Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy by Haofang Zhang, Changbao Xu, Chenge Hu, Yunlai Xue, Daoke Yao, Yifan Hu, Ankang Wu, Miao Dai, Hang Ye

“…Our study aimed to construct a machine learning algorithm predictive model to predict the risk of fungal infection following F-URL. …”
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Integration of deep learning and railway big data for environmental risk prediction models and analysis of their limitations by Liuhui Quan, Minjie Wang, Lyu Baihang, Zhang Ziwen, Zhang Ziwen, Zhang Ziwen

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Prediction of sleep disorders using Novel decision support neutrosophic based machine learning models by Nihar Ranjan Panda, Surapati Paramanik, Prasanta Kumar Raut, Ruchi Bhuyan

“…This study introduces a novel decision support system utilizing a neutrosophic machine learning prediction model to enhance the accuracy and reliability of sleep disorder diagnosis. …”
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A predictive analytics approach with Bayesian-optimized gentle boosting ensemble models for diabetes diagnosis by Behnaz Motamedi, Balázs Villányi

“…Effective disease management necessitates the accurate and timely prediction of lung cancer and diabetes. Machine learning (ML) based models have garnered attention in the realm of predictive healthcare, with ensemble methods, in particular, bolstering algorithms to improve classification performance. …”
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Evaluating the accuracy of models using routinely collected herd data for prediction of on-farm lameness prevalence by Conor Barry, Esben Østergaard Eriksen, Kristian Ellingsen-Dalskau, Christoph Winckler, Nicholas J. Bell, Camilla Kielland

“…The accuracy of both models was assessed in terms of their concordance r and prediction errors. …”
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Machine Learning and Multilayer Perceptron-Based Customized Predictive Models for Individual Processes in Food Factories by Byunghyun Lim, Dongju Kim, Woojin Cho, Jae-Hoi Gu

“…The results of this study demonstrate that straightforward data and models can accurately predict steam energy consumption for individual processes. …”
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Improving soil pH prediction and mapping using anthropogenic variables and machine learning models by Daocheng Li, Erlong Xiao, Yingxin Xia, Xingyu Liang, Mengxin Guo, Lixin Ning, Jun Yan

“…This study evaluates the impact of anthropogenic activities on soil pH prediction in China's Huang-Huai-Hai Plain using four machine learning models (RF, LightGBM, XGBoost, SVM). …”
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Optimizing in-hospital mortality predictive models in ACS patients: QTc prolongation and machine learning approaches by Hamrish Kumar Rajakumar

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Comparative analysis of control methods with predictive models of six-phase permanent magnet synchronous motor by A. A. R. Rahim, S. N. Kladiev, S. Saeidi

Subjects: “…model predictive control…”
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Comparison of deep learning models for predictive maintenance in industrial manufacturing systems using sensor data by Wenjun Li, Ting Li

“…Abstract This paper presents a comprehensive comparison of deep learning models for predictive maintenance (PdM) in industrial manufacturing systems using sensor data. …”
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Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction by Chenxi Wang, Weiwei Zhang, Ming Ni, Qiong Wang, Chang Liu, Linbin Dai, Mengguo Zhang, Yong Shen, Feng Gao

“…Dementia related brain regions, such as the medial temporal lobe, were identified by our model. Finally, amyloid plaque prediction model was trained to predict amyloid plaque, and achieved an AUC about 0.8 for dementia patients. …”
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Early molecular changes predict cancer cachexia in LKB1‐deleted mouse models of NSCLC by Gloriana Ndembe, Andrea David Re Cecconi, Federica Palo, Dorina Belotti, Laura Sala, Selena Foroni, Eugenio Scanziani, Rosanna Piccirillo, Massimo Broggini, Mirko Marabese

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Prediction of the strength characteristics of basalt fibre reinforced concrete using explainable machine learning models by B. Y. Wickramasuriya, Yasitha Alahakoon, B. R. G. A. Krishantha, Janaka Alawatugoda, I. U. Ekanayake

“…This study applies machine learning (ML) models, paired with explainable artificial intelligence (XAI), to predict the compressive, flexural, and tensile strengths of BFRC efficiently and transparently. …”
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Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models by Andre R. Kuroswiski, Annie S. Wu, Angelo Passaro

“…This study investigates high-performance models for predicting the Weapon Engagement Zone (WEZ) in beyond-visual-range (BVR) air combat scenarios. …”
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A comparative analysis of deep learning models for accurate spatio-temporal soil moisture prediction by Litao Zhu, Wen Dai, Jiru Huang, Zicong Luo

“…All models demonstrated increased prediction errors as SM values exceed 75%, suggesting limitations in the models’ ability to predict extreme events. …”
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Optimizing pore pressure prediction in earth dams through the integration of panel data and intelligent models by Behrang Beiranvand, Taher Rajaee, Mehdi Komasi

“…This study presents an innovative spatiotemporal modeling approach that leverages data from properly functioning instruments to reconstruct missing measurements through panel data analysis. …”
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Frailty risk prediction models for patients undergoing maintenance hemodialysis in China: a systematic review by Yu Zhong, Songmei Cao, Liangying Chen, Teng Li, Wei Ye

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On the Use of Predictive Models for Improving the Quality of Industrial Maintenance: An Analytical Literature Review of Maintenance Strategies by Oana Merkt

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Predictive models based on machine learning to analyze the adoption of digital payments in Latin America and the Caribbean by Jiang Wagner Mamani Lopez, Antonio Víctor Morales Gonzales, Pedro Pablo Chambi Condori

“…This study aims to develop machine learning models to predict the adoption of digital payments in Latin America and the Caribbean using statistical data from the World Bank's Global Findex Database for 2021. …”
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Trajectory prediction of human-driven vehicles on the basis of risk field theory and interaction multiple models by Zhaojie Wang, Guangquan Lu, Jinghua Wang, Haitian Tan, Renjing Tang

Subjects: “…trajectory prediction…”
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