Automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning: a review by Shixiao Tan, Zhen Tang, Qiang He, Ying Li, Yuliang Cai, Jiawei Zhang, Di Fan, Zhenkai Guo

“…In recent years, nonlinear dynamics methods such as chaos theory, fractal analysis, and entropy computation have provided new perspectives for EEG signal analysis, while deep learning approaches like convolutional neural networks and long short-term memory networks further enhance the robustness of dynamical pattern recognition through end-to-end nonlinear feature extraction. …”
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Fault detection and classification of bipolar DC system with dedicated metallic return based on TF-ENSR by Bo Ren, Niancheng Zhou, Qianggang Wang

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Early detection of gray mold on eggplant leaves using hyperspectral imaging technique by FENG Lei, ZHANG De-rong, CHEN Shuang-shuang, FENG Bin, XIE Chuan-qi, CHEN Youyuan, HE Yong

“…The pictures on three feature wavelengths were selected by principal component analysis (PCA), which was a good method to reduce the dimension of hyperspectral data. …”
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Iterative PolInSAR Target Decomposition for Scattering Characterization and Building Detection by Di Zhuang, Lamei Zhang, Bin Zou

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Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging by Huan Chen, Taesung Shin, Bosoon Park, Kyoung Ro, Changyoon Jeong, Hwang-Ju Jeon, Pei-Lin Tan

“…Using indium gallium arsenide (InGaAs; 800–1600 ​nm) and mercury cadmium telluride (MCT; 1000–2500 ​nm) sensors, we applied logistic regression and support vector machines by employing both linear and nonlinear kernels to analyze spectral features extracted via principal component analysis and partial least squares. …”
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PCA and PSO based optimized support vector machine for efficient intrusion detection in internet of things by Mutkule Prasad Raghunath, Shyam Deshmukh, Poonam Chaudhari, Sunil L. Bangare, Kishori Kasat, Mohan Awasthy, Batyrkhan Omarov, Rajesh R. Waghulde

“…This article presents the development of an intrusion detection system for the Internet of Things using machine learning and feature selection techniques. …”
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Machine learning and facial recognition for down syndrome detection: A comprehensive review by Khosro Rezaee

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Abnormal event detection based on local topology and l_1/2norm regularize by Qing YU, Ken CHEN, Meng LI, Fei LI

“…A new dictionary learning method was proposed by introducing a local topology term to describe structural information of video events and using the l_1/2norm as the sparsity constraint to the representation coefficients based on the traditional analysis dictionary learning method.In feature extraction,a histogram of interaction force(HOIF) containing rich motion information and a histogram of oriented gradient(HOG) containing texture information were merged.Then,the improved dictionary was used to train the feature data.Finally,the reconstruction error of the testing sample under the dictionary was used to determine whether the testing sample was an abnormal sample.Experiments on UMN show the high performance of the algorithm.Compared with the state-of-the-art algorithms,the analysis dictionary classification algorithm based on local topology and l_1/2norm has made more effective detection on the abnormal events in the crowd.…”
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Research on Fault Detection Technology for Circuit Breaker Operating Mechanism Combinations Based on Deep Residual Networks by Hongping Shao, Yizhe Jiang, Jianeng Zhao, Xueteng Li, Mingzhan Zhang, Mingkun Yang, Xinyu Wang, Hao Yang

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Methodology For Extracting Poplar Planted Fields From Very High-Resolution Imagery Using Object-Based Image Analysis and Feature Selection Strategy by E. O. Yilmaz, T. Kavzoglu, I. Colkesen, H. Tonbul, A. Teke

“…According to the SHAP analysis, the IHS feature was the most effective one in the constructed RF model, followed by the CI (red edge), NDVI-1 and NDVI-2 vegetation indices.…”
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ViTAU: Facial paralysis recognition and analysis based on vision transformer and facial action units by Jia GAO, Wenhao CAI, Junli ZHAO, Fuqing DUAN

“…This innovative approach not only enhances the accuracy of facial paralysis detection but also contributes to facial medical imaging.…”
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GENDER FEATURES OF THE POPULATION'S ATTITUDE TO MEDICINES by N. B. Dremova, N. P. Yaroshenko, N. I. Afanaseva, S. V. Solomka

“…The study of gender features of attitudes visitors of pharmacies to medicines.Materials and Methods. …”
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Feasibility of EfficientDet-D3 for Accurate and Efficient Void Detection in GPR Images by Sung-Pil Shin, Sang-Yum Lee, Tri Ho Minh Le

“…This study presents a novel approach using the EfficientDet-D3 deep learning model for automated void detection in GPR images. The model combines advanced feature extraction and compound scaling to balance accuracy and computational efficiency, making it suitable for real-time applications. …”
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ECG-based cardiac arrhythmia classification using fuzzy encoded features and deep neural networks by Kiruthika Balakrishnan, Durgadevi Velusamy, Karthikeyan Ramasamy, Lisiane Pruinelli

“…Compared to conventional deep learning models that rely on raw ECG signals, our method enhances interpretability and feature extraction by incorporating time–frequency analysis and fuzzy feature encoding. …”
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Features of vertebral arteries pathology at trauma by V. V. Shchedrenok, T. V. Zakhmatova, O. V. Moguchaya

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Vigilance State Classification of Comatose Patients Based on Multifractal Analysis of EEG Signals by Bechir Hbibi, Lamine Mili, Kamel Baccar, Abdelkader Mami

“…This information can be used for the detection or classification of several diseases using many signal processing methods. …”
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Unsupervised Anomaly Detection Method for Electrical Equipment Based on Audio Latent Representation and Parallel Attention Mechanism by Wei Zhou, Shaoping Zhou, Yikun Cao, Junkang Yang, Hongqing Liu

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Postinfectious epilepsy: clinical and diagnostical features by А. V. Vasilenko, А. Yu. Ulitin, L. S. Onishchenko, N. I. Ananyeva, R. V. Grebenshchikova, О. N. Gaykova, А. V. Ivanenko, S. S. Kolosov, S. А. Turanov, S. N. Chudievich

“…Gross and marked diffuse disturbances in brain bioelectrical activity were most often detected (58% and 31%, respectively) during video-EEG monitoring in Group 1, whereas moderate alterations were recorded less frequently (11% of observations). …”
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Phish Fighter: Self Updating Machine Learning Shield Against Phishing Kits Based on HTML Code Analysis by Gabriela Brezeanu, Alexandru Archip, Codrut-Georgian Artene

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Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment Detection by Seyedeh Gol Ara Ghoreishi, Charles Boateng, Sonia Moshfeghi, Muhammad Tanveer Jan, Joshua Conniff, Kwangsoo Yang, Jinwoo Jang, Borko Furht, David Newman, Ruth Tappen, Monica Rosselli, Kelley L. Jackson

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