Detection of Cardiac Function Abnormality from MRI Images Using Normalized Wall Thickness Temporal Patterns by Mai Wael, El-Sayed H. Ibrahim, Ahmed S. Fahmy

“…To develop a method for identifying abnormal myocardial function based on studying the normalized wall motion pattern during the cardiac cycle. Methods. The temporal pattern of the normalized myocardial wall thickness is used as a feature vector to assess the cardiac wall motion abnormality. …”
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Artificial intelligence in automatic image segmentation system for exploring recurrence patterns in small cell carcinoma of the lung by Jing Shen, Shaobin Wang, Hui Guan, Mingyi Di, Zhikai Liu, Qi Chen, Mei Li, Jie Shen, Ke Hu, Fuquan Zhang

“…Upon analyzing recurrence patterns among 94 patients, a total of 170 instances were examined. …”
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Leakage Detection in Subway Tunnels Using 3D Point Cloud Data: Integrating Intensity and Geometric Features with XGBoost Classifier by Anyin Zhang, Junjun Huang, Zexin Sun, Juju Duan, Yuanai Zhang, Yueqian Shen

“…These challenges primarily arise from the prevalence of noise in tunnel point clouds and the difficulty in accurately capturing the three-dimensional morphological characteristics of leakage patterns. To address these limitations, this study proposes a classification method based on XGBoost classifier, integrating both intensity and geometric features. …”
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A Study on Partial Discharge Fault Identification in GIS Based on Swin Transformer-AFPN-LSTM Architecture by Jiawei Li, Shangang Ma, Fubao Jin, Ruiting Zhao, Qiang Zhang, Jiawen Xie

“…Aiming at the problem of manual feature extraction and insufficient mining of feature information for partial discharge pattern recognition under different insulation faults in GIS, a deep learning model based on phase and timing features with Swin Transformer-AFPN-LSTM architecture is proposed. …”
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GT-SRR: A Structured Method for Social Relation Recognition with GGNN-Based Transformer by Dejiao Huang, Menglei Xia, Ruyi Chang, Xiaohan Kong, Shuai Guo

“…However, existing methods primarily focus on modeling individual visual traits, interaction patterns, and scene-level contextual cues, often failing to capture the complex dependencies among these features and the hierarchical structure of social groups, which are crucial for effective reasoning. …”
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Modelling effective soil depth at field scale from soil sensors and geomorphometric indices by Mauricio Castro Franco, Marisa Domenech, José Luis Costa, Virginia Carolina Aparicio

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Visual Perception and Fixation Patterns in an Individual with Ventral Simultanagnosia, Integrative Agnosia and Bilateral Visual Field Loss by Isla Williams, Andrea Phillipou, Elsdon Storey, Peter Brotchie, Larry Abel

“…We hypothesised that gaze patterns would differ with successful processing and that feature integration could be disrupted by distractors. …”
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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

“…The feature patterns of the actively controlled group significantly differed from those of the inactively controlled group. …”
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Clinical and Dermoscopic Patterns of Basal Cell Carcinoma and Its Mimickers in Skin of Color: A Practical Summary by Emmanouil Karampinis, Konstantina-Eirini Georgopoulou, Elli Kampra, Efterpi Zafiriou, Aimilios Lallas, Elizabeth Lazaridou, Zoe Apalla, Biswanath Behera, Enzo Errichetti

“…The most prevalent differentiating dermoscopic feature for BCC includes blue, black and gray dots, though arborizing vessels still remain the predominant BCC feature, even in dark phototypes. …”
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Weighted Filter Bank and Regularization Common Spatial Pattern-Based Decoding Algorithm for Brain-Computer Interfaces by Jincai Ye, Jiajie Zhu, Shoulin Huang

“…To address this challenge, this study proposes a mutual information weighted filter bank regularized common spatial pattern (WFBRCSP) algorithm. The algorithm divides the signal into multiple frequency bands, adaptively assigns subject weights based on the mutual information maximization criterion, and optimizes the covariance matrix with a regularization strategy, significantly improving the robustness of feature extraction. …”
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Modulation pattern recognition method of wireless communication automatic system based on IABLN algorithm in intelligent system. by Ting Xie, Xing Han

“…The aim of this study is to address the limitations of convolutional networks in recognizing modulation patterns. These networks are unable to utilize temporal information effectively for feature extraction and modulation pattern recognition, resulting in inefficient modulation pattern recognition. …”
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LSTM-based framework for predicting point defect percentage in semiconductor materials using simulated XRD patterns by Mehran Motamedi, Reza Shidpour, Mehdi Ezoji

“…This LSTM-based method offers a novel approach to predicting defect percentages using simulated XRD patterns of materials.…”
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A Novel Method for Recognition of Bioradiolocation Signal Breathing Patterns for Noncontact Screening of Sleep Apnea Syndrome by Maksim Alekhin, Lesya Anishchenko, Alexander Tataraidze, Sergey Ivashov, Vladimir Parashin, Lyudmila Korostovtseva, Yurii Sviryaev, Alexey Bogomolov

“…A novel method for recognition of breathing patterns of bioradiolocation signals breathing patterns (BSBP) in the task of noncontact screening of sleep apnea syndrome (SAS) is proposed and implemented on the base of wavelet transform (WT) and neural network (NNW) applications. …”
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Efficacy of Mepolizumab in the Treatment of Severe Asthma with a Mixed Granulocytic Pattern of Airway Inflammation (Case Report) by A. M. Gubareva, S. V. Fedosenko, D. A. Vinokurova, S. V. Nesterovich, E. S. Kulikov, D. V. Polyakova, E. A. Starovoitova

“…A feature of one of the cases was the presence of cylindrical bronchiectasis in both lungs. …”
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Can Amygdala-Derived-EEG-fMRI-Pattern (EFP) Neurofeedback Treat Sleep Disturbances in PTSD? by Aron Tendler, Yaki Stern, Tal Harmelech

“…Background: Sleep disturbances are a core feature of post-traumatic stress disorder (PTSD), affecting up to 90% of patients and often persisting after standard PTSD treatment. …”
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DDA-MSLD: A Multi-Feature Speech Lie Detection Algorithm Based on a Dual-Stream Deep Architecture by Pengfei Guo, Shucheng Huang, Mingxing Li

“…It can perform in-depth sequence pattern analysis on manually extracted static prosodic features and nonlinear dynamic features, obtaining high-order dynamic features related to lies. …”
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Parametric and nonparametric two-sample tests for feature screening in class comparison: a simulation study by Elena Landoni, Federico Ambrogi, Luigi Mariani, Rosalba Miceli

“…AD, CvM, ZA and ZC tests proved to be the most sensitive tests in mixture distribution patterns, while still maintaining a high power in normal distribution patterns. …”
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Commonalities and differences in gene expression patterns in major depressive disorder and chronic spontaneous urticaria: implications for comorbidity by Yibo Jiang, Wen Li, Lihao Zheng, Nan Ba

“…Further feature selection yielded 6 core transcriptomic features in the total population (BCL11A, BEX2, C5AR1, DDX60L, LCE3D, NAMPT), 2 in males (GNAQ, RNF19B), and 4 in females (GNG7, LCE3D, PYGL, UPP1). …”
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Identification of Depression Patients Using LIF Spiking Neural Network Model From the Pattern of EEG Signals by Rekha Sahu, Prasant Kumar Pattnaik, Kalaiarasi Sonai Muthu Anbananthen, Saravanan Muthaiyah

“…Interpreting electroencephalography signals and the abnormality of the signals can help to find the specific pattern for specific diseases like depression. A Spiking Neural Network is a machine learning approach that emphasizes the data value and manipulates the value to find the particular signal feature. …”
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Similarity measure for intuitionistic fuzzy sets and its applications in pattern recognition and multimodal medical image fusion by Anjali Patel, Deepa Gupta, E. A. Gopalakrishnan, Divya Sasidharan, V. Sowmya, Mohammed Zakariah, Abdulaziz S. Almazyad

“…Then, we use the proposed IFSM to extract the features of base subimages and define two fusion rules to fuse the base subimages and detailed subimages. …”
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