Control of Linear-Threshold Brain Networks via Reservoir Computing by Michael McCreesh, Jorge Cortes

“…Learning is a key function in the brain to be able to achieve the activity patterns required to perform various activities. …”
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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…Additionally, we use Otsu’s thresholding method to segment blood vessels accurately. …”
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Detecting Driver Drowsiness Using Hybrid Facial Features and Ensemble Learning by Changbiao Xu, Wenhao Huang, Jiao Liu, Lang Li

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Enhancing landslide dam stability prediction: a data-driven framework integrating missing data imputation and optimal threshold discrimination by Xiaojun Li, Xiaobo Zhang, Jun He, Yixiang Song, Yanqi Li

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Applying deep learning model to aerial image for landslide anomaly detection through optimizing process by Chwen-Huan Wang, Li Fang, Chiung-Yun Hu

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Sweet pepper foliar diseases quantification and identification using an image analysis tool by Vijayanandh Rajamanickam, Adesh Ramsubhag, Jayaraj Jayaraman

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Concept Thresholds: The Key to Self-efficacy and Effective Teaching in Higher Education by Bridget Percy

“…This article proposes that by using a framework of ‘threshold concepts’, the teacher is able to identify areas where they have transcended a threshold in their professional teacher development and, alternatively, areas where they have become ‘stuck’. …”
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An improved particle swarm optimization for multilevel thresholding medical image segmentation. by Jiaqi Ma, Jianmin Hu

“…Multilevel thresholding image segmentation is one of the widely used image segmentation methods, and it is also an important means of medical image preprocessing. …”
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Environmental thresholds triggering changes in above and belowground biomass carbon in China by Xin Zhang, Shihang Zhang, Jun Zhou, Jianrong Fan

“…However, the environmental thresholds that govern these dynamics under climate change remain poorly understood in China. …”
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Adaptive Warning Thresholds for Dam Safety: A KDE-Based Approach by Nathalia Silva-Cancino, Fernando Salazar, Joaquín Irazábal, Juan Mata

“…KDE is then used to estimate the density of historical data, allowing for dynamic calibration of warning thresholds. In regions of low data density—where prediction uncertainty is higher—the thresholds are widened to reduce false alarms, while in high-density regions, stricter thresholds are maintained to preserve sensitivity. …”
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A Texture-Enhanced Deep Learning Network for Cloud Detection of GaoFen/WFV by Integrating an Object-Oriented Dynamic Threshold Labeling Method and Texture-Feature-Enhanced Attention Module by Bo Zhong, Xiao Tang, Xiaobo Luo, Shanlong Wu, Kai Ao

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Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients☆ by Parisa Davar, Frédéric Godin, Jose Garrido

Subjects: “…Risk-aware reinforcement learning…”
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Automated post-run analysis of arrayed quantitative PCR amplification curves using machine learning [version 1; peer review: awaiting peer review] by David Garrett Brown, Darwin J. Operario, Lan Wang, Shanrui Wu, Daniel T. Leung, Eric R. Houpt, James A. Platts-Mills, Jie Liu, Ben J. Brintz

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Autoimmune gastritis detection from preprocessed endoscopy images using deep transfer learning and moth flame optimization by Fadiyah M. Almutairi, Sara A. Althubiti, Shabnam Mohamed Aslam, Habib Dhahri, Omar Alhajlah, Nitin Mittal

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Unsupervised Learning for Machinery Adaptive Fault Detection Using Wide-Deep Convolutional Autoencoder with Kernelized Attention Mechanism by Hao Yan, Xiangfeng Si, Jianqiang Liang, Jian Duan, Tielin Shi

Subjects: “…unsupervised feature learning…”
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Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems by Tingting Wu, Hongliang Song, Hongli Gao, Zongshen Wu, Feifei Han

“…Employing an algorithm for inferring dynamic relationships among multiple parameters of the lubrication system builds an adaptive threshold detection model. Extensive diesel engine tests and actual fault data demonstrate that the proposed method can address the issues of missed faults encountered by static threshold methods and the low detection accuracy of machine learning approaches without the need for fault samples. …”
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Investigating the Nonlinear Relationship Between the Built Environment and Urban Vitality Based on Multi-Source Data and Interpretable Machine Learning by Wenhao Liu, Zhen Yang, Chen Gui, Gen Li, Hongyi Xu

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Research on information extraction methods for historical classics under the threshold of digital humanities by Lifan HAN, Zijing JI, Zirui CHEN, Xin WANG

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Verifiable Threshold Multi-Party Fully Homomorphic Encryption from Share Resharing by Yuqi Xie, Ruwei Huang, Junbin Qiu

“…We construct a compact TMFHE scheme based on the Learning with Errors (LWE) problem. The scheme applies Shamir secret sharing and share resharing to support an arbitrary t-out-of-N threshold access structure, and enables non-interactive reconstruction of secret key shares using additive shares derived from the current set of online participants. …”
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TBKIN: Threshold-based explicit selection for enhanced cross-modal semantic alignments. by Zihan Guo, Xiang Shen, Chongqing Chen

“…To address this gap, we propose a novel vision-language model, the threshold-based knowledge integration network (TBKIN), designed to effectively capture intra-modal and cross-modal knowledge while systematically mitigating the impact of extraneous information. …”
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