Phonological awareness and sinusoidal amplitude modulation in phonological dislexia by Yolanda Peñaloza-López, Aline Herrera-Rangel, Santiago J. Pérez-Ruiz, Adrián Poblano

“…ABSTRACT Objective Dyslexia is the difficulty of children in learning to read and write as results of neurological deficiencies. …”
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China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022 by R. Shang, R. Shang, X. Lin, J. M. Chen, J. M. Chen, Y. Liang, K. Fang, M. Xu, Y. Yan, W. Ju, G. Yu, N. He, L. Xu, L. Liu, J. Li, W. Li, J. Zhai, Z. Hu

“…This study aims to generate China's annual forest age dataset (CAFA V2.0) at a 30 m resolution from 1986 to 2022, utilizing forest disturbance monitoring and machine learning techniques. Forest disturbance monitoring, which typically has lower uncertainty compared to machine learning approaches, is primarily employed to update annual forest age. …”
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Wood Species Classification in Open Set Using an Improved NNO Classifier by Ke-Xin Zhang, Peng Zhao

“…The spectral dimension reduction was performed with a Metric Learning (ML) algorithm. Two improvements were proposed in the following NNO classifier. …”
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Feature ranking and network analysis of global financial indices. by Mahmudul Islam Rakib, Md Javed Hossain, Ashadun Nobi

“…The feature ranking method of machine learning is applied to investigate the feature ranking and network properties of 21 world stock indices. …”
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Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models by Sami Naouali, Oussama El Othmani

“…This study introduces a novel framework leveraging Rough Set Theory (RST)-based feature selection—MLReduct, MLSpecialReduct, and MLFuzzyRoughSet—to enhance machine learning performance on uncertain data. Applied to a private cardiovascular dataset, our MLSpecialReduct algorithm achieves a peak Random Forest accuracy of 0.99 (versus 0.85 without feature selection), while MLFuzzyRoughSet improves accuracy to 0.83, surpassing our MLVarianceThreshold (0.72–0.77), an adaptation of the traditional VarianceThreshold method. …”
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A Dual-Level Intelligent Architecture-Based Method for Coupling Fault Diagnosis of Temperature Sensors in Traction Converters by Yunxiao Fu, Qiuyang Zhou, Haichuan Tang

“…This method achieves online sensor fault isolation and early equipment anomaly warning by leveraging spatiotemporal correlation modeling of multimodal sensor data and ensemble learning-based prediction. At the first level, it integrates multi-source parameters such as outlet temperature and pressure to establish dynamic prediction models, which are combined with adaptive threshold mechanisms for detecting various sensor faults including offset, open-circuit, and noise interference. …”
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Reducing the Parameter Dependency of Phase-Picking Neural Networks with Dice Loss by Yongsoo Park, Gregory C. Beroza

“…When strategically used, models trained on the Dice loss can reduce the parameter dependency of machine learning-based seismic monitoring.…”
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Assessing the Effectiveness of DL-Clustering for Energy Optimization in Wireless Sensor Networks by Shailaja S. Halli, Poornima G Patil

“…Mobile sinks and neighboring nodes are alerted if the fitness is low, which also predicts energy efficient traffic and creates an implicit alarm threshold. To address multi-energy efficient traffic situations, a novel LAFLC algorithm is used, which uses Learning Automata with Water wave game theory to learn the nature of the energy efficient traffic. …”
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Parallel synapses with transmission nonlinearities enhance neuronal classification capacity. by Yuru Song, Marcus K Benna

“…Nevertheless, successful learning in the model neuron often requires only a small number of parallel synapses. …”
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Expression Recognition Using Improved AlexNet Network in Robot Intelligent Interactive System by Yifeng Zhao, Deyun Chen

“…Finally, the Focal Loss is improved by setting the probability threshold to avoid the impact of mislabeling samples on the classification performance of the model. …”
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Distrust Spillover in Sharing Accommodation: Evidence From Airbnb in Beijing by Xin Jin, Bo Wang, Ning Ma

“…Beijing Airbnb listings data were collected and analyzed using sentiment analysis, machine learning, and econometric statistics in English and Chinese languages. …”
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Detection of Release Fabric Defects in Fiber-Reinforced Composites Using Through-Transmission Ultrasound by Gary LeMay, Enkhsaikhan Boldsaikhan

“…Future research will aim to investigate additional physical factors and deep learning approaches to further advance the TTU inspection method.…”
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Ablating adult neurogenesis in the rat has no effect on spatial processing: evidence from a novel pharmacogenetic model. by James O Groves, Isla Leslie, Guo-Jen Huang, Stephen B McHugh, Amy Taylor, Richard Mott, Marcus Munafò, David M Bannerman, Jonathan Flint

“…An effect on contextual freezing was significant at a threshold of 5% (P = 0.04), but not at a threshold corrected for multiple testing. …”
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Prediction of High-ozone Events Using GAM, SMOTE, and Tail Dependence Approaches in Texas (2005–2019) by Benjamin Brown-Steiner, Xiong Zhou, Matthew J. Alvarado, Brook T. Russell

“…We also find that the tail dependence approach is capable of predicting extreme ozone events, but algorithmic stability and configuration complexity can make this approach difficult to operationalize on a broad scale and that the selection of the threshold needs to be carefully considered. Finally, the feature selection via the tail dependence method performs comparably to other forms of machine learning-based feature selection and we find that there are multiple parameter sets that can predict MDA8 O3 with equal success.…”
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Socratic wisdom in the age of AI: a comparative study of ChatGPT and human tutors in enhancing critical thinking skills by Hoda Fakour, Moslem Imani

“…Findings underscore the importance of developing hybrid educational models that leverage both the strengths of human facilitators and the efficiencies of AI tools to enhance student learning and critical thinking skills.…”
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Influences of pulsed electric field parameters on cell electroporation and electrofusion events: Comprehensive understanding by experiments and molecular dynamics simulations. by Sujun Qu, Qiang Ke, Xinhao Li, Lin Yu, Shuheng Huang

“…Experimental results and machine learning-based regression analysis showed that the number of pores is mainly determined by pulse strength, while the sizes of pores were enlarged by increasing the pulse widths. …”
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A Cloud Computing-Based Intelligent Forecasting Method for Cross-Border E-Commerce Logistics Costs by Yanting Li

“…Normalize the input data samples of the input layer, and select the initial weight, threshold, and learning rate parameters of the BP neural network to determine the momentum coefficient. …”
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Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies by Abishek Sankaranarayanan, Georgii Khachaturov, Kimberly S. Smythe, Shachi Mittal

“…Here, we benchmark and compare the nuclear segmentation tools commonly used in multiplexed immunofluorescence data by evaluating their performance across 7 tissue types encompassing ~20,000 labeled nuclei from human tissue samples. Pre-trained deep learning models outperform classical nuclear segmentation algorithms. …”
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An Elastic Fine-Tuning Dual Recurrent Framework for Non-Rigid Point Cloud Registration by Munan Yuan, Xiru Li, Haibao Tan

“…Many advanced non-rigid alignment models are implemented using supervised learning; however, the large number of labels required for the training process makes their application difficult. …”
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Automated Earthwork Detection Using Topological Persistence by Dana A. Lapides, Gillian Grindstaff, Mary H. Nichols

“…Various methods for detecting topographic features exist in the literature, including a set of rule and threshold‐based techniques and machine learning methods. …”
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