Machine learning to improve HIV screening using routine data in Kenya by Jonathan D. Friedman, Jonathan M. Mwangi, Kennedy J. Muthoka, Benedette A. Otieno, Jacob O. Odhiambo, Frederick O. Miruka, Lilly M. Nyagah, Pascal M. Mwele, Edmon O. Obat, Gonza O. Omoro, Margaret M. Ndisha, Davies O. Kimanga

“…We trained four machine learning algorithms including logistic regression, Random Forest, AdaBoost and XGBoost. …”
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Clinician Attitudes and Perceptions of Point-of-Care Information Resources and Their Integration Into Electronic Health Records: Qualitative Interview Study by Marlika Marceau, Sevan Dulgarian, Jacob Cambre, Pamela M Garabedian, Mary G Amato, Diane L Seger, Lynn A Volk, Gretchen Purcell Jackson, David W Bates, Ronen Rozenblum, Ania Syrowatka

“…Some recommended that further integration would allow us to leverage existing POCI tool features, such as chatbots and knowledge links, as well as aspects of artificial intelligence and machine learning, such as predictive algorithms and personalized alert systems, to enhance EHR functionality. …”
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Deciphering the impact of intra-tumoral bacterial infiltration on multi-omics profiles in low-grade gliomas by Wenshu Li, Zixiang Zhu, Zixiang Zhu, Longyuan Li, Xin Wu, Jiaxuan Li, Yi Zhou, Lingwen Gu, Pranathi Vittal, Zhouqing Chen, Zhong Wang, Lingchuan Guo

“…For the TCGA samples, utilizing advanced machine learning algorithms, this study identified distinct patterns of bacterial infiltration within the LGG population and constructed a prognostically relevant intra-tumoral bacteria risk model (PRIBR Index). …”
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Associations of greenhouse gases, air pollutants and dynamics of scrub typhus incidence in China: a nationwide time-series study by Haoyue Cao, Jianqiang Han, Weiming Hou, Juxiang Yuan

“…Second, four statistical methods were used to assess the correlation and lag effects across different age groups and epidemic periods. Deep learning algorithms were employed to evaluate the predictive effect of environmental factors on the incidence of scrub typhus. …”
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A machine learning-based screening model for the early detection of prostate cancer developed using serum microRNA data from a mixed cohort of 8,741 participants by Cong Lai, Zhensheng Hu, Zhuohang Li, Zhikai Wu, Kuiqing Li, Lin Li, Hongze Liu, Juanyi Shi, Yi Zhou, Kewei Xu, Cheng Liu

“…Six machine learning algorithms were employed to develop a screening model for PCa using the training dataset. …”
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Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence by Xiaoyin Wu, Xiaoyin Wu, Buyu Guo, Buyu Guo, Xingyu Chang, Xingyu Chang, Yuxuan Yang, Yuxuan Yang, Qianqian Liu, Qianqian Liu, Jiahui Liu, Jiahui Liu, Yichen Yang, Yichen Yang, Kang Zhang, Yumei Ma, Songbo Fu, Songbo Fu, Songbo Fu

“…The current lack of reliable predictive biomarkers hinders early diagnosis and personalized therapy.MethodsTranscriptomic profiles of DN samples and controls were extracted from GEO databases. …”
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EUS-based intratumoral and peritumoral machine learning radiomics analysis for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer by Shuangyang Mo, Shuangyang Mo, Nan Yi, Fengyan Qin, Huaying Zhao, Huaying Zhao, Yingwei Wang, Haiyan Qin, Haixiao Wei, Haixing Jiang, Shanyu Qin

“…Following this, the radiomic features underwent dimensionality reduction through the utilization of the least absolute shrinkage and selection operator (LASSO) algorithm. Six machine learning algorithms were utilized to train prediction models employing features with nonzero coefficients. …”
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Correlation Between Clinical Indicators and Liver Pathology in Children with Chronic Hepatitis B by Chenyang Huang, Ying Lu, Ziwei Wang, Qiyu Jiang, Yi Dong, Lili Cao, Jianguo Yan, Zhiqiang Xu, Fuchuan Wang, Yinjie Gao, Junliang Fu, Min Zhang, Fu-Sheng Wang

“…Our findings highlight the value of integrating age and key biochemical markers into non-invasive diagnostic algorithms for the early detection and management of liver pathology in children.…”
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Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.) by Jorge A. Fernandez-Jibaja, Nilton Atalaya-Marin, Yeltsin A. Álvarez-Robledo, Victor H. Taboada-Mitma, Juancarlos Cruz-Luis, Daniel Tineo, Malluri Goñas, Darwin Gómez-Fernández

“…Among the regression algorithms tested, support vector regression (SVR) demonstrated the highest predictive accuracy (R² = 0.81) for the Bellavista variety at the maximum tillering stage. …”
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An Ensemble Learning Approach for Drought Analysis and Forecasting in Central Bangladesh by Md. Alomgir Hossain, Momotaz Begum, Md. Nasim Akhtar, Md. Alamin Talukder, Nomanur Rahman, Mahfuzur Rahman

“…The study revealed that integrating ARIMA with ML algorithms improved forecasting accuracy, achieving over 92.0% accuracy in predicting SPI and SPEI, thereby significantly enhancing drought prediction capabilities.…”
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Developing an alternative data-driven model to resemble geomorphologic rainfall-runoff models by Pin-Chun Huang, Kwan Tun Lee

“…The topological distribution inherent in the input data space is analyzed to improve predictive accuracy. The proposed artificial intelligence (AI) model, which incorporates a classification algorithm for preprocessing input features prior to training a model based on the recurrent neural network, exhibits outstanding performance in runoff discharge prediction. …”
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Perbandingan Algoritme Machine Learning untuk Memprediksi Pengambil Matakuliah by Fitra A. Bachtiar, Indra K. Syahputra, Satrio A. Wicaksono

“…Thus, there is a need to build a system that can predict students taking courses. However, there are many algorithms that can be used for the prediction. …”
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Clustering-Based Thermography for Detecting Multiple Substances Under Large-Scale Floating Covers by Yue Ma, Benjamin Steven Vien, Thomas Kuen, Wing Kong Chiu

“…The method integrates infrared thermal imaging with a clustering algorithm to predict the distribution of various substrates beneath Traditional outdoor large-scale opaque geomembranes, using solar radiation as an excitation source. …”
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Artificial intelligence driven innovations in biochemistry: A review of emerging research frontiers by Mohammed Abdul Lateef Junaid

“…Key AI techniques—such as machine learning algorithms, natural language processing, and AI-based molecular modeling—are discussed. …”
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Improvement of an Edge-IoT Architecture Driven by Artificial Intelligence for Smart-Health Chronic Disease Management by William Alberto Cruz Castañeda, Pedro Bertemes Filho

“…The study also establishes a baseline of twelve regression algorithms to assess the non-invasive glucose prediction performance regarding the mean squared error, root mean squared error, and r-squared score, and the catboost regressor outperforms the other models with 218.91 and 782.30 in MSE, 14.80 and 27.97 in RMSE, and 0.81 and 0.31 in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>, respectively, on training and test sets. …”
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Rapid in-air ultrasound holography measurement and camera-in-the-loop generation using thermography by Zak Morgan, Youngjun Cho, Sriram Subramanian

“…Finally, we integrate this with holography algorithms to propose a camera-in-the-loop algorithm that employs real-time measurement, enabling targeted data acquisition and on-line training of acoustic holography algorithms. …”
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SOFTWARE IMPLEMENTATION OF THE ARTIFICIAL NEURAL NETWORK FOR VIRTUAL OBJECTS СONTROL by Yu. B. Popova, S. V. Yatsynovich

“…The use of an artificial neural network has reduced the use of CPU time, which is extremely important in problems where rapid decision making is required, because complex calculations and prediction algorithms can not always be invested in 20 ms, which is fraught with skipping moves and losses. …”
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Artificial intelligence in colorectal cancer: a review by G. Singh

“…Challenges in AI development are addressed, such as data standardization and the interpretability of machine learning algorithms. The potential of AI in treatment decision support, precision medicine, and prognosis prediction is discussed, emphasizing the role of AI in selecting optimal treatments and improving surgical precision. …”
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Heisenberg-Limited Adaptive Gradient Estimation for Multiple Observables by Kaito Wada, Naoki Yamamoto, Nobuyuki Yoshioka

“…The proposed method is an adaptive version of the quantum gradient-estimation algorithm and has a resource-efficient implementation due to its adaptiveness. …”
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Quality assessment of chicken using machine learning and electronic nose by Hassan Anwar, Talha Anwar

“…This study investigates the use of an electronic nose system—a sensor array that detects odors and generates data, which is then analyzed by machine learning algorithms to predict chicken freshness. An electronic nose system was developed using six MQ gas sensors and one humidity temperature sensor. …”
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