A Deep Learning-Based Algorithm for Ceramic Product Defect Detection by Junxiang Diao, Hua Wei, Yawei Zhou, Zhihua Diao

“…To address these challenges, this study proposes a deep learning-based algorithm for ceramic product defect detection. …”
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Learning curve for the open Latarjet procedure: a single-surgeon study by Aidan G. Papalia, Paul V. Romeo, Matthew G. Alben, Andrew Cecora, Dashaun Ragland, Mandeep S. Virk

“…This study aims to determine the learning curve for the open LP (oLP) and the threshold for proficiency. …”
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Application of deep learning in wound size measurement using fingernail as the reference by Dun-Hao Chang, Duc-Khanh Nguyen, Thi-Ngoc Nguyen, Chien-Lung Chan

“…Considering a more human-centered design, we propose an automatic wound size measurement system by combining three deep learning (DL) models and using fingernails as a reference. …”
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Joint Intensity and Spatio-Temporal Representation Learning for Extreme Precipitation Nowcasting by Zefeng Pan, Renlong Hang, Qingshan Liu, Chunxiang Shi, Zhiqiang Xu, Xiao-Tong Yuan

“…To address this issue, we design an Intensity Trend Module (ITM) to explicitly learn and model the intensity features of extreme precipitation events. …”
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Social work and anti-oppressive approach: educational practices and housing interventions with Roma and Sinti in Venice by Claudia Mantovan

“…This reconstruction leads to the argument that the educators employed in this and in other low-threshold services of the lagoon municipality adopt a modus operandi in tune with what Scarscelli (2022), based on the theoretical perspective of critical social work, defines as an anti-oppressive approach in social work. …”
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SpecBoost: Accelerating Tiled Sparse Matrix Multiplication via Dataflow Speculation by Gwanghwi Seo, Sungju Ryu

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Quantifying ruminal health: A statistical review and application of area and time under the curve in animal science by Luis O. Tedeschi

“…By combining these methods with advancements in data processing, such as machine learning and predictive modeling, the potential for broader applications becomes evident. …”
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Advancing Biomedical Engineering With Artificial Intelligence and Machine Learning: A Systematic Review by Abebe Belay Adege

“…The inclusion of artificial intelligence (AI) and machine learning (ML) in biomedical engineering opens new frontiers of innovation and better decision-making and allows the art to cross new thresholds in healthcare technologies. …”
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Data-driven network intrusion detection using optimized machine learning algorithms by Dauda Adeite Adenusi, Oladosu Oyebisi Oladimeji, Theopilus Adekunle Oyekola, Korede Solomon Olagunju

“…The proposed approach demonstrates perfect Area Under the Curve (AUC) scores of 1.00 for tree-based methods, indicating robust detection capabilities across varying decision thresholds. This research contributes to the field by establishing that simpler machine learning models can achieve state-of-the-art performance in network intrusion detection, offering practical implications for real-world deployment in network security operations.…”
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Optimizing Sensitivity in Machine Learning Models for Pediatric Post-operative Kyphosis Prediction by Raja Ayu Mahessya, Dian Eka Putra, Rostam Ahmad Efendi, Rayendra, Rozi Meri, Riyan Ikhbal Salam, Dedi Mardianto, Ikhsan, Ismael, Arif Rizki Marsa

“…This study developed and evaluated machine learning models for kyphosis prediction using a dataset of 81 pediatric patients by comparing the logistic regression and decision tree approaches. …”
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Deep learning-based research on fault warning for marine dual fuel engines by Lingkai Meng, Huibing Gan, Haisheng Liu, Daoyi Lu

“…Additionally, residual value distribution curves and statistical process control methods were employed to set alarm thresholds for residuals. A sliding window approach was used to establish the alarm threshold for residual standard deviation, with an upper boundary of the residual threshold set at 0.15 and a lower boundary at -0.1. …”
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Deep Learning-Based Differentiation of Vertebral Body Lesions on Magnetic Resonance Imaging by Hüseyin Er, Murat Tören, Berkutay Asan, Esat Kaba, Mehmet Beyazal

“…<b>Conclusions:</b> The YOLO-v8 deep learning model we used demonstrates effective performance in distinguishing vertebral body metastases from different groups of benign pathologies.…”
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Performance of machine learning algorithms to evaluate the physico-mechanical properties of nanoparticle panels by Derrick Mirindi, James Hunter, David Sinkhonde, Tajebe Bezabih, Frederic Mirindi

“…DT algorithms demonstrated high predictive accuracy (R2=0.92for WA-TS, 0.96 for MOR-MOE, and 0.80 for IB-MOE), identifying critical thresholds: WA below 29.73% corresponded to minimal TS (9.94%), MOR above 38.18 MPa led to MOE above 3598.86 MPa, and IB above 0.88 MPa corresponds to MOE greater than 2,747.99 MPa. …”
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Risk assessment of oral leukoplakia by DNA content enhanced by machine learning models by Matheus Henrique Lopes Dominguete, Vera Cavalcanti de Araújo, Fernanda Viviane Mariano, Carmen Silvia Passos Lima, João Figueira Scarini, Paulo de Camargo Moraes, Victor Angelo Martins Montalli, Larissa Agatti, Zuraiza Mohamad Zaini, Luíza Hellmeister, Marcelo Sperandio

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Improved detection of air trapping on expiratory computed tomography using deep learning. by Sundaresh Ram, Benjamin A Hoff, Alexander J Bell, Stefanie Galban, Aleksa B Fortuna, Oliver Weinheimer, Mark O Wielpütz, Terry E Robinson, Beverley Newman, Dharshan Vummidi, Aamer Chughtai, Ella A Kazerooni, Timothy D Johnson, MeiLan K Han, Charles R Hatt, Craig J Galban

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Analysis for sparse channel representation based on dictionary learning in massive MIMO systems by Qing‐Yang Guan

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Multi-perspective dynamic consistency learning for semi-supervised medical image segmentation by Yongfa Zhu, Xue Wang, Taihui Liu, Yongkang Fu

“…Abstract Semi-supervised learning (SSL) is an effective method for medical image segmentation as it alleviates the dependence on clinical pixel-level annotations. …”
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Constraint-aware wind power forecasting with an optimized hybrid machine learning model by Md. Omer Faruque, Md. Alamgir Hossain, S.M. Mahfuz Alam, Muhammad Khalid

“…Furthermore, the results indicate that the GAMO optimization framework facilitated smoother convergence in the model learning process.…”
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An ensemble learning model to predict lymph node metastasis in early gastric cancer by Kaiqing Song, Jiaming Wu, Muchen Xu, Mengying Li, Yuqi Chen, Yi Zhang, Hong Chen, Caifeng Jiang

“…Based on the F1 score, the model’s threshold was set at 0.18. Additionally, the model demonstrated an AUC of 0.892 in the external validation set, along with an accuracy of 78.30% and an F1 score of 0.60.We constructed an ensemble learning model for predicting lymph node metastasis of early gastric cancer. …”
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Development of Deep Learning Models for Real-Time Thoracic Ultrasound Image Interpretation by Austin J. Ruiz, Sofia I. Hernández Torres, Eric J. Snider

“…With the recent growth in artificial intelligence (AI) for healthcare, the hypothesis for this study is that deep learning (DL) models for classifying images as showing HTX or PTX injury, or being negative for injury can be developed for lowering the skill threshold for POCUS diagnostics on the future battlefield. …”
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