciplines (IT, Advertising and PR), but from a linguistic point of view it is practically not considered.Methodology and sources. …”
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Revealing Three-Dimensional Printing Technology Advances for Oral Drug Delivery: Application to Central-Nervous-System-Related Diseases by Samir I. Paipa-Jabre-Cantu, Marisela Rodriguez-Salvador, Pedro F. Castillo-Valdez

“…In this context, three-dimensional (3D) printing technology has introduced innovative alternatives to produce more efficient medicines with diverse features, patterns, and consistencies, particularly oral medications. …”
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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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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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Real-time Diagnosis and Prediction of Grounding Insulation Fault inTrain Power Supply System by QIN Fangfang, ZHENG Caihui, LI Xueming, LI Peng

“…In order to solve the problem of fault location and real-time evaluation of fault development trend, it proposed a real-time diagnosis and prediction method based on signal feature pattern recognition. By using the related signal characteristics of different types of faults, the characteristic variable related to various faults is designed and the corresponding diagnosis and prediction model is constructed, which realizes the detection and accurate positioning of various faults and the real-time prediction of fault trend. …”
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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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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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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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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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Multivariate bidirectional gate recurrent unit for improving accuracy of energy prediction by Quota Alief Sias, Rahma Gantassi, Yonghoon Choi

“…The proposed main model is a multi-variate bidirectional GRU combined with a periodic feature pattern. The proposed model will also be compared with the fundamental bidirectional models of the GRU and LSTM models. …”
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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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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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