Construction and validation of HBV-ACLF bacterial infection diagnosis model based on machine learning by Neng Wang, Shuai Tao, Liang Chen

“…Abstract Objective To develop and validate a novel diagnostic model for detecting bacterial infections in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) using advanced machine learning algorithms. …”
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Prediction rotary drilling penetration rate in lateritic soils using machine learning models by Eugène Gatchouessi Kamdem, Franck Ferry Kamgue Tiam, Luc Leroy Mambou Ngueyep, Olivier Wounabaissa, Hugues Richard Lembo Nnomo, Abraham Kanmogne

“…The present paper investigated an accurate machine learning model for the penetration rates (ROP) prediction in lateritic soil covers layers. …”
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Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning by Ranran Kou, Cong Wang, Jinxia Liu, Ran Wan, Zhe Jin, Le Zhao, Youjie Liu, Junwei Guo, Feng Li, Hongbo Wang, Song Yang, Cong Nie

“…Particle swarm optimization (PSO) was used to optimize parameters of each model. …”
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Automatic Generation Technology of Safety Measures for Digital Substation Based on Improved Support Vector Machine by Yabing YAN, Xu CHU, Haolong XIAO, Wenwu LIANG, Hui LI, Zhenxing XIA

“…Firstly, construct a secondary circuit model and equipment model based on adjacency matrix, and further integrate the secondary security measure rule library to form a sample dataset; Secondly, support vector machines were used to classify secondary security measures, and bacterial foraging algorithms were introduced to optimize penalty factors and kernel parameters, effectively improving the training effectiveness of the automatic generation model for security measures; Finally, the effectiveness of the proposed method was verified through numerical examples.…”
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Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques by Abbas Jafar, Myungho Lee

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Estimating Canopy Chlorophyll Content of Potato Using Machine Learning and Remote Sensing by Xiaofei Yang, Hao Zhou, Qiao Li, Xueliang Fu, Honghui Li

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Multimodal machine learning-based model for differentiating nontuberculous mycobacteria from mycobacterium tuberculosis by Hong-ling Li, Ri-zeng Zhi, Hua-sheng Liu, Mei Wang, Si-jie Yu

“…The multimodal model contained age, IL-6, and the 2 radiomics features, and the optimal model was from LightGBM algorithm. …”
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Enhanced Prediction and Uncertainty Modeling of Pavement Roughness Using Machine Learning and Conformal Prediction by Sadegh Ghavami, Hamed Naseri, Farzad Safi Jahanshahi

“…Gray relational analysis was performed to identify the optimal uncertainty model. The results showed that Minmax/80 was the optimal uncertainty model for IRI prediction, with an effective coverage of 93.4% and an average interval width of 0.256 m/km. …”
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A robust and statistical analyzed predictive model for drug toxicity using machine learning by Deepak Rawat, Rohit Bajaj, Rachit Manchanda, Ankush Mehta, Prabhu Paramasivam, Suraj Kumar Bhagat, Abinet Gosaye Ayanie

“…An optimized ensembled model is used to contrast the results of seven machine learning algorithms and three deep learning models with regard to state-of-the-art parameters. …”
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A Comprehensive Study on the Estimation of Concrete Compressive Strength Using Machine Learning Models by Yusuf Tahir Altuncı

“…To this end, this study aims to conduct a scientometric analysis of contributions that utilize machine learning (ML) models for predicting concrete compressive strength, assess these models, and provide insights for developing optimal solutions. …”
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A Lightweight Machine Learning Model for High Precision Gastrointestinal Stromal Tumors Identification by Xin Sun, Xiwen Mo, Jing Shi, Xinran Zhou, Yanqing Niu, Xiao-Dong Zhang, Man Li, Yonghui Li

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Building a machine learning-based risk prediction model for second-trimester miscarriage by Sangsang Qi, Shi Zheng, Mengdan Lu, Aner Chen, Yanbo Chen, Xianhu Fu

“…Through this rigorous assessment, the optimal model was selected. Shapley additive explanations (SHAP) were generated to provide insights into the model’s predictions, and a visual representation of the predictive model was built. …”
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Choice of machine learning models for predicting the development of psychological disorders in people with hypothireosis and hyperthireosis by Нурал Гулієв

“…Machine learning methods that are widespread in the medical field were analyzed and one of them was chosen that more optimally solves all the tasks of the task. …”
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Predictive modeling of oil rate for wells under gas lift using machine learning by Famin Ma, Farag M. A. Altalbawy, Pinank Patel, R. Manjunatha, Rishiv Kalia, Shoira Formanova, P. Raja Naveen, Kamal Kant Joshi, Aashna Sinha, Abdolali Yarahmadi Kandahari, Taqi Mohammed Khattab Al-Rubaye, Mohammad Mahtab Alam

“…This study aimed to develop robust predictive models for estimating oil production rates using a comprehensive dataset from oil fields in south-eastern Iraq, leveraging advanced machine learning techniques. …”
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Development and validation of machine learning models for MASLD: based on multiple potential screening indicators by Hao Chen, Jingjing Zhang, Xueqin Chen, Ling Luo, Wenjiao Dong, Yongjie Wang, Jiyu Zhou, Canjin Chen, Wenhao Wang, Wenbin Zhang, Zhiyi Zhang, Yongguang Cai, Danli Kong, Yuanlin Ding

“…This study aimed to utilize multifaceted indicators to construct MASLD risk prediction machine learning models and explore the core factors within these models.MethodsMASLD risk prediction models were constructed based on seven machine learning algorithms using all variables, insulin-related variables, demographic characteristics variables, and other indicators, respectively. …”
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Physics-informed transformation toward improving the machine-learned NLTE models of ICF simulations by Min Sang Cho, Paul E. Grabowski, Kowshik Thopalli, Thathachar S. Jayram, Michael J. Barrow, Jayaraman J. Thiagarajan, Rushil Anirudh, Hai P. Le, Howard A. Scott, Joshua B. Kallman, Branson C. Stephens, Mark E. Foord, Jim A. Gaffney, Peer-Timo Bremer

“…However, determining how to optimize machine-learning-based NLTE models in order to match ICF simulation dynamics remains challenging, underscoring the need for physically relevant error metrics and strategies to enhance model accuracy with respect to these metrics. …”
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Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms by Juchao Chen, Zicheng Lei, Zongcai Duan, Zhili Wen

“…The optimal model was screened on the basis of the area under the curve (AUC), calibration curves and confusion matrix to assess and compare the predictive performance of the models, the model was interpreted through SHAP plots, and a web-based version of the risk assessment tool for spontaneous rupture and bleeding in hepatocellular carcinoma patients was developed on the basis of the optimal machine learning predictive model. …”
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Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement by Pradnyawant M. Gote, Praveen Kumar, Hemant Kumar, Prateek Verma, Moses Makuei Jiet

“…This study introduces a novel hybrid machine learning model aimed at enhancing early detection accuracy and improving patient outcomes. …”
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Comparing Models and Performance Metrics for Lung Cancer Prediction using Machine Learning Approaches. by Ruqiya, Noman Khan, Saira Khan

“…This enhancement shows that tuning hyperparameters is effective. It optimizes the performance of models for predicting lung cancer. …”
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Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning Models by Chaohui Zhang, Peng Liu, Tiantian Song, Bin He, Wei Li, Yuansheng Peng

“…In this study, 10 different machine learning models were evaluated for their capacity to predict the elastic modulus of UHPC. …”
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