Threshold-based exploitation of noisy label in black-box unsupervised domain adaptation. by Huiwen Xu, Jaeri Lee, U Kang

“…We utilize a flexible thresholding approach to adjust the threshold for each class, thereby obtaining an adequate amount of clean data for hard-to-learn classes. …”
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High-speed programming with threshold division for RRAM-based neural network accelerators by Xiangyu Du, Taiping Chen, Man Su, Zhen Li, Peiwen Tong, Wei Wang, Rongrong Cao

“…The relationship between threshold conductance and programming error is systematically investigated by allowing a larger programming error for cells below the threshold. …”
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Multistage Threshold Segmentation Method Based on Improved Electric Eel Foraging Optimization by Yunlong Hu, Liangkuan Zhu, Hongyang Zhao

“…Multi-threshold segmentation of color images is a critical component of modern image processing. …”
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The Cybersemiotic Model of Communication: An Evolutionary View on the Threshold between Semiosis and Informational Exchange by Søren Brier

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Cadmium accumulation in wheat grain: Accumulation models and soil thresholds for safe production by Lu Lin, Xiaopeng Zhao, Yumeng Li, Jingbo Ling, Jinghua Ren, Qilin Liao, Dongmei Zhou, Xueyuan Gu

“…Furthermore, the Extreme Random Tree model (RMSE ​= ​0.221, MAE ​= ​0.165) outperformed the other seven machine learning algorithms. The thresholds for both soil total Cd and bioavailable Cd for safe wheat production were further back-calculated according to the permissible value of Cd in wheat grain, which demonstrated enhanced protection accuracy compared to the current soil quality standard. …”
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Fast Digital Circuit Synthesis Framework Based on Deep Reinforcement Learning Using Scoreboard by Hyeonseong Cho, Yeonhak Pyo, Tae Woong Lee, Jiyoung Lee, Hyunyoung Choi, Hyung Uk Cho, Hyoungsik Nam

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FraudGNN-RL: A Graph Neural Network With Reinforcement Learning for Adaptive Financial Fraud Detection by Yiwen Cui, Xu Han, Jiaying Chen, Xinguang Zhang, Jingyun Yang, Xuguang Zhang

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Unveiling the Spatial Heterogeneity of Urban Vitality Using Machine Learning Methods: A Case Study of Tianjin, China by Fengshuo Sun, Enxu Wang

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A Method for Identifying Landslide-Prone Areas Using Multiple Factors and Adaptive Probability Thresholds: A Case Study in Northern Tongren, Longwu River Basin, Qinghai Province by Jiawen Bao, Xiaojun Luo, Yueling Shi, Mingyue Hou, Jichao Lv, Guoxiang Liu

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AMCL: supervised contrastive learning with hard sample mining for multi-functional therapeutic peptide prediction by Jiwei Fang, Henghui Fan, Jintao Zhao, Jianping Zhao, Junfeng Xia

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An automated hybrid deep learning framework for paddy leaf disease identification and classification by Chatla Subbarayudu, Mohan Kubendiran

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Prediction of Drought Thresholds Triggering Winter Wheat Yield Losses in the Future Based on the CNN-LSTM Model and Copula Theory: A Case Study of Henan Province by Jianqin Ma, Yan Zhao, Bifeng Cui, Lei Liu, Yu Ding, Yijian Chen, Xinxi Zhang

Subjects: “…CNN-LSTM deep learning model…”
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A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing by Chin-Yi Lin, Tzu-Liang (Bill) Tseng, Tsung-Han Tsai

Subjects: “…active learning…”
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A lung nodule segmentation model based on the transformer with multiple thresholds and coordinate attention by Tianjiao Hu, Yihua Lan, Yingqi Zhang, Jiashu Xu, Shuai Li, Chih-Cheng Hung

“…With the rapid development of deep learning, lung nodule segmentation models based on the encoder-decoder structure have become the mainstream research approach. …”
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Optimal thresholds and key parameters for predicting influenza A virus transmission events in ferrets by Troy J. Kieran, Xiangjie Sun, Taronna R. Maines, Jessica A. Belser

“…Interestingly, viruses with ‘intermediate’ transmission outcomes (33–66%) had NW titers and derived quantities more similar to non-transmissible viruses (<33%) in a DCT setting, but with efficiently transmissible viruses (>67%) in a RDT setting. Machine learning was employed to further assess the predictive role of summary measures and varied interpretation of intermediate transmission outcomes in both DCT and RDT models, with models employing these different thresholds yielding high performance metrics against both internal and external datasets. …”
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A Joint Detection and Tracking Paradigm Based on Reinforcement Learning for Compact HFSWR by Xiaotong Li, Weifeng Sun, Yonggang Ji, Weimin Huang

Subjects: “…Adaptive detection threshold adjustment…”
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Machine Learning-Based Interpretable Screening for Osteoporosis in Tuberculosis Spondylitis Patients Using Blood Test Data: Development and External Validation of a Novel Web-Based... by Yasin P, Ding L, Mamat M, Guo W, Song X

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Multi-Threshold Remote Sensing Image Segmentation Based on Improved Black-Winged Kite Algorithm by Yi Zhang, Xinyu Liu, Wei Sun, Tianshu You, Xin Qi

“…This paper proposes an adaptive multi-threshold image segmentation method named IBKA-OTSU to address the limitations of existing deep learning-based image segmentation methods, particularly their heavy reliance on large-scale annotated datasets and high computational complexity. …”
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Consequences of ignoring dominance genetic effects from genomic selection model for discrete threshold traits by Farhad Ghafouri-Kesbi

“…Results showed that when the genetic architecture of the traits is purely additive, or when dominance effects are present but ignored from the genomic evaluation process, GBLUP, BayesB, and ridge regression BLUP-method 6 (rrBLUPm6) had better predictive performance, and therefore recommended for genomic evaluation of discrete threshold traits. Machine learning methods, in particular regression tree, had poor predictive performance and were not recommended for genomic selection. …”
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A multi-strategy improved crow search algorithm for multi-level thresholding image segmentation by Xiaoping Zhang, Chengliang Huang, Weixia Gui

“…In the second set of experiments, Otsu’s method and fuzzy entropy are employed as objective functions for performing multilevel threshold segmentation on twelve grayscale images. …”
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