Dataset of polarimetric images of mechanically generated water surface waves coupled with surface elevation records by wave gauges linear arrayScienceDB by Noam Ginio, Michael Lindenbaum, Barak Fishbain, Dan Liberzon

“…Existing techniques are often cumbersome, computationally heavy and generally suffer from limited wavenumber/frequency response. To address these challenges a novel method was developed, using polarization filter equipped camera as the main sensor and Machine Learning (ML) algorithms for data processing [1,2]. …”
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Detection of DRFM Deception Jamming Based on Diagonal Integral Bispectrum by Dianxing Sun, Ao Li, Hao Ding, Jifeng Wei

“…The transponder-style deception jamming implemented by Digital Radio Frequency Memory (DRFM) exhibits high similarity to real target radar echoes, while traditional detection methods suffer severe performance degradation under low signal-to-noise ratio (SNR) conditions. …”
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Identifying Disinformation on the Extended Impacts of COVID-19: Methodological Investigation Using a Fuzzy Ranking Ensemble of Natural Language Processing Models by Jian-An Chen, Wu-Chun Chung, Che-Lun Hung, Chun-Ying Wu

“…Experimental results reveal that language models, particularly XLNet, outperform traditional approaches that combine term frequency–inverse document frequency features with support vector machine or utilize deep models like HAN. …”
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DNA sequence classification for diabetes mellitus using NuSVC and XGBoost: A comparative. by Said A Salloum, Khaled Mohammad Alomari, Ayham Salloum

“…Traditional diagnostic methods rely heavily on clinical symptoms and biochemical tests, which may not capture the underlying genetic predispositions. …”
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Investigation of Compact Metallic Environments on Power Attenuation and Read Performance of HF RFID by Herrick Han Lin Yeap, Kok Seng Eu, Kian Meng Yap

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Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program. by Vitaly Lorman, L Charles Bailey, Xing Song, Suchitra Rao, Mady Hornig, Levon Utidjian, Hanieh Razzaghi, Asuncion Mejias, John Erik Leikauf, Seuli Bose Brill, Andrea Allen, H Timothy Bunnell, Cara Reedy, Abu Saleh Mohammad Mosa, Benjamin D Horne, Carol Reynolds Geary, Cynthia H Chuang, David A Williams, Dimitri A Christakis, Elizabeth A Chrischilles, Eneida A Mendonca, Lindsay G Cowell, Lisa McCorkell, Mei Liu, Mollie R Cummins, Ravi Jhaveri, Saul Blecker, Christopher B Forrest, RECOVER Consortium

“…Our method, an extension of the Phe2Vec algorithm, uses tens of thousands of clinical concepts from multiple domains to represent patients' clinical histories to then identify groups of patients with similar presentations. …”
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Analysis of Acoustic Signals of Footsteps from the Piezoelectric Sensor by Bilge Çiğdem Çiftçi, Gamze Kaya, Mustafa Kurt

“…We analyzed the acoustic signal data by assuming that the electrical output voltage of the piezoelectric sensor completely coincides with the frequency of the acoustic signals. The original signal was pre-processed using filtering systems and analyzed by the fast Fourier transform and power spectral density methods to extract descriptive spectral features of the signal. …”
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Identification of Voltage Sag Source in Distribution Network Based on BAS-SVM by Haitao LIU, Xiaoyi YE, Ganyun LÜ, Huajun YUAN, Zongpu GENG

“…In order to improve the identification accuracy of different voltage sag disturbance sources, a voltage sag source identification method based on beetle antennae search (BAS) and support vector machine (SVM) is proposed. …”
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Determination of the Bentonite Content in Molding Sands Using AI-Enhanced Electrical Impedance Spectroscopy by Xiaohu Ma, Alice Fischerauer, Sebastian Haacke, Gerhard Fischerauer

“…Optimizing the control of these mixtures and the recycling of used sand after casting requires an efficient in-line monitoring method, which is currently unavailable. This study explores the potential of an AI-enhanced electrical impedance spectroscopy (EIS) system as a solution. …”
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Energy Evolution and AE Failure Precursory Characteristics of Rocks with Different Rockburst Proneness by Feng Pei, Hongguang Ji, Jiwei Zhao, Jingming Geng

“…The results show that the rockburst proneness of granite is obviously stronger than that of metagabbro based on the comprehensive evaluation method of multiple rockburst proneness index. The reasons for different rockburst proneness are analyzed from the perspective of mineral composition and microstructure. …”
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Monthly Runoff Prediction Based on STL-CEEMDAN-LSTM Model by WANG Hai, SHEN Yanqing, QI Shansheng, PAN Hongzhong, HUO Jianzhen, WANG Zhance

“…According to the nonlinear and non-stationary characteristics of monthly runoff sequences, the quadratic decomposition method was combined with machine learning to construct a model for predicting monthly runoff. …”
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The Effects of Whole Body Vibration on the Limits of Stability in Adults With Subacute Ankle Injury by Sonia Young, Harvey W. Wallmann, Kailey L. Quiambao, Brooke M. Grimes

“… # Study Design Quasi-experimental, pretest-posttest design. # Methods Fifteen participants ages 19-27 years (Mean age 22±2.36) with either a Grade I or Grade II lateral ankle sprain received WBV in bilateral stance under three randomized conditions (high frequency-25 Hz, low frequency-6 Hz, and control, which consisted of bilateral stance with machine off) for six minutes over three sessions (one time per week). …”
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Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals by Yazeed Alkhrijah, Yazeed Alkhrijah, Shehzad Khalid, Shehzad Khalid, Syed Muhammad Usman, Amina Jameel, Muhammad Zubair, Haya Aldossary, Aamir Anwar, Saad Arif

“…In recent years, multiple researchers have proposed multiple machine learning and deep learning-based methods to predict the onset of seizures using electroencephalogram (EEG) signals before they occur; however, robust preprocessing to mitigate the effect of noise, channel selection to reduce dimensionality, and feature extraction remain challenges in accurate prediction.MethodsThis study proposes a novel method for accurately predicting epileptic seizures. …”
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Exploration-Driven Genetic Algorithms for Hyperparameter Optimisation in Deep Reinforcement Learning by Bartłomiej Brzęk, Barbara Probierz, Jan Kozak

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Dynamic reconstruction of electroencephalogram data using RBF neural networks by Xuan Wang, Congcong Du, Xianjin Ke, Jian Zhang, Zheng Zheng, Yayan Yue, Ming Yu

“…IntroductionElectroencephalography (EEG) is widely used for analyzing brain activity; however, the nonlinear and nature of EEG signals presents significant challenges for traditional analysis methods. Machine has shown great promise in addressing these limitations. …”
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Gaussian Process Regression Total Nitrogen Prediction Based on Data Decomposition Technology and Several Intelligent Algorithms by WANG Yongshun, CUI Dongwen

“…Total nitrogen (TN) is one of the important indicators to reflect the degree of water pollution and measure the eutrophication status of lakes and reservoirs.To improve the accuracy of TN prediction,based on the empirical wavelet transform (EWT) and wavelet packet transform (WPT) decomposition technology,this paper proposes a Gaussian process regression (GPR) prediction model optimized by osprey optimization algorithm (OOA),rime optimization algorithm (ROA),bald eagle search (BES) and black widow optimization algorithm (BWOA) respectively.Firstly,the TN time series is decomposed into several more regular subsequence components by EWT and WPT respectively.Then,the paper briefly introduces the principles of OOA,ROA,BES,and BWOA algorithms and applies OOA,ROA,BES,and BWOA to optimize GPR hyperparameters.Finally,EWT-OOA-GPR,EWT-ROA-GPR,EWT-BES-GPR,EWT-BWOA-GPR,WPT-OOA-GPR,WPT-ROA-GPR,WPT-BES-GPR,WPT-BWOA-GPR models (EWT-OOA-GPR and other eight models for short) are established to predict the components of TN by the optimized super-parameters.The final prediction results are obtained after reconstruction,and WT-OOA-GPR,WT-ROA-GPR,WT-BES-GPR and WT-BWOA-GPR models based on wavelet transform (WT) are built.Eight models,including EWT-OOA-SVM based on support vector machine (SVM),the paper compares the unoptimized EWT-GPR,WPT-GPR models,and the uncomposed OOA-GPR,ROA-GPR,BES-GPR,and BWOA-GPR models.The models were verified by the monitoring TN concentration time series data of Mudihe Reservoir,an important drinking water source in China,from 2008 to 2022.The results are as follows.① The average absolute percentage error of eight models such as EWT-OOA-GPR for TN prediction is between 0.161% and 0.219%,and the coefficient of determination is 0.999 9,which is superior to other comparison models,with higher prediction accuracy and better generalization ability.② EWT takes into account the advantages of WT and EMD.WPT can decompose low-frequency and high-frequency signals at the same time.Both of them can decompose TN time series data into more regular modal components,significantly improving the accuracy of model prediction,and the decomposition effect is better than that of the WT method.③ OOA,ROA,BES,and BWOA can effectively optimize GPR hyperparameters and improve GPR prediction performance.…”
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A Temporal-Spectral Fused and Attention-Based Deep Model for Automatic Sleep Staging by Guidan Fu, Yueying Zhou, Peiliang Gong, Pengpai Wang, Wei Shao, Daoqiang Zhang

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Optimized CNN-Bi-LSTM–Based BCI System for Imagined Speech Recognition Using FOA-DWT by Meenakshi Bisla, Radhey Shyam Anand

“…Neural correlates of speech imagery EEG signals are variable and weak as compared to the vocal state; hence, it is challenging to interpret them using machine learning (ML)–based classifiers. The applicability of modern deep learning methods such as convolutional neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) networks has seen substantial advances in complex EEG signal analysis as compared to ML-based methods. …”
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Aplicación de técnicas clásicas y avanzadas de procesamiento de vibraciones al diagnóstico de cojinetes. Análisis experimental. // Application of classic and advanced techniques of... by F. E. Hernández Montero, V. Atxa Uribe

“…These methods can be extended to the diagnosis of other machine components.…”
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OPTIMAL PARAMETERS OF ASYMMETRICAL OSCILLATIONS OF THE TOOL FOR CIRCULARING WITH VISCOUS STRUCTURAL STEEL by S. S. Danilchyk, V. K. Sheleh

“…A turning method with asymmetric oscillations of cutting tool has been proposed on the basis of the analysis of various methods for chip crushing in the process of structural steel lathe turning and the method makes it possible to ensure a stable crushing of discharge chips and to reduce roughness of the machined surfaces. …”
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