Study of Detection of Typical Pesticides in Paddy Water Based on Dielectric Properties by Shuanggen Huang, Mei Yang, Junshi Huang, Longwei Shang, Qi Chen, Fang Peng, Muhua Liu, Yan Wu, Jinhui Zhao

“…Principal component analysis (PCA) and competitive adaptive reweighted sampling (CARS) were used to extract characteristic frequencies. …”
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Review of Masked Face Recognition Based on Deep Learning by Bilal Saoud, Abdul Hakim H. M. Mohamed, Ibraheem Shayea, Ayman A. El-Saleh, Abdulaziz Alashbi

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Experimental Comparative Investigations to Evaluate Cavitation Conditions within a Centrifugal Pump Based on Vibration and Acoustic Analyses Techniques by Ahmed Ramadhan AL-OBAIDI

“…The results obtained from vibration and acoustic signals in time and frequency domains were analysed in order to achieve better understanding regarding detection of cavitation within a pump. The effect of different operating conditions related to the cavitation was investigated in this work using different statistical features in time domain analysis (TDA). …”
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FEATURE EXTRACTION METHOD OF SOUND SIGNAL TO ROLLING BEARING BASED ON BLIND SOURCE SEPARATION AND MORLET WAVELET by LI JingJiao, CHEN EnLi, LIU YongQiang

“…It is a new method of sound signal testing and analysis,but the SNR is too low,so we raise a feature extraction method of sound signal to rolling bearing based on blind source separation and adaptive Morlet wavelet. …”
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STATE PREDICTION OF WIND TURBINE GENERATOR BASED ON K-CNN AND N-GRU (MT) by CHAI Tong, YUAN YiPing, MA JunYan, FAN PanPan

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Diagnostic model for COPD patients with nocardia infection: a study based on clinical features and risk factors by Kai Zhang, Kangli Yang, Hongmin Wang

“…Conclusion: This validated nomogram provides a clinically actionable tool for early Nocardia detection in COPD patients, addressing a critical diagnostic gap. …”
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Prediction of Parkinson Disease Using Long-Term, Short-Term Acoustic Features Based on Machine Learning by Mehdi Rashidi, Serena Arima, Andrea Claudio Stetco, Chiara Coppola, Debora Musarò, Marco Greco, Marina Damato, Filomena My, Angela Lupo, Marta Lorenzo, Antonio Danieli, Giuseppe Maruccio, Alberto Argentiero, Andrea Buccoliero, Marcello Dorian Donzella, Michele Maffia

“…That is why, the voice can be nominated as the non-invasive method to detect PD from healthy subjects (HS). <b>Methods:</b> Our study was based on cross-sectional study to analysis voice impairment. …”
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Composite fault feature extraction for gears based on MCKD-EWT adaptive wavelet threshold noise reduction by Yanchang LV, Jingyue Wang, Chengqiang Zhang, Jianming Ding

“…The results of experimental data analysis show that compared with the feature extraction methods such as spatial scale threshold EWT-MCKD and Complete Ensemble Empirical Mode Decomposition (CEEMDAN)-MCKD, the proposed method is more suitable for the diagnosis of gear composite faults in a strong background noise environment, the noise interference is effectively suppressed, and the extraction effect of gear composite fault features is more obvious.…”
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Improved Liquefaction Hazard Assessment via Deep Feature Extraction and Stacked Ensemble Learning on Microtremor Data by Oussama Arab, Soufiana Mekouar, Mohamed Mastere, Roberto Cabieces, David Rodríguez Collantes

“…We created a synthetic dataset of 1000 samples using realistic feature ranges that mimic the Rif data region to test model performance and conduct sensitivity analysis. …”
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Genomic insights into host-associated variants and transmission features of a ToBRFV isolate from Mexico by Erika Janet Zamora-Macorra, Daniel Leobardo Ochoa-Martínez, Claudia Yaritza Chavarín-Camacho, Rosemarie W. Hammond, Katia Aviña-Padilla

“…Understanding its genomic features and transmission mechanisms is critical for effective disease management. …”
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Hybrid Paddy disease classification using optimized statistical feature based transformation technique with explainable AI by M Amudha, Brindha K

“…The next step is to use a Cat Boost classifier to sort the features. Using SHAP analysis and the Cat Boost classifier, this study delves deeper into the model’s classification process and the effects of each feature on the model’s operation. …”
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Clinical features and prognostic factors of pediatric Langerhans cell histiocytosis: a single-center retrospective study by Yunfeng Lu, Liying Liu, Qi Wang, Bingju Liu, Ping Zhao, Guotao Guan, Yunpeng Dai

“…An analysis was conducted on 82 recently identified LCH cases to retrospectively evaluate the initial symptoms, therapeutic alternatives, and extended results. …”
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FEBE-Net: Feature Exploration Attention and Boundary Enhancement Refinement Transformer Network for Bladder Tumor Segmentation by Chao Nie, Chao Xu, Zhengping Li

“…The automatic and accurate segmentation of bladder tumors is a key step in assisting urologists in diagnosis and analysis. At present, existing Transformer-based methods have limited ability to restore local detail features and insufficient boundary segmentation capabilities. …”
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A Unified Framework for Fault and Performance Prediction Using Spatio-Temporal Geometric Features Based on STSFE by Dong-Hyun Kang, A-Youn Yang, Jong-Min Lee, Jong-Gu Lee

“…This paper proposes a unified deep-learning framework for fault and performance prediction in communication equipment by utilizing spatiotemporal geometric features. The core methodology, Spatio-Temporal Slope Feature Extraction (STSFE), transforms irregular time-series data into slope-, area-, and volume-based representations, capturing both temporal dynamics and spatial correlations. …”
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FEPA-Net: A Building Extraction Network Based on Fusing the Feature Extraction and Position Attention Module by Yuexin Liu, Yulin Duan, Xuya Zhang, Wen Zhang, Chang Wang

“…This substitution aims to broaden the receptive field, with the primary intention of enabling the output of each convolution layer to incorporate a broader spectrum of feature information. Additionally, a feature extraction module is added to mitigate the loss of detailed features. …”
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ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning by Raiyan Jahangir, Muhammad Nazrul Islam, Md. Shofiqul Islam, Md. Motaharul Islam

“…As an outcome, the stack clas- sifier with XGBoost as the meta-classifier, trained with 65 important features determined by the Principal Component Analysis (PCA) technique, achieved the best performance among all the models. …”
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DIAGNOSIS OF ARRHYTHMIA DISEASES USING HEART SOUNDS AND ECG SIGNALS by V. Kalaivani

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The Accurate Interpretation and Clinical Significance of Morphological Features of Fine Needle Aspiration Cells in Papillary Thyroid Carcinoma by Xue-Jiao Xiong, Ming-Ming Xiao, Yi-Xia Zhang, Dong-Ge Liu, Mu-Lan Jin, Jian Wang, Hong-Tao Xu, Qing-Chang Li, Guang-Ping Wu

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Machine learning-based feature selection for ultra-high-dimensional survival data: a computational approach by Nahid Salma, Majid Khan Majahar Ali, Raja Aqib Shamim

“…Gene interaction network analysis confirmed their role in RCC progression. Despite SCAD’s strong performance, it left 31% of data variability unexplained, suggesting hybrid ML models that integrate ensemble learning, two-component regression structures, and deep learning-based feature selection could further enhance gene selection and predictive accuracy. …”
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Early identification of autism spectrum disorder in preschoolers by static and dynamic amplitude of low-frequency fluctuations features by Kanghui Yu, Kanghui Yu, Shoujun Xu, Shishun Fu, Kelei Hua, Yi Yin, Qiang Lei, Jinwu Liu, Yunfan Wu, Guihua Jiang, Guihua Jiang

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