Predicting <i>Cyperus esculentus</i> Biomass Using Tiller Number: A Comparative Analysis of Growth Models by Ya Ding, Yan Lu, Akash Tariq, Fanjiang Zeng, Yanju Gao, Jordi Sardans, Dhafer A. Al-Bakre, Josep Peñuelas

“…In conclusion, the tiller number is a reliable predictor for developing robust biomass models for <i>C. esculentus</i>. The Gompertz model is best for leaves, roots, and total biomass, while the logistic model is optimal for predicting tuber biomass in arid areas. …”
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A COMPARISON OF COX PROPORTIONAL HAZARD AND RANDOM SURVIVAL FOREST MODELS IN PREDICTING CHURN OF THE TELECOMMUNICATION INDUSTRY CUSTOMER by Sitti Nurhaliza, Kusman Sadik, Asep Saefuddin

“…So the prediction quality of the RSF model is better than the CPH model in predicting the churn of the telecommunications industry customer.…”
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Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis by Somayeh Farahani, Marjaneh Hejazi, Sahar Moradizeyveh, Antonio Di Ieva, Emad Fatemizadeh, Sidong Liu

“…This study aimed to assess the diagnostic accuracy of DL models in predicting various glioma molecular markers using MRI. …”
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Data driven models for predicting pH of CO2 in aqueous solutions: Implications for CO2 sequestration by Mohammad Rasool Dehghani, Moein Kafi, Hamed Nikravesh, Maryam Aghel, Erfan Mohammadian, Yousef Kazemzadeh, Reza Azin

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Improved Quantum Artificial Bee Colony Algorithm-Optimized Artificial Intelligence Models for Suspended Sediment Load Predicting by Peng Wei, Wang Yu

“…The results indicate that the BDQABC-SVR and BDQABC-ANN models exhibit stronger predictive capabilities for SSL than other models. …”
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Predicting Remaining Useful Life of Lithium-Ion Batteries for Electric Vehicles Using Machine Learning Regression Models by Sravanthi C L, Dr.J N Chandra sekhar

“…Findings show that Bagging Regressor model outperforms the other three models in terms of RUL prediction. …”
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Artificial intelligence models for predicting the performance of proton exchange membrane water electrolyzers under steady and dynamic power by Thomas Waite, Alireza Sadeghi, Mohammad Yazdani-Asrami

“…The duration studied, the number of input features, and the variety of PEMWE constructions considered make this the most comprehensive AI model on the subject, to date. The model offered a goodness of fit or coefficient of determination ( R ^2 ) value of 0.9991 for the testing data and can predict the performance of untrained electrolyzers and their operation with similar accuracy.…”
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Application of Seq2Seq models for predicting the development of thunderstorm activity to enhance the pilot’s situational awareness in flight by G. V. Kovalenko, I. A. Yadrov

“…Future research is expected to further optimize the model architecture and integrate the predictive technology into flight crew decision support systems.…”
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Predicting habitat suitability for an endangered medicinal plant, Saussurea medusa: insights from ensemble species distribution models by Xiang Guo, Wei Bai, Yihua Wang, Senliang Hao, Liping Zhao, Xin Li, Zongliang Guo, Xiaoyan Li, Xiaoyan Li

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Assessing the effectiveness of fuzzy logic-based models for predicting sports event outcomes: A CRITIC-VIKOR approach. by Taibo Liu

“…Incorporating fuzzy logic-based models into sports prediction has generated significant interest due to the intricate nature of athletic events and the many factors influencing their outcomes. …”
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Four-parameter beta mixed models with survey and sentinel 2A satellite data for predicting paddy productivity by Dian Kusumaningrum, Hari Wijayanto, Anang Kurnia, Khairil Anwar Notodiputro, Muhlis Ardiansyah, Islam MD Parvez

“…Consequently, these models yield less accurate and potentially misleading predictions. …”
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Evaluating Machine Learning Models for Predicting Late Leprosy Diagnosis by Physical Disability Grade in Brazil (2018–2022) by Lucia Rolim Santana de Freitas, José Antônio Oliveira de Freitas, Gerson Oliveira Penna, Elisabeth Carmen Duarte

“…This study evaluates machine learning models to predict factors associated with late leprosy diagnosis—defined as grade 2 physical disability (G2D)—in Brazil from 2018 to 2022. …”
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A Study Investigating Interpretable Deep Learning Models for Predicting Mortality and Survival in Patients with Primary Thyroid Lymphomas by Zihan Yu, Rong Hu, Jiaqing Chen

“…Primary thyroid lymphoma (PTL) is a rare malignancy, and this study aimed to develop a prognostic prediction model for PTL using deep learning algorithms while providing interpretable analyses. …”
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Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. by Montaser Abdelsattar, Mohamed A Ismeil, Karim Menoufi, Ahmed AbdelMoety, Ahmed Emad-Eldeen

“…This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. …”
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Machine learning models for predicting morphological traits and optimizing genotype and planting date in roselle (Hibiscus Sabdariffa L.) by Fazilat Fakhrzad, Warqaa Muhammed ShariffAl-Sheikh, Mohammed M. Mohammed, Heidar Meftahizadeh

“…In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. …”
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A Comparative Analysis of Machine Learning Models for Predicting EFL Student Language Performance in Smart Learning Environments by Banchakarn Sameephet, Wirapong Chansanam, Mahboubeh Rakhshandehroo, Chawin Srisawat, Kittichai Nilubol, Arnon Jannok, Bhirawit Satthamnuwong, Kornwipa Poonpon

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Comparison of LSTM and Transformer Models in Predicting NVIDIA Stock Closing Prices and the Application of Rule-based Trading Strategies by Muhammad Irfan Abdul Gani, Putry Wahyu Setyaningsih

“…A hybrid model demonstrated improved prediction accuracy with an RMSE of 3.5643 (training) and 8.6727 (testing), although it still did not outperform LSTM. …”
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Deep learning models using intracranial and scalp EEG for predicting sedation level during emergence from anaesthesia by Lichy Han, David A. Purger, Sarah L. Eagleman, Casey H. Halpern, Vivek Buch, Samantha M. Gaston, Babak Razavi, Kimford Meador, David R. Drover

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Comparison of the performance of random forest and decision tree models in predicting the risk of poor prognosis in diabetic foot patients by PENG Ying, ZHENG Hailan, QI Mingxia, JIANG Lan

“…Objective To analyze the performance of random forest and decision tree models in predicting the risk of poor prognosis in diabetic foot patients, and to select the optimal risk prediction model for prognosis assessment in diabetic foot patients. …”
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Enhanced machine learning models for predicting three-year mortality in Non-STEMI patients aged 75 and above by Jing Zhang, Wuyu Xiong, Chengzhi Zhang, Cuiyuan Huang, Wenqiang Li, Li Liu, Wei Wang, Ye Sang, Huiling Zhen, Caiwei Tan, Jiajuan Yang, Jian Yang

“…The Random Forest (RF) model was employed for prediction, which yielded the best performance with an area under the curve (AUC) of 0.92. …”
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