A localization strategy combined with transfer learning for image annotation. by Zhiqiang Chen, Leelavathi Rajamanickam, Jianfang Cao, Aidi Zhao, Xiaohui Hu

“…We propose a transfer learning model called CNN-2L that incorporates the label localization strategy described in this study. …”
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Modeling student satisfaction in online learning using random forest by Jinlei Li, Xiaowei Chen

“…Partial dependence plots revealed threshold and saturation effects, highlighting complex nonlinear patterns missed by traditional linear models. …”
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Semisupervised Learning for Detecting Inverse Compton Emission in Galaxy Clusters by Sheng-Chieh Lin, Yuanyuan Su, Fabio Gastaldello, Nathan Jacobs

“…We present a semisupervised deep-learning approach to search for IC emission in galaxy clusters. …”
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Multi-contrast machine learning improves schistosomiasis diagnostic performance. by María Díaz de León Derby, Charles B Delahunt, Ethan Spencer, Jean T Coulibaly, Kigbafori D Silué, Isaac I Bogoch, Anne-Laure Le Ny, Daniel A Fletcher

“…Here we present a machine learning (ML)-based strategy for automated detection of S. haematobium that combines two imaging contrasts, brightfield (BF) and darkfield (DF), to improve diagnostic performance. …”
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A Deep Learning Approach to Measure Visual Function in Zebrafish by Manjiri Patil, Annabel Birchall, Hammad Syed, Vanessa Rodwell, Ha-Jun Yoon, William H. J. Norton, Mervyn G. Thomas

“…Here, we present a novel deep learning pipeline for OKR analysis, using ResNet-50 within the DeepLabCut framework in a Python Version 3.10 environment. …”
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Few-Shot Learning With Prototypical Networks for Improved Memory Forensics by Muhammad Fahad Malik, Ammara Gul, Ayesha Saadia, Faeiz M. Alserhani

“…Prototypical networks are a type of few-shot learning algorithm that excels at classifying new categories with minimal examples. …”
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Robust hierarchical federated learning with dual-layer filtering mechanism by Chaoyi Yang, Wei Liang, Yuxiang Chen, Jiahong Xiao, Dacheng He, Tianxiong Liu, Jin Wang, Amr Tolba

“…Hierarchical federated learning (HFL) is an effective “cloud-edge-device” distributed model training framework that protects data privacy. …”
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Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food by Zhenlong Wang, Wei An, Jiaxue Wang, Hui Tao, Xiumin Wang, Bing Han, Jinquan Wang

“…These results suggest that the combination of E-nose technology and machine learning provides a rapid, cost-effective approach for screening ZEN contamination in pet food at the market entry stage.…”
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imageseg: An R package for deep learning‐based image segmentation by Jürgen Niedballa, Jan Axtner, Timm Fabian Döbert, Andrew Tilker, An Nguyen, Seth T. Wong, Christian Fiderer, Marco Heurich, Andreas Wilting

“…Abstract Convolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications, and are particularly suited for image data. …”
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Machine learning analysis of cardiovascular risk factors and their associations with hearing loss by Ali Nabavi, Farimah Safari, Ali Faramarzi, Mohammad Kashkooli, Meskerem Aleka Kebede, Tesfamariam Aklilu, Leo Anthony Celi

“…The National Health and Nutrition Examination Survey (NHANES) 2012–2018 data comprising audiometric tests and cardiovascular risk factors was utilized. Machine learning algorithms were trained to classify hearing impairment thresholds and predict pure tone average values. …”
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Early Warning for Stepwise Landslides Based on Traffic Light System: A Case Study in China by Shuangshuang Wu, Zhigang Tao, Li Zhang, Song Chen

“…Furthermore, leveraging the C5.0 machine learning algorithm, a comparison between the predictive capabilities of the TLS model and a pure rate threshold model reveals that the TLS model achieves a 93% accuracy rate, outperforming the latter by 7 percentage points. …”
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A general approach for determining applicability domain of machine learning models by Lane E. Schultz, Yiqi Wang, Ryan Jacobs, Dane Morgan

“…Automated tools are provided to enable researchers to establish acceptable dissimilarity thresholds to identify whether new predictions of their own machine learning models are in-domain versus out-of-domain.…”
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Deep learning-based prediction of multi-level just noticeable distortion by Haifeng XU, Hongkui WANG, Haibing YIN, Chuqiao CHEN

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Detection and location of EEG events using deep learning visual inspection. by Mohammad Amin Fraiwan

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Integer ambiguity validation through machine learning for precise point positioning by Jiang Guo, Jianghui Geng

“…We aim at improving the reliability of ambiguity validation by integrating these tests using a machine learning model called the Support Vector Model (SVM). …”
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An optimize canny algorithm with traditional machine learning for edge detection enhancement by Russel Lafta, Zainab Sultani

“…Also, a new approach for estimating Canny algorithm thresholds has been developed using the Flower Pollination algorithm. …”
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Uncertainty aware domain incremental learning for cross domain depression detection by Zita Lifelo, Jianguo Ding, Huansheng Ning, Sahraoui Dhelim

“…To overcome these challenges, we propose an Uncertainty-Aware Domain Incremental Learning framework for Cross-Domain Depression Detection (UDIL-DD), integrating Uncertainty-guided Adaptive Class Threshold Learning (UACTL) and Data-Free Domain Alignment (DFDA). …”
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Machine learning for predicting Chagas disease infection in rural areas of Brazil. by Fabio De Rose Ghilardi, Gabriel Silva, Thallyta Maria Vieira, Ariela Mota, Ana Luiza Bierrenbach, Renata Fiuza Damasceno, Lea Campos de Oliveira, Alexandre Dias Porto Chiavegatto Filho, Ester Sabino

“…We simulated the decision boundary using various thresholds and observed that in this dataset a threshold of 0.45 resulted in a 100% recall. …”
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Communicating about overdiagnosis: Learning from community focus groups on osteoporosis. by Ray Moynihan, Rebecca Sims, Jolyn Hersch, Rae Thomas, Paul Glasziou, Kirsten McCaffery

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Physically Based Dimensionless Features for Pluvial Flood Mapping With Machine Learning by Mark S. Bartlett, Jared VanBlitterswyk, Martha Farella, Jinshu Li, Curtis Smith, Anthony J. Parolari, Lalitha Krishnamoorthy, Assaad Mrad

“…Abstract Rapid delineation of flash flood extents is critical to mobilize emergency resources and to manage evacuations, thereby saving lives and property. Machine learning (ML) provides a promising solution for this rapid delineation, offering a computationally efficient alternative to high‐resolution 2D flood models. …”
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