Radar signal recognition exploiting information geometry and support vector machine by Yuqing Cheng, Muran Guo, Limin Guo

“…As a consequence, the recognition accuracy for LPI radar signals with similar time‐frequency images is effectively improved. In addition, the proposed algorithm has better robustness to small samples than other deep learning‐based algorithms, since the SVM method minimises the structural risk instead of the empirical risk. …”
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A Novel Machine Learning Technique for Fault Detection of Pressure Sensor by Xiufang Zhou, Aidong Xu, Bingjun Yan, Mingxu Gang, Maowei Jiang, Ruiqi Li, Yue Sun, Zixuan Tang

“…Blockage is recognized as a common failure in pressure sensing lines; therefore, a novel detection method based on Trend Features in Time–Frequency domain characteristics (TFTF) is proposed in this paper. …”
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Relevance Vector Machines for Enhanced BER Probability in DMT-Based Systems by Ashraf A. Tahat, Nikolaos P. Galatsanos

“…A new channel estimation method for discrete multitone (DMT) communication system based on sparse Bayesian learning relevance vector machine (RVM) method is presented. …”
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Fault Line Selection of Single Phase Grounding Based on Wavelet Packet Full Frequency Analysis and OS-ELM by JI Wen-lu, ZHAO Xiao-long, ZHANG Ming, YANG Hong-lei, WENG Jia-ming

“…In order to enhance effectiveness of single-phase ground fault feature extraction and to achieve exactly identification of fault line selection，a new fault line selection method of single phase grounding fault based on wavelet packet and online sequential extreme learning machine ( OS-ELM) is proposed. …”
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Source-Free Domain Adaptation Framework for Rotary Machine Fault Diagnosis by Hoejun Jeong, Seungha Kim, Donghyun Seo, Jangwoo Kwon

“…The experimental results show that our method outperforms conventional machine learning and deep learning models in both F1-score and recall across domains. …”
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Analysis of Mechanism of Formation of Interference at the Output Multilevel Finite State Machine by Y. A. Nikitin

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Computational Feasibility Study for Time-Frequency Analysis of Non-Stationary Vibration Signals Based on Wigner-Ville Distribution by Luis Otávio de Angeles Dias, Pedro Oliveira Conceição Junior, Paulo Monteiro de Carvalho Monson

“…Three approaches were pursued: the first consisting in calculating the average WVD from equidistant time windows; the second consisting in reducing the sampling rate by a factor of ‘k’ by creating an array where each ‘nth’ element corresponds to the ‘k*nth’ element of the original signal; and the third consisting in a joint analysis, incorporating a preprocessing routine into the second method. The mean WVD method distorted the time-frequency diagram with middle-range frequencies, while the second approach preserved the WVD, even with significant ‘k’ factors, reducing analysis time significantly. …”
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Induction Machine Bearing Fault Detection Using Empirical Wavelet Transform by Ricardo Lopez-Gutierrez, Jose de Jesus Rangel-Magdaleno, Carlos Javier Morales-Perez, Arturo García-Perez

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Digital Art Feature Association Mining Based on the Machine Learning Algorithm by Zhiying Wu, Yuan Chen

“…In order to mine some correlation features in data, a heuristic feature mining method based on minimum support was studied to discover the frequency of correlation features and construct the optimal feature subset. …”
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A Novel Fault Diagnosis Method Using FCEEMD-Based Multi-Complexity Low-Dimensional Features and Directed Acyclic Graph LSTSVM by Rongrong Lu, Miao Xu, Chengjiang Zhou, Zhaodong Zhang, Kairong Tan, Yuhuan Sun, Yuran Wang, Min Mao

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Heart rate variability in soccer players and the application of unsupervised machine learning by Wollner Materko, Sávio Andrei Medeiros Miranda, Thiago Henrique Lobato Bezerra, Carlos Alberto Machado de Oliveira Figueira

“…Aim: This study aimed to investigate the relationship between heart rate variability (HRV) parameters and performance in soccer players. Methods: This study used a cross-sectional design to assess HRV parameters in a cohort of twenty-nine male athletes, aged 18 to 20 years, randomly selected from the Macapá Sports Club team in the Amazon region. …”
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A Novel Open Circuit Fault Diagnosis for a Modular Multilevel Converter with Modal Time-Frequency Diagram and FFT-CNN-BIGRU Attention by Ziyuan Zhai, Ning Wang, Siran Lu, Bo Zhou, Lei Guo

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Enhanced IoT cybersecurity through Machine Learning - based penetration testing by Mohammed J. BAWANEH, Obaida M. AL-HAZAIMEH, Malek M. AL-NAWASHI, Monther H. AL-BSOOL, Essam HANANDAH

“…A network connects all objects using technologies such as Radio Frequency Identification (RFID), sensors, GPS, or Machine-to-Machine (M2M) communication. …”
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Machine learning assisted noncontact neonatal anthropometry using FMCW radar by Jun Byung Park, Jae Yoon Na, Seung Hyun Kim, Jinjoo Choi, Jihyun Keum, Sung Ho Cho, Hyun-Kyung Park

“…Abstract This study proposes a method for measuring the height and weight of a neonate conveniently, safely, and accurately by applying a convolutional neural network to frequency-modulated continuous-wave (FMCW) radar sensor data. …”
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Frequency Stability Analysis Based on Full State Model in Autonomous-Synchronization Voltage Source Interfaced Power System by Zhenyao LI, Deqiang GAN, Moude LUAN, Guoqing HE

“…Finally, the correctness and effectiveness of the above conclusions and methods are verified by a system with 10 machines and 39 nodes.…”
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Spatio-temporal characteristics in the GEONET F5 solution in the frequency domain estimated based on the robust spectral analysis by Masayuki Kano, Keisuke Yano, Yusuke Tanaka, Tetsuya Takabatake, Yusaku Ohta

“…The frequency dependence of the spectra indicated temporally correlated observation noise, even in the high-frequency components. …”
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Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning Analysis by Marcos Uceta, Alberto del Cerro‐León, Danylyna Shpakivska‐Bilán, Luis M. García‐Moreno, Fernando Maestú, Luis Fernando Antón‐Toro

“…Recent studies have changed their scope into finding predisposition factors that may lead adolescents into this kind of patterns of consumption. Methods In this article, using unsupervised machine learning (UML) algorithms, we analyze the relationship between electrophysiological activity of healthy teenagers and the levels of consumption they had 2 years later. …”
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Machine learning-based spectrum occupancy prediction: a comprehensive survey by Mehmet Ali Aygül, Mehmet Ali Aygül, Hakan Ali Çırpan, Hüseyin Arslan

“…This survey provides a comprehensive overview of machine learning (ML)-based SOP methods that address these challenges. …”
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A Comprehensive Review of Machine Learning Approaches for Flood Depth Estimation by Bo Liu, Yingbing Li, Minyuan Ma, Bojun Mao

“…Abstract In the context of increasing frequency and impact of flood events, traditional methods for estimating flood depth have become insufficient to meet current demands, leading to a gradual shift toward machine learning approaches. …”
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Machine Learning and Deep Learning for Wildfire Spread Prediction: A Review by Henintsoa S. Andrianarivony, Moulay A. Akhloufi

“…The increasing frequency and intensity of wildfires highlight the need to develop more efficient tools for firefighting and management, particularly in the field of wildfire spread prediction. …”
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