Winter Wheat Nitrogen Content Prediction and Transferability of Models Based on UAV Image Features by Jing Zhang, Gong Cheng, Shaohui Huang, Junfang Yang, Yunma Yang, Suli Xing, Jingxia Wang, Huimin Yang, Haoliang Nie, Wenfang Yang, Kang Yu, Liangliang Jia

“…This study aims to present an innovative approach by integrating 40 texture features and 22 spectral features from UAV multispectral images with machine learning (ML) methods (RF, SVR, and XGBoost) for winter wheat nitrogen content prediction. In addition, through analysis of an 8-year long-term field experiment with rigorous data, the results indicated that (1) the RF and XGboost models incorporating both spectral and texture features achieved good prediction accuracy, with R² values of 0.98 and 0.99, respectively, RMSE values of 0.10 and 0.07, and MAE values of 0.07and 0.05; (2) models trained on Farmers’ Practice (FP) data showed superior transferability to Ecological Intensification (EI) conditions (R² = 0.98, RMSE = 0.08, and MAE = 0.05 for XGBoost), while EI-trained models performed less well when applied to FP conditions (R² = 0.89, RMSE = 0.45, and MAE = 0.35 for XGBoost). …”
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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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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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Development and validation of web-based, interpretable predictive models for sepsis and mortality in extensive burns by Shi-Qi Wang, Shi-Qi Wang, Kan Qiu, Qi-Rui Zheng, Bing-Jie Zhou, Ming-Yu Li, Hai-Yan Zhong, Yong Chen, Si-Ming Yuan, Si-Ming Yuan

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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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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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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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A systematic review of dengue outbreak prediction models: Current scenario and future directions. by Xing Yu Leung, Rakibul M Islam, Mohammadmehdi Adhami, Dragan Ilic, Lara McDonald, Shanika Palawaththa, Basia Diug, Saif U Munshi, Md Nazmul Karim

“…The reporting of methodology and model performance measures were inadequate in many of the existing prediction models. …”
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Enhanced Performance of Wastewater Membrane Bioreactor Using Machine Learning Model’s Prediction and Optimization by Jia-Chi Jiang, Nien-Hsun Li, Mo Chen, Kuan-Chung Chen, Chih-Hua Chang

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Prediction of virus-host associations using protein language models and multiple instance learning. by Dan Liu, Francesca Young, Kieran D Lamb, David L Robertson, Ke Yuan

“…It also identifies important viral proteins that significantly contribute to host prediction. The method combines a pre-trained large protein language model (ESM) and attention-based multiple instance learning to allow protein-orientated predictions. …”
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Dust impact on photovoltaic modules: Global data, predictive models, emphasis on chemical composition by Hussam Almukhtar, Tek Tjing Lie, Wisam Al-Shohani

“…Incorporating 690 global datasets and leveraging Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) in MATLAB, the study integrates key dust chemical components (Si, Fe, Ca, Al) and weight to predict the PV optical properties. This approach enhances models’ predictive accuracy across diverse environmental settings, which in turn enables more accurate forecasting of PV power output and thermal behavior under varying dust conditions, as these optical properties govern the module equations. …”
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