Deep learning-based framework for Mycobacterium tuberculosis bacterial growth detection for antimicrobial susceptibility testing by Hoang-Anh T. Vo, Sang Nguyen, Ai-Quynh T. Tran, Han Nguyen, Hai Bich Ho, Philip W. Fowler, Timothy M. Walker, Thuy Thi Nguyen

“…TMAS is designed to measure Minimum Inhibitory Concentrations (MICs) rapidly and accurately while differentiating between true bacterial growth and artefacts. …”
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Network characteristics of comorbid symptoms in alcohol use disorder by Xin Yu, Wen Zhang, Can Wang, Guolin Mi, Xiuzhe Chen, Yanhu Wang, Xu Chen

“…The bnlearn package in R was used to construct directed acyclic graph (DAG) for individuals with AUD using the Bayesian hill-climbing algorithm.Results In the partial correlation network, among the three major comorbidity categories, ‘anxiety/depression’ was most strongly associated with ‘sleep quality’. …”
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Unpacking the dangers of super absorbent polymer water beads: an in vitro analysis by Jack J. Hachem, Jack J. Hachem, Javier Monagas, Javier Monagas, Ankona Banerjee, Robert A. Noel, Robert A. Noel

“…Our findings underscore the need for standardized management of SAP bead ingestion, which could improve patient outcomes while reducing unnecessary diagnostic and therapeutic interventions. …”
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Machine Learning and Deep Learning for Crop Disease Diagnosis: Performance Analysis and Review by Habiba Njeri Ngugi, Andronicus A. Akinyelu, Absalom E. Ezugwu

“…The review method includes an in-depth analysis of algorithm performance using key metrics such as accuracy, precision, recall, and F1 score across various datasets. …”
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Validity of the International Classification of Diseases 10th revision code for hospitalisation with hyponatraemia in elderly patients by Amit X Garg, Salimah Z Shariff, Sonja Gandhi, Jamie L Fleet, Matthew A Weir, Arsh K Jain

“…In the two settings, the positive predictive values were 96.4% (95% CI 94.6% to 97.6%) and 82.3% (95% CI 80.0% to 84.4%), while the negative predictive values were 89.2% (95% CI 89.0% to 89.5%) and 87.1% (95% CI 86.8% to 87.4%). …”
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Study protocol for a stepped-wedge, randomized controlled trial to evaluate implementation of a suicide risk identification model among behavioral health patients in three large he... by Scott P. Stumbo, Stephanie A. Hooker, Rebecca C. Rossom, Kathleen Miley, Brian K. Ahmedani, Elizabeth Lockhart, Hsueh-Han Yeh, Bobbi Jo H. Yarborough

“…Abstract Background Age-adjusted suicide rates have increased in the U.S. over the past 25 years. Algorithm-based methods for identifying individuals at risk for suicide based on electronic health record and claims data have been validated but few studies have evaluated implementation or effects on population-level suicide attempt rates. …”
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Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion by Xiaoshuo Cui, Xiaoxue Sun, Shuxin Xuan, Jinyu Liu, Dongfang Zhang, Jun Zhang, Xiaofei Fan, Xuesong Suo

“…Savitzky–Golay (SG) convolution smoothing and standard normal variate (SNV) and normalization (Norm) preprocessing methods were employed to preprocess the original spectral data and textural features, while a successive projection algorithm (SPA) was used to extract relevant feature bands. …”
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Field-independent carbon mapping and quantification in forest plantation through remote sensing by Saverio Francini, Elia Vangi, Giovanni D’Amico, Costanza Borghi, Guido Cencini, Cecilia Monari, Catherine Zolli, Gherardo Chirici

“…Accordingly, the RMSE obtained using the FI method was 34% while that obtained by the MA method – exploiting random forest algorithm – was greater (RMSE = 39%). …”
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The association between the albumin to globulin ratio and thoracic spine bone mineral density in adolescents: NHANES 2011–2016 by Haoyu Yan, Jie Yang, Yancheng Dai, Yan Li, Peng Zhang, Liuyan Li

“…BackgroundNumerous studies have demonstrated a strong correlation between osteoporosis and nutritional status and inflammation, while albumin and globulin are important references for nutritional status and inflammation, respectively. …”
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Analysis of the Impact of Rain on Perception in Automated Vehicle Applications by Tim Brophy, Darragh Mullins, Ashkan Parsi, Jonathan Horgan, Enda Ward, Patrick Denny, Ciaran Eising, Brian Deegan, Martin Glavin, Edward Jones

“…The reliable performance of object detection perception algorithms in automated vehicles under adverse conditions such as rain is critical for maintaining vulnerable road user safety. …”
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Pre-transplant IE1-specific T-cell response and CD8+ T-cell count as predictive markers of treated HCMV reactivation in kidney transplant recipients by Federica Zavaglio, Irene Cassanti, Irene Cassanti, Marilena Gregorini, Marilena Gregorini, Maria Antonietta Grignano, Teresa Rampino, Daniele Lilleri, Fausto Baldanti, Fausto Baldanti

“…Potential cut-off and diagnostics algorithm should be better investigated in a large patients setting.…”
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A deep learning approach for SMAP soil moisture downscaling informed by thermal inertia theory by Mengyuan Xu, Haoxuan Yang, Annan Hu, Lee Heng, Linyi Li, Ning Yao, Gang Liu

“…In this study, we attempt to use an SM downscaling approach that integrates thermal inertia (TI) theory with the DenseNet deep network algorithm. This approach provides partial interpretability of the physical mechanisms while utilizing DenseNet’s superior nonlinear learning ability and feature reuse capability for downscaling the Soil Moisture Active Passive (SMAP) satellite product, generating daily 1 km × 1 km SM. …”
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Uncertainty‐Aware Machine Learning Bias Correction and Filtering for OCO‐2: 1 by Steffen Mauceri, William Keely, Josh Laughner, Christopher W. O’Dell, Steven Massie, Robert Nelson, David Baker, Matthäus Kiel, Otto Lamminpää, Jonathan Hobbs, Abhishek Chatterjee, Tommy Taylor, Paul Wennberg, Sean Crowell, Britton Stephens, Vivienne H. Payne

“…Using a physics‐based retrieval algorithm, these measurements are inverted to estimate column‐averaged atmospheric carbon dioxide dry‐air mole fractions (XCO2). …”
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A Detection and Cover Integrated Waveform Design Method with Good Correlation Characteristics and Doppler Tolerance by Haoting Guo, Fulai Wang, Nanjun Li, Zezhou Wu, Chen Pang, Lei Zhang, Yongzhen Li

“…The gradient descent algorithm is used to solve the model, and the optimized waveform with low sidelobe characteristics and multiple false target interference abilities is obtained. …”
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Flow topology and thermal mechanism in turbulent channel flow with tapered V-shaped baffles by Somchai Sripattanapipat, Naman Jain, Suvanjan Bhattacharyya, Varesa Chuwattanakul, Paisarn Naphon, Smith Eiamsa-ard

“…The simulations are performed using the finite volume method in conjunction with the SIMPLE algorithm, and the Generalized k-ω (GEKO) turbulence model is employed to capture the flow dynamics. …”
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A multicenter bladder cancer MRI dataset and baseline evaluation of federated learning in clinical application by Kangyang Cao, Yujian Zou, Chang Zhang, Weijing Zhang, Jie Zhang, Guojie Wang, Chu Zhang, Jiegeng Lyu, Yue Sun, Hongyuan Zhang, Bin Huang, Lei Deng, Shuiqing Yang, Jianpeng Li, Bingsheng Huang

“…Given this concern, protecting patient privacy during machine learning algorithm training is a crucial aspect that requires substantial attention. …”
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Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review by Oussama Khouili, Mohamed Hanine, Mohamed Louzazni, Miguel Angel López Flores, Eduardo García Villena, Imran Ashraf

“…Furthermore, Wavelet Transform (WT) was found to be the most prominent data decomposition technique, while Pearson Correlation was the most used for feature selection. …”
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Integration of machine learning and bulk sequencing revealed exosome-related gene FOSB was involved in the progression of abdominal aortic aneurysm by Xianlu Ma, Xianlu Ma, Hongjie Zhou, Hongjie Zhou, Ren Wang, Ren Wang

“…Additionally, FOSB overexpression inhibited the expression of contractile phenotype markers α-SMA and SM22α, while promoting the expression of synthetic phenotype marker OPN.ConclusionExosome-related gene FOSB was involved in the progression of abdominal aortic aneurysm. …”
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Electrocardiographic, echocardiographic and lipid parameters in predicting obstructive coronary artery disease in patients with non-ST elevation acute coronary syndrome by M. M. Tsivanyuk, B. I. Geltser, K. I. Shakhgeldyan, E. D. Emtseva, G. S. Zavalin, O. I. Shekunova

“…The quality metrics of the best predictive algorithm based on MLR were 0,81 for AUC, 0,74 for Sp and Ac, and 0,75 for Se. …”
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Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia by Mahwish Ilyas, Muhammad Bilal, Nadia Malik, Hikmat Ullah Khan, Muhammad Ramzan, Anam Naz

“…While our model is built on a typical CNN architecture, we make significant advances by using preprocessing techniques and hyperparameter tuning. …”
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