Abnormal Electricity Consumption Behaviors Detection Based on Improved Deep Auto-Encoder by Nvgui LIN, Lanxiu HONG, Daoshan HUANG, Yang YI, Zhixuan LIU, Qifeng XU

“…In order to accurately detect the abnormal electricity consumption behaviors for reducing the operating costs of power companies, a detection method of abnormal electricity consumption behaviors is proposed based on the improved deep auto-encoder (DAE). Firstly, the data of normal electricity users are employed as training samples, and the effective features of the data are automatically extracted by AE; and then the data is reconstructed to calculate the detection threshold. …”
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Prediction of EGFR mutation status in non-small cell lung cancer based on CT radiomic features combined with clinical characteristics by YANG Taotao, WANG Xianqi, CHEN Cancan

“…Conclusion‍　Our comprehensive model constructed based on chest CT radiomic features and clinical characteristics shows superior predictive performance for EGFR gene mutations in NSCLC across multiple center datasets, which may be helpful for clinical decision-making for treatment strategies. …”
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Machine Learning‐Based Identification of Children With Intermittent Exotropia Using Multiple Resting‐State Functional Magnetic Resonance Imaging Features by Mengdi Zhou, Huixin Li, Xiaoxia Qu, Lirong Zhang, Xueying He, Xiwen Wang, Jie Hong, Jing Fu, Zhaohui Liu

“…The linear regression (LR) classifier with analysis of variance (ANOVA) feature selection achieved the highest area under the receiver operator characteristic curve values (0.957, 0.804, and 0.818 for the training, validation, and test datasets, respectively) using five features, including the slow‐5 fALFF values of the right inferior parietal gyrus (IPG), right supplementary motor area (SMA), left primary somatosensory complex, right frontal opercula, and left dorsolateral prefrontal cortex (DLPFC), and the accuracy, sensitivity, and specificity values were 0.759, 0.759, and 0.760, respectively. …”
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Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer by Wenwen, Zekun Jiang, Jingyan Liu, Dingbang Liu, Yiyue Li, Yushuang He, Haina Zhao, Lin Ma, Yixin Zhu, Qiongxian Long, Jun Gao, Honghao Luo, Heng Jiang, Kang Li, Xiaorong Zhong, Yulan Peng

“…Abstract Objective This study aimed to evaluate the predictive value of implementing machine learning models based on ultrasound radiomics and clinicopathological features in the survival analysis of triple-negative breast cancer (TNBC) patients. …”
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Comparative Analysis of the Operation Control System for High-speed Maglev and the CBTC Signal System for Metro by WU Weiping, HU Jian, GAO Yuan

“…The backbone of high-speed maglev system is the operation control system (OCS), which is responsible for train control and operation safety. In order to provide theoretical basis for further research and to better understand the key features of OCS, comparative researches were made between CBTC (communication based train control) system and the OCS from different aspects including system framework, routing, etc. …”
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Research on Tool Wear Monitoring Based on ET-GD and K-nearest Neighbor Algorithm by QIN Yiyuan, LIU Xianli, YUE Caixu, GUO Bin, DING Mingna

“…The fitting degree and evaluation measure of the three K-nearest neighbor models before and after the two optimization are compared and analyzed. The optimized features are used to train logical regression extreme random tree support vector regression and K-nearest neighbor algorithm models and verified by ten fold cross validation method and test set. …”
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FEATURES OF DISTANCE LEARNING IN MEDICINE by M.Н. Skikevych, L.І. Voloshyna, K.Р. Lokes, V.М. Havryliev

“…Considering a competency-based approach to professional training, the new role of the higher education instructor in the educational process is determined by us. …”
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Visual Positioning Detection of EMU Brake Pad Based on Deep Learning by GUAN Chunling, XU Yingjie, SONG Yuechao

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Identifying the risk of Kawasaki disease based solely on routine blood test features through novel construction of machine learning models by Tzu-Hsien Yang, Ying-Hsien Huang, Yuan-Han Lee, Jie-Nan Lai, Kuang-Den Chen, Mindy Ming-Huey Guo, Yan Pan, Chun-Yu Chen, Wei-Sheng Wu, Ho-Chang Kuo

“…To support frontline pediatricians with a more objective diagnostic tool, we developed and implemented KDpredictor, a machine learning-based model for KD risk identification. KDpredictor leverages only the routine blood test features, including complete blood count with differential count, C-reactive protein, and alanine aminotransferase. …”
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An Approach using Skeleton-based Representations and Neural Networks for Yoga Pose Recognition by Nguyen Hai Thanh, Truong Nguyen Nhat, Pham Linh Thuy Thi, Pham Ngoc Huynh

“…Therefore, we present an approach grounded in skeleton-based feature extraction and neural networks to find a solution to the recognition of yoga postures, creating a premise for researching a smart virtual trainer that supports home workouts for users from input image data converted into skeleton data through MoveNet. …”
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A Classification-Based Blood–Brain Barrier Model: A Comparative Approach by Ralph Saber, Sandy Rihana

“…Five different classifiers were initially trained on a dataset using eight molecular descriptors. …”
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Ensemble machine learning-based pre-trained annotation approach for scRNA-seq data using gradient boosting with genetic optimizer by Osama Elnahas, Waleed M. Ead, Yushan Qiu, Jian Lu

“…We propose an ensemble machine learning-based pre-trained annotation framework that integrates gradient boosting and genetic optimization for robust feature selection. …”
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Flight safety level improvement methodology based on the pilot model by A. D. Barabash, S. F. Borodkin, M. A. Kiselev, Yu. V. Petrov

“…One of the possible ways out of the situation may be the introduction of so-called concept of personnel training relying on the evidence-based training analysis (EBT) based not on the pursue to memorize a certain list of exercises but to develop each particular pilot’s skills and competences that could help him cope with any unpredictable situation. …”
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Acoustic-based machine learning approaches for depression detection in Chinese university students by Yange Wei, Yange Wei, Shisen Qin, Fengyi Liu, Rongxun Liu, Yunze Zhou, Yuanle Chen, Xingliang Xiong, Wei Zheng, Guangjun Ji, Yong Meng, Fei Wang, Fei Wang, Ruiling Zhang

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OMRoadNet: A Self-Training-Based UDA Framework for Open-Pit Mine Haul Road Extraction from VHR Imagery by Suchuan Tian, Zili Ren, Xingliang Xu, Zhengxiang He, Wanan Lai, Zihan Li, Yuhang Shi

“…This paper introduces OMRoadNet, an unsupervised domain adaptation (UDA) framework for open-pit mine road extraction, which synergizes self-training, attention-based feature disentanglement, and morphology-aware augmentation to address these challenges. …”
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Anomaly Detection of Deepfake Audio Based on Real Audio Using Generative Adversarial Network Model by Daeun Song, Nayoung Lee, Jiwon Kim, Eunjung Choi

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LINGUISTIC AND ACOUSTIC RESOURCES OF THE COMPUTER-BASED SYSTEM FOR ANALYSIS AND INTERPRETATION OF SPEECH INTONATION by Yu. A. Zdaranok

“…This article describes a novel approach to discriminating native and nonnative utterances based on suprasegmental features that constitute the intonation of the syntagma. …”
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Speech-Based Parkinson’s Detection Using Pre-Trained Self-Supervised Automatic Speech Recognition (ASR) Models and Supervised Contrastive Learning by Hadi Sedigh Malekroodi, Nuwan Madusanka, Byeong-il Lee, Myunggi Yi

“…The experiments, conducted using the NeuroVoz dataset, demonstrated that features extracted from the pre-trained ASR models exhibited superior performance compared to the baseline features. …”
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Classification of Russian Texts by Genres Based on Modern Embeddings and Rhythm by Ksenia Vladimirovna Lagutina

“…Models include ELMo embeddings, BERT language model with pre-training and a complex of numerical rhythm features based on lexico-grammatical features. …”
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Health Monitoring of Carbon Fiber Reinforced Building Materials Based on Phase Unwrapping Algorithm by Min Yan, Jianjun Zhou, Shuai Guo, Yanlong Li

“…Meanwhile, after introducing the multi-feature fusion strategy, the gap between the training set and the test set was significantly narrowed, significantly improving the generalization ability of the model. …”
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