Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models by Sherif S. M. Ghoneim, Mohammed Baz, Ali Alzaed, Yohannes Tesfaye Zewdie

“…The collected data from these tests were used to supply XGBoost/LightGBM to build artificial intelligence model to predict the insulating paper state. The results indicated that the great ability of the proposed model to predict the insulating state with high accuracy. …”
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Enhancing diabetes risk prediction through focal active learning and machine learning models by Wangyouchen Zhang, Zhenhua Xia, Guoqing Cai, Junhao Wang, Xutao Dong

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Comparative evaluation of hybrid and individual models for predicting soybean yellow mosaic virus incidence by Yunish Khan, Vinod Kumar, Amel Gacem, Anurag Satpathi, Parul Setiya, Kumari Surbhi, Ajeet Singh Nain, Dinesh Kumar Vishwakarma, Ahmad J. Obaidullah, Krishna Kumar Yadav, Ozgur Kisi

“…These findings highlight the superior efficiency of hybrid models in predicting soybean disease severity based on weather indices in the study region.…”
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Advancing the accuracy of clathrin protein prediction through multi-source protein language models by Watshara Shoombuatong, Nalini Schaduangrat, Pakpoom Mookdarsanit, Jaru Nikom, Lawankorn Mookdarsanit

“…These models were used to encode complementary feature embeddings, capturing diverse and valuable information. …”
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Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Models by Yogesh Gupta, Ghanshyam Raghuwanshi, Abdullah Ali H. Ahmadini, Utkarsh Sharma, Amit Kumar Mishra, Wali Khan Mashwani, Pinar Goktas, Shokrya S. Alshqaq, Oluwafemi Samson Balogun

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Epigenetic age acceleration and rheumatoid arthritis: an NHANES-based analysis and survival prediction models by Yuhang Ou, Zhihao Wang, Yunbo Yuan, Yuze He, Wenhao Li, Hao Ren, Junhong Li, Siliang Chen, Yanhui Liu

“…Conclusion Epigenetic aging may play a harmfully promotive role in the onset and progression of RA, and the GrimAge2Accel-based prediction models could effectively predict the survival of RA patients. …”
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Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease by Wei Zheng, Xiaoyan Qin, Ronghua Mu, Peng Yang, Bingqin Huang, Bingqin Huang, Zhixuan Song, Xiqi Zhu, Xiqi Zhu

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A Detailed Review for Predicting the Quantity of Sugar From Sugarcane Using Various Models by Kathirvel Narayanasamy, Ilayaraja Venkatachalam

“…This review aims to analyze various aspects of sugar production, including sugar prediction, processing techniques, and sugarcane quality parameters, and focuses on the use of sugarcane juice parameters to construct predictive models. …”
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Sensor-Based Bermudagrass Yield Prediction Models Using Random Forest Algorithm in Oklahoma by Gabriel Camargo de Campos Jezus, Lucas Freires Abreu, Daryl Brian Arnall, Lucas Martins Stolerman, Alexandre Caldeira Rocateli

“…Pers.] biomass prediction models using the Random Forest regressor with laser, ultrasonic, multispectral sensors, precipitation, and N fertilization as input features. …”
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Comprehensive Evaluation of Bankruptcy Prediction in Taiwanese Firms Using Multiple Machine Learning Models by Hung V. Pham, Tuan Chu, Tuan M. Le, Hieu M. Tran, Huong T.K. Tran, Khanh N. Yen, Son V. T. Dao

“…The results suggest that the predictive performance of bankruptcy models can be significantly enhanced by integrating multiple analytical methodologies.  …”
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Crop Classification and Yield Prediction Using Robust Machine Learning Models for Agricultural Sustainability by Abid Badshah, Basem Yousef Alkazemi, Fakhrud Din, Kamal Z. Zamli, Muhammad Haris

“…Machine learning, a subset of Artificial Intelligence (AI), enables prediction, classification, and automation in agriculture. …”
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Interpretable Machine Learning Models for Predicting Cesarean Delivery in Class III Obese Cohorts by Rachel Bennett, Stephanie L. Pierce, Talayeh Razzaghi

“…Our comparative analysis shows logistic regression to be the most accurate in predicting the need for cesareans in the nulliparous cohort, while random forest outperformed other models in the combined dataset.…”
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Predicting Mesothelioma Using Artificial Intelligence: A Scoping Review of Common Models and Applications by Malihe Ram MS, Mohammad Reza Afrash PhD, Khadijeh Moulaei PhD, Erfan Esmaeeli, Mohadeseh Sadat Khorashadizadeh, Ali Garavand PhD, Parastoo Amiri PhD, Azam Sabahi PhD

“…Conclusion Artificial intelligence, particularly machine learning models such as neural networks, decision trees, support vector machines, and random forests, holds promise in predicting and managing mesothelioma, potentially enhancing early detection and improving patient outcomes.…”
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Comparison of Prediction Models for Sonic Boom Ground Signatures Under Realistic Flight Conditions by Jacob Jäschke, Samuele Graziani, Francesco Petrosino, Antimo Glorioso, Volker Gollnick

“…This paper presents a comparative analysis of simplified and high-fidelity sonic boom prediction methods to assess their applicability in the conceptual design of supersonic aircraft. …”
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Hybrid neural network models for time series disease prediction confronted by spatiotemporal dependencies by Hamed Bin Furkan, Nabila Ayman, Md. Jamal Uddin

“…The models' predictions were compared using MAPE, and RMSE, as well as graphical representations generated by employed models. …”
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Alzheimer’s Disease Prediction Using Fisher Mantis Optimization and Hybrid Deep Learning Models by Sameer Abbas, Mustafa Yeniad, Javad Rahebi

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Ensemble deep learning models for tropical cyclone intensity prediction using heterogeneous datasets by Dikshant Gupta, Menaka Pushpa Arthur

Subjects: “…Ensemble model…”
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Normative isokinetic knee strength values and prediction models in non-athletic Chinese adults by Kun Liu, Lulu Yin, Ye Zhang, Gongliang Liu, Ping Fang, Yanhong Ma, Lihua Huang

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Comparing Models and Performance Metrics for Lung Cancer Prediction using Machine Learning Approaches. by Ruqiya, Noman Khan, Saira Khan

“…It optimizes the performance of models for predicting lung cancer. …”
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The application of artificial intelligence models in predicting the risk of diabetic foot: a multicenter study by Yao Li, Siyuan Zhou, Bichen Ren, Shuai Ju, Xiaoyan Li, Wenqiang Li, Bingzhe Li, Yunmin Cai, Chunlei Chang, Lihong Huang, Zhihui Dong

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