A new method of semi-supervised learning classification based on multi-mode augmentation in small labeled sample environment by Yuxuan Liu, Chenglin Wen

“…Abstract Semi-supervised learning mitigates the problem of labeled data scarcity by utilizing unlabeled data, but the generalization performance of existing methods usually degrades significantly when the unlabeled data is small in size or poor in quality. …”
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A novel criterion for crack detection in beam structures by frequency response functions by Nguyen Tien Khiem, Tran Thanh Hai, Le Khanh Toan, Nguyen Thi Lan, Ho Quang Quyet

“…However, the complex feature of FRF has not been thoroughly employed for structural damage detection. …”
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Develop Approach to Predicate Software Reliability Growth Model Parameters Based on Machine learning by anfal A. Fadhil, Asmaa’ H. AL_Bayati, Ibrahim Ahmed Saleh

“…One of the most important aspects in determining the quality of a software product before placing it on the market is its reliability. The main problem in creating effective software that satisfies the user preferences is that it must be highly reliable. …”
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Option Pricing under the Jump Diffusion and Multifactor Stochastic Processes by Shican Liu, Yanli Zhou, Yonghong Wu, Xiangyu Ge

“…In financial markets, there exists long-observed feature of the implied volatility surface such as volatility smile and skew. …”
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Few-Shot Learning for Triplet-Based EV Energy Consumption Estimation by Alminas Čivilis, Linas Petkevičius, Simonas Šaltenis, Kristian Torp, Ieva Markucevičiūtė-Vinckė

“…In particular, using few-shot learning techniques, we tackle the problem of the need to create different energy prediction models for different EV types, from small EVs to electric buses. …”
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DualCascadeTSF-MobileNetV2: A Lightweight Violence Behavior Recognition Model by Yuang Chen, Yong Li, Shaohua Li, Shuhan Lv, Fang Lin

“…By introducing the Dual Cascade Temporal Shift and Fusion module, the model further enhances the feature correlation ability in the time dimension and solves the problem of information sparsity caused by multiple temporal shifts. …”
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A Dataset of Raman and Infrared Spectra as an Extension to the ChEMBL by Jiechun Liang, Jack Ling, Limin Xu, Xi Zhu

“…However, plenty of spectra still missing in current datasets. To solve this problem, we applied Gaussian09 to construct a Raman spectrum and IR spectral dataset. …”
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VROHI: Visibility Recovery for Outdoor Hazy Image in Scattering Media by Mingye Ju, Can Ding, Y. Jay Guo

“…In this paper, according to the low-frequency feature (LFC) of haze, AHM is modified via gamma correction technique to make it suitable for modeling outdoor images. …”
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Planck-Scale Dual-Curvature Lensing and Spacetime Noncommutativity by Giovanni Amelino-Camelia, Leonardo Barcaroli, Stefano Bianco, Laura Pensato

“…It was recently realized that Planck-scale momentum-space curvature, which is expected in some approaches to the quantum-gravity problem, can produce dual-curvature lensing, a feature which mainly affects the direction of observation of particles emitted by very distant sources. …”
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Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks by Muawia A. Elsadig, Abdelrahman Altigani, Yasir Mohamed, Abdul Hakim Mohamed, Akbar Kannan, Mohamed Bashir, Mousab A. E. Adiel

“…In other words, two layers of enhancements were applied—using a suitable feature selection technique and fixing the dataset imbalance problem. …”
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Point Cloud Geometry Scalable Coding Using a Resolution and Quality-Conditioned Latents Probability Estimator by Daniele Mari, Andre F. R. Guarda, Nuno M. M. Rodrigues, Simone Milani, Fernando Pereira

“…While scalable coding is a well-known and addressed feature in conventional image and video codecs, this paper focuses on a new and very different problem, notably the development of scalable coding solutions for deep learning-based Point Cloud (PC) coding. …”
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Shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi-objective optimization by Wenli Liu, Yang Chen, Tianxiang Liu, Wen Liu, Jue Li, Yangyang Chen

“…Adequate control of shield machine parameters to ensure the safety and efficiency of shield construction is a difficult and complex problem. To address this problem, this paper proposes a hybrid intelligent optimization framework that combines interpretable machine learning, intelligent optimization algorithms, and multi-objective optimization and decision-making methods. …”
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FsDAOD: Few-shot domain adaptation object detection for heterogeneous SAR image by Siyuan Zhao, Yong Kang, Hang Yuan, Guan Wang, Hui Wang, Shichao Xiong, Ying Luo

“…In which the small sample of data scarcity is becoming an urgent problem for researchers. Therefore, this paper proposes a novel few-shot domain adaptation object detection (FsDAOD) method based on Faster Region Convolutional Neural Network baseline to cope with the above problem. …”
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Spatiotemporal Forecasting of Traffic Flow Using Wavelet-Based Temporal Attention by Yash Jakhmola, Madhurima Panja, Nitish Kumar Mishra, Kripabandhu Ghosh, Uttam Kumar, Tanujit Chakraborty

“…This paper proposes a wavelet-based temporal attention model, namely a wavelet dynamic spatiotemporal aware graph neural network (W-DSTAGNN), for tackling the traffic forecasting problem. A key feature of W-DSTAGNN is the use of wavelets that can effectively separate noise and capture multi-resolution temporal patterns. …”
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Real-Time Evaluation of Ground Insulation Degradation and Fault Warning Method Under Multiple Operating Conditions for Train Traction Drive System by Zhenglin Cheng, Kan Liu, Xueming Li, Shaolong Xu, Zhiwen Chen, Fengbing Jiang

“…Secondly, based on engineering application experience, a feature index set that can accurately classify different types of grounding faults is extracted. …”
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GS-GVINS: A Tightly-Integrated GNSS-Visual-Inertial Navigation System Augmented by 3D Gaussian Splatting by Zelin Zhou, Shichuang Nie, Saurav Uprety, Hongzhou Yang

“…However, most integrated systems rely on feature-tracking based visual odometry, which suffers from the problem of feature sparsity, high dynamics, significant illumination changes, etc. …”
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Building electrical consumption patterns forecasting based on a novel hybrid deep learning model by Nasser Shahsavari-Pour, Azim Heydari, Farshid Keynia, Afef Fekih, Aylar Shahsavari-Pour

“…This paper addresses the problem of accurate energy forecasting by proposing an intelligent hybrid model that integrates advanced feature selection, signal decomposition, and deep learning techniques. …”
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Optimizing Chlorophyll-a Concentration Inversion in Coastal Waters Using SVD and Deep Learning Approach by Lili Zhan, Yongxin Xu, Jinshan Zhu, Zhangshuo Liu

“…The purpose of this research is to address the problem of high-precision retrieval of the Chl-a concentration in small coastal waters. …”
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Interturn short circuit fault diagnosis method of direct-drive permanent magnet synchronous motor based on two-stage PNN by GAO Caixia, FENG Lingyun, DANG Jianjun

“…Then， in order to eliminate the influence of fault degree on the characteristic signals and enhance the fault characteristics， the extracted BDC amplitude was normalized by L2 norm to obtain the fault branch location feature vector. The extracted BRC amplitude was normalized by Z-Score to obtain the short-circuit coil positioning feature vector. …”
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Similarity-Based Clustering for Identification and Segmentation of Responsive Electricity Customers by Amirhossein Ahmadi, Hamidreza Zareipour, Henry Leung

“…The identification and segmentation of responsive electricity customers have been formulated here as a binary time series clustering (TSC) problem. The assumption of a stationary environment in kernel methods can complicate the mapping of non-stationary time series data to a high-dimensional feature space, leading to a degradation in the performance of kernel K-means clustering. …”
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