PoE-Enabled Visible Light Positioning Network With Low Bandwidth Requirement and High Precision Pulse Reconstruction by Zhenghai Wang, Xuan Huang, Xuanbang Chen, Mengzhen Xu, Xiaodong Liu, Yuhao Wang, Xun Zhang

“…It achieves a positioning accuracy of 1.7 cm by using the reconstructed 2 GHz sampling rate in the case of a bandwidth of 50 MHz and a real sampling rate of 100 MHz. …”
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Quality Indicators of Agaricus bisporus after Ultraviolet Treatment by Vladimir V. Kondratenko, Natalia E. Posokina, Natalia I. Fedyanina, Olga V. Karastoyanova, Nadezhda V. Korovkina

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Validación del inventario de autoeficacia para inteligencias múltiples revisado (IAMI-R) en una muestra de estudiantes universitaria / Validation of Self-efficacy Inventory for Mul... by Emilse Durán-Aponte, María Antonieta Elvira-Valdés, Lydia Pujol

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Average high school teacher salaries vs student performance on standardized tests: A correlational analysis of northern New Jersey high schools by Sousa, J.

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Comparison of Matrix Decomposition in Null Space-Based LDA Method by Carissa Devina Usman, Farikhin, Titi Udjiani

“…Problems with small sample sizes and high dimensionality are common in pattern recognition. …”
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An integrated approach to uncertainty and global sensitivity analysis in penstock structural modeling by Manal Haddouch, Imane Hajjout, El Mostapha Boudi

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Adaptive Focal Loss for Keypoint-Based Deep Learning Detectors Addressing Class Imbalance by Zhihao Su, Afzan Adam, Mohammad Faidzul Nasrudin

“…Popular solutions such as hard sampling, soft sampling, and sampling-free methods have been proposed to tackle this issue. …”
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A Comparative Study of Conventional Pap Smear and Liquid‐Based Cytology by Kldiashvili Ekaterina, Khuntsaria Irakli, Kekelia Elene, Mamiseishvili Ana, Abuladze Mariam

“…ABSTRACT Background Cervical cancer is a major health issue globally, particularly in developing countries where it remains a leading cause of cancer‐related deaths among women. …”
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Points2Model: a neural-guided 3D building wireframe reconstruction from airborne LiDAR point clouds by Perpetual Hope Akwensi, Akshay Bharadwaj, Ruisheng Wang

“…To create accurate building wireframe models effectively in the face of these issues, we propose explicitly learning to fill in the areas of occlusion in the APC and implicitly learning to enhance the point resolution via up-sampling for effective primitive extraction. …”
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Class-Balanced Random Patch Training to Address Class Imbalance in Tiling-Based Farmland Classification by Yeongung Bae, Yuseok Ban

“…Additionally, farmland classification frequently exhibits class imbalance due to uneven cultivation areas, resulting in biased training toward majority classes and poorer performance on minority classes. To overcome these issues, we propose Class-Balanced Random Patch Training, which combines Random Patch Extraction (RPE) and Class-Balanced Sampling (CBS). …”
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Time Series Data Generation Method with High Reliability Based on ACGAN by Fang Liu, Yuxin Li, Yuanfang Zheng

“…In the process of big data processing, especially in fields like industrial fault diagnosis, there is often the issue of small sample sizes. The data generation method based on Generative Adversarial Networks(GANs) is an effective way to solve this problem. …”
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Development and application of a pressure-preserving sampler for long boreholes in coal mines by WANG Mian, HE Wei, WANG Hongtao, ZHAO Yongchao, YU Hong

“…In underground coal mine long borehole sampling, significant challenges include substantial gas loss, complex borehole conditions, and low coal sample drilling rates. …”
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A systematic review on AI based class imbalance handling in software defect prediction by Somya R. Goyal

“…A huge range of diversified AI based mechanisms are available to handle the class imbalance issue underlying the defect datasets that would hinder the performance of prediction models.This study assesses the timeline of evolution of AI applications in the domain of software defect prediction and culminates a Systematic Review over the period of 25 years from 2000 to 2025 dedicatedly focusing on class imbalance issue. …”
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A self-growth convolution network for thermal and mechanical fault detection with very limited engine data by Gou Xin, Zhu Xiaolong, Wang Xinwei, Wang Hui, Zhang Junhong, Lin Jiewei

“…The self-growth scheme is proposed to disrupt the coadaptation between layers and that between kernels in order to mitigate the overfitting issue of small-sample cases. The SGNet is verified and implemented in the PHM of a heavy-duty diesel engine. …”
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Hydrops fetalis with septated cystic hygroma by Reddy Ravikanth, Rajalakshmy Prasannan

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Three novel cost-sensitive machine learning models for urban growth modelling by Mohammad Ahmadlou, Mohammad Karimi, Saad Sh. Sammen, Karam Alsafadi

“…This article addresses the class imbalance problem in urban gain modelling (UGM) of Tabriz and Isfahan megacities in Iran by proposing novel cost-sensitive machine learning models, namely cost-sensitive support vector machine (CSVM), random forest (CRF) and artificial neural network (CANN). Random sampling, a frequently utilized method, fails to effectively tackle this issue by biasing models towards no change samples, which outnumber change samples. …”
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Mapping Grayscale Images to Colour Space Using Deep Learning by Anu Saini, Jyoti Tripathi

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The Point Cloud Reduction Algorithm Based on the Feature Extraction of a Neighborhood Normal Vector and Fuzzy-c Means Clustering by Hongxiao Xu, Donglai Jiao, Wenmei Li

“…Non-feature points are then sampled using an enhanced farthest point sampling technique. …”
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Stress-Strength Reliability of Two-Parameter Exponential Distribution Based on Progres- sively Type-II Censored Data by Sajad Rostamian

“…A set of real data is presented for better clarification of the issue. Conclusion: The results demonstrated that with increasing the sample size, in almost cases the estimated risk of all the estimators decrease. …”
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Few-Shot Learning in Wi-Fi-Based Indoor Positioning by Feng Xie, Soi Hoi Lam, Ming Xie, Cheng Wang

“…The results indicated that the base CNN model achieved varying accuracy levels depending on the scenario and the number of samples per class retained after FCS. Meta-learning performed acceptably in scenarios with fewer samples, which are the distinct datasets pertaining to novel classes. …”
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